QUALITY OF ADVICE Conceptual Framework

Abstract

Whilst many have quoted the need to improve the quality of advice provided by financial planners, few

including regulatory authorities have sort out to define what quality advice encompasses and indeed

take the step further to outline a way to measure and assess against that definition. Perhaps due to the

difficulties in dealing with the multitude of scenarios and possibilities, we may concur that quality advice

cannot be defined – what may appear to be quality in one case may not hold true in another. However it

is the position of this paper that defining quality – even if only in the abstract – provides a valuable tool

and is an important first step in improving the standard of advice.

This paper aims to provide a conceptual framework to define and measure the quality of advice. Having

said that however, it is not the aim to outline the perfect and ideal model, but rather to broaden the

approach currently taken to measuring and monitoring quality, as well as addressing areas unexplored

by most literature currently available. It aims to provide a point of reference which can be extended

upon using real world data and trials.

The chapters of this paper will build upon the previous in attempt to address the challenges relating to

advice quality. It is split into two parts, the first part dedicated to the purpose of defining quality and

what it is to mean, while the second part attempts to outline a practical mechanism for measuring and

monitoring against that definition. Whilst all effort is given to outline the most pragmatic model

possible, as stated above, it is not the aim to present a model suitable for direct implementation but

rather outline the principles which can be extended and transposed with modification. As such, minimal

consideration will be given to factors such as cost, resource, time and commitment, which will need to

be considered in practice.

QUALITY OF ADVICE Conceptual Framework

Geoffrey Lew

February 2010

“Even though quality cannot be defined, you know what quality is” sd – Robert M. Pirsig 1928

CONTENTS

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Table of Contents

Part I – Defining quality

1.1 Context .............................................................................................................................................. 7

1.2 The problem ...................................................................................................................................... 8

2.1 The approach .................................................................................................................................... 9

2.2 Mental models ................................................................................................................................ 10

2.3 Complex problem solving................................................................................................................ 12

3.1 Components of value ...................................................................................................................... 14

3.2 Compliance and quality................................................................................................................... 15

4.1 Quality in the absolute .................................................................................................................... 18

4.2 Quality defined ................................................................................................................................ 18

4.3 Components of quality .................................................................................................................... 19

5.1 Uncovering the systemic structure ................................................................................................. 23

5.2 Introduction to causal loop diagrams ............................................................................................. 24

5.3 Feedback diagrams ......................................................................................................................... 25

5.4 Education view ................................................................................................................................ 26

5.5 Compliance view ............................................................................................................................. 28

5.6 Recruitment view ............................................................................................................................ 30

5.7 Service view .................................................................................................................................... 32

5.8 Factors in quality ............................................................................................................................. 34

Part II – A model for quality

6.1 Commander’s intent ....................................................................................................................... 36

6.2 Design and planning ........................................................................................................................ 38

6.3 Design for quality ............................................................................................................................ 40

6.4 Logical lines of operations .............................................................................................................. 41

7.1 Learning and adapting .................................................................................................................... 43

7.2 Concepts of measurement .............................................................................................................. 44

7.3 Current monitoring approach ......................................................................................................... 44

7.4 Measuring and assessing strategy and risk quality ......................................................................... 46

7.5 Measuring and assessing product quality ....................................................................................... 47

CONTENTS

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7.6 Holistic quality ................................................................................................................................. 51

8.1 Quantitative methods ..................................................................................................................... 52

8.2 General application ......................................................................................................................... 53

8.3 Further investigations ..................................................................................................................... 54

9.1 Quality mindset ............................................................................................................................... 56

9.2 Conflicts of interest ......................................................................................................................... 56

9.3 A social science ............................................................................................................................... 57

9.4 Resistance to change ...................................................................................................................... 57

9.5 Future direction .............................................................................................................................. 57

Conclusion ................................................................................................................................................... 59

Bibliography ................................................................................................................................................ 60

PART ONE Defining quality

INTRODUCTION

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Introduction

1.1 Context For the purposes of this paper, we consider the term ‘financial planner’ to capture authorised representatives of entities holding an Australian Financial Services License (AFSL) that provides personalised advice to retail clients. The role of the financial planner is deemed multi-faceted, not only charged with bringing investment advice to the client in a way they understand, but also with assisting them in achieving their broader financial and lifestyle goals. Whilst different Licensees may stipulate varying advice processes, in essence it involves three main stages:

i. Information gathering: Making reasonable inquiries in relation to the client’s personal circumstances and needs;

ii. Strategy: The devising of a comprehensive strategy based on the information gathered in the first stage; and

iii. Delivery: Provision of the strategy to the client in the form of a written Statement of Advice. The dimensions of the second strategy stage forms the basis for the bulk of the discussion in this paper and can be seen as the core value add of a financial planner. It requires that financial planners have sufficient expertise and technical understanding of the broad spectrum of areas that may be applicable, including (Kaplan Education, 2009):

i. Aged care & special residences: Specific needs related to the elderly or the disabled (e.g. nursing);

ii. Debt and gearing: Leveraging strategies used to magnify potential investment or income gains; iii. Estate planning: Future succession planning and disbursement of estate assets after death; iv. Income streams: Regular income payments for those at or near retirement; v. Insurance: Life, disability and other general insurance needs;

vi. Investments: Financial investment in sectors according to risk and return benefits; vii. Self managed superannuation funds (SMSF): Legislation and compliance requirements relating

to self managed superannuation funds; viii. Social security: Eligibility and impacts of government benefits (including pension benefits);

ix. Superannuation: Savings and investment towards retirement; x. Taxation: Impacts of taxation on aspects such as investments, social security and pension

receipts. Most importantly, it is expected that the financial planner is able to recommend strategies which imbue the areas above to the circumstances of the client and be able to do so in a manner which is comparatively superior to the strategies taken if the client had acted alone in the absence of their advice. Without this aspect, there would be no need and role for financial planners and the industry.

INTRODUCTION

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1.2 The problem “Personal advice by its nature, is likely to be relied on by retail clients who may suffer significant loss if the advice is not of sufficient quality. For this reason, the law imposes a specific obligation on providing entities to give due consideration of the client’s circumstances and the subject matter of the advice”. Referred to as the ‘suitability rule’ in ASIC Regulatory Guide 175, the approach taken under the current regulatory framework is that advice provided needs to be demonstrative that it has some reasonable grounds for its basis, as derived from the circumstances of the client. Despite these good intentions, in essence, the approach adopted is one of process and activity rather than one focused on the quality of outcomes. Given the current regulatory framework, few licensees have sought out to define and measure the quality of advice provided by their representatives as regulations do not require that the advice provided be ‘ideal, perfect or best’. Whilst they have been active to ensure that a reasonable basis exists for the advice provided, few aim to ensure that the strategies presented are actually of sufficient comprehensiveness and benefit to the client. The framework falls short of imposing the requirement to both monitor and measure actual quality, but instead operates under the assumption that the processes and policies in place regarding training, recruitment and other requirements as stipulated by the Corporations Act have a strong correlation to and are effective in driving the quality of advice provided by Licensee representatives. However, by failing to measure and validate this assumption, one does not actually have the basis for determining whether quality is achieved or that the quality of advice is progressively improved (Hubbard, 2007). The backdrop of the ‘global financial crisis’, has heightened the need towards improving the quality of advice provided by financial planners and has also highlighted deficiencies (albeit in hindsight) with previously acceptable approaches such as the ‘one size fits all’ approach. It is acknowledged that some may argue, given the fact that relatively few financial planning practices have failed throughout the whole ordeal of the global financial crisis; there is in a sense some negative assurance over the effectiveness of the current framework. However, we argue that quality should not be thought of as the ability to avoid the extremes of failure or collapse, but instead should be viewed relative to the outcomes that would have been achieved if the client had not chosen to engage in the advice of the financial planner in the first instance. As we have not made the attempt to perform such a comparison, we are unable to draw such conclusions.

CONCEPTUAL APPROACH

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Conceptual approach

2.1 The approach The following will outline the thought methodology applied in driving the concepts and framework presented in this paper and will provide some background on the approach. A system may be defined as “a group of independent but interrelated elements comprising a unified whole” (Miller, 2009). Conceptually, it is proposed then, that the framework for the provision of advice may also constitute as an abstract complex system comprising of the processes and operations involved in the provision of advice. The elements or components of the system are thus regarded as the individual processes and policies, such as practices on training and information gathering which combine to form the system as a whole. Advice as a system will form the principle construct for the rest of the paper. The traditional approach taken to analysing systems was to reduce a system into its smaller components and observe their nature and behaviours separately. By doing so, one was able to develop succinct objectives, tests and measurements in an attempt to predict the behaviour of those discrete components. The overarching principle was that a system was simply a sum of its components and by mapping their behaviour, one was also able to predict the behaviour of the entire system as a whole. Often thought of as the reductionism principle, a heavy emphasis is placed on the causal relationships and behaviours of the system’s components influencing the unified whole in a similar fashion. The reductionism principle will be applied heavily throughout this paper, to assist in understanding how the quality of advice is driven by its elements and components. The limitation to reductionism in complex systems however, is that often the whole is more than the sum of its parts. Reductionism ignores the interactions of its components which may prevent the properties of the unified whole being attributed to any one element (otherwise known as emergence). Emergence originates in the interactions between the parts, whilst reductionism by its very application and nature obscures these interactions (Hitchins, 2004). “The necessary overview and understanding of how [the] components effectively perform together is frequently overlooked” (Blanchard & Fabrycky, 2006). It is crucial that any framework that we apply to achieve quality advice does not ignore “the dynamic forces, interactions [and feedback] that may cause emergent properties [to] manifest themselves as side effects of decisions and policies” (Fowler, 2009). A framework which ignores the interactions of its components are in effect ‘blind’ and static, causing us to falsely over-amplify our understanding of the system and engage in decisions which can lead to false and deceptive results. Therefore, not only is there the need to apply a ‘bottom up approach’ which observes the behaviours of the system’s components individually, but also one which views the behaviours of the system as a whole.

CONCEPTUAL APPROACH

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2.2 Mental models To further explore the relevance and importance of understanding the system as a whole, the following explains the idea of mental models relating to systems as developed by Maani & Cavanna (2000). Graphically depicted below, the hierarchy of the structure illustrates the four different levels of understanding systems.

EVENTS

PATTERNS

MENTAL MODELS

SYSTEMIC STRUCTURE

Maani & Cavanna (2000) Events depict the outcomes or symptoms of the world and whilst events are the most visible and pronounced, they have limited use in furthering our understanding of the drivers of a system (e.g. announcements made on the stock market are events, however they alone do provide any insights into the interactions of the stock market) (Fowler, 2009). Patterns extend the value of events by adding the element of time. In doing so, one hopes to establish trends and patterns to help explain the observations or predict likely results for the future (e.g. tracking announcements made over a period of time will assist us in predicting the likely stock market reaction should the event reoccur).

CONCEPTUAL APPROACH

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Whilst it is common for most to see the value of trends, one finds much greater difficulty in deciphering the interaction of trends and how they may affect each other (Maani & Cavana, 2000). Systemic structures are the set of information interconnections that generate patterns of behaviour – we may view this as a complex mesh linking all possible trends and events that one is able to observe. This level of understanding is particularly valuable, as unlike patterns and trends, understanding the interconnections allow us to precisely predict the events that would occur given a particular input. For example, the knowledge of the relationship that an announcement made on the stock market simultaneously influences the share price of the company and that of a competitor, is extremely valuable but difficult to prove through the pure observation of patterns alone given the number of possible inputs and dependencies (economists spend a great deal of time attempting to uncover and verify these underlying interrelations).

SYSTEMIC STRUCTURE

Graphical representation of a systemic structure and its many path ways.

EVENT 1

EVENT 2

EVENT 3INPUT

The final layer “represents the mental models of individuals and organisations that influence why things do or do not work” (Maani & Cavana, 2000). The final layer adds the complexity of human values and beliefs to the interactions above, which in combination ultimately drive the events and patterns that are observed.

EVENTS

PATTERNS

MENTAL MODELS

SYSTEMIC STRUCTURE

Modified depiction of mental models - Maani & Cavanna (2000) In some cases, the ability to grasp and apply the third and fourth layers of the mental models is quite easy and accepted. For example, when asked why an apple falls from a tree, few would attribute the

CONCEPTUAL APPROACH

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cause of the event to be the tree branch giving way or due to factors of the wind (both also events). Instead, it is accepted that the underlying cause is of course due to the effects of gravity (a systemic structure). Conversely, in dealing with complex systems it is often the case that one reverts to thinking only within the bounds of events and patterns. For example take the two separate events, a fall in a company’s share price and an announcement of a negative earnings result. Due to the complexity inherent within the stock market, one is often satisfied to simply link the two events together and faithfully follow the logic that one must have caused the other – that it was because of the negative earnings result that that company’s share price fell. However when viewed in light of the presented model and how we should view systems, we know that this is unlikely to be the case and to believe so overwhelmingly ignores the emergence properties of the system. Fortunately few systems are as complex as the interactions seen in the stock market, however it does highlight the need to consider the interactions of systems as a whole and not just the components in isolation. For example, if applied to the scope of financial planning, the failure of the financial planner to consider the tax consequences of selling a particular investment may not be caused solely as a result of technical incompetency. Thus, despite the difficulties in uncovering and understanding the interconnections of the third and fourth layers, there is nevertheless much value in attempting to do so. One should be appreciative of the complex dynamics of the system and not be content with applying relationships according solely to the observable behaviours of events and patterns. The proper methodology to be applied is therefore, one of both reductionism and emergence. Whilst principles of reductionism are easier to grasp and apply, there retains the need to understand and consider the properties of the complex dynamics of the system as a whole.

2.3 Complex problem solving For the purposes of discussion it may also be helpful to draw in brief, similarities between the features of complex problems and those also found in complex systems. Complex problem solving as it is so called, is a mental process which involves seeking out and discovering how a particular organism or system, can be made to move from a given state to a desired goal state. In the early phases of complex problem solving research, five particular qualities were common and core to all frameworks developed in describing their nature and scenarios. These attributes as we will later see, are also features of complex systems. These typical features were (Funke, 2001):

i. Complexity: The number of variables in a system; ii. Connectivity: The dependencies between variables; iii. Dynamics: An inherent characteristic which makes the system clearly distinct from a static

state. The processes and structural relationships of the (dynamic) system may develop or change over time to varying outcomes. Any decisions or methods of measurement that are applied must also incorporate how the system develops or changes over time and what the short- and long-term effects of specific interventions are;

CONCEPTUAL APPROACH

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iv. Opaqueness: Recognition that there may be limitations to the amount of feedback or information flowing out of a system;

v. Polytely: Recognition that a problem may concurrently have multiple and conflicting goals that may need to be achieved before the end state can be reached.

These features in addition to the mental models explored prior will play important roles in shaping the effectiveness of our model and in guiding the approach that we should take in manipulating outcomes.

VALUE OF ADVICE

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Value of advice

3.1 Components of value Before we attempt to define quality and investigate how it is to be measured, we must firstly understand the importance of advice, and where its value is derived from. Ultimately, any pursuit of quality in advice must also result in a corresponding increase in its value, as without such a link, the definition of quality would neither be worth knowing or worth measuring. Unlike quality, what constitutes as the value of advice can be obtained by simply answering the question “Why does one obtain advice from a financial planner?” Using a crude example, if posed from the perspective of a patient seeing a doctor, the answer may simply be that it is because the diagnostic proposed by the doctor is much more likely to be correct, complete and beneficial than if we were to undertake our own diagnosis. Similarly, the value of advice from a financial planner can be seen to be derived from the belief that the advice provided from the planner is also more likely to be correct, complete and beneficial (albeit financially not medically). This belief may stem from a number of reasons including the client’s:

i. Lack of experience and technical knowledge in the relevant areas; ii. Lack of commitment and patience to personally undertake the appropriate research;

iii. Under confidence in their own ability and knowledge; iv. Perception of the industry and industry professionals.

Despite the beliefs held, past circumstances have shown quite clearly that it is at the very least possible that the advice obtained in reality may neither be correct nor beneficial to the client. Financial advice obtained may have in fact, left clients worse off than if they had acted without advice at all. When viewed from this perspective, one may wonder why clients decided to engage in these activities at all – why would people pay money for advice that leaves them in a worse off position. The answer to this is given by ASIC in their Survey on the quality of financial planning advice (2003), where it stated that the “information suggested that [clients] judged ‘quality’ on a limited range of factors. Most commented on issues like the planner’s demeanour and office, the prompt time to produce a plan and how accurately the client’s details were recorded. Many customers had not detected where their plan had serious gaps or shortcomings”. In this respect, we are able to see that the value of advice not only stems from the legitimacy and worth of the advice given, but also from its perceived value.

Value of advice

Perceived value Actual value

VALUE OF ADVICE

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This is an important distinction, as the drivers of the two areas of value are potentially vastly different. Where aspects such as courtesy, credibility and communication may increase the worth of perceived value, these drivers have no affect on the actual value of the underlying advice. Additionally, whether due to the lack of understanding or inexperience, clients may be blinded entirely by the notion of perceived value and have no bearing on the actual value of the underlying advice they are receiving. Quality advice must therefore be positioned under the banner of actual value. The drivers of quality advice must be synonymous with the drivers of actual value, as unlike perceived value, actual value must stand true from all angles. Correspondingly, for the purposes of this paper, we have ignored the development of perceived value but focused entirely on actual value or quality advice which we now use interchangeably.

Value of advice

Perceived value Actual value

Customer satisfaction Quality advice

3.2 Compliance and quality It cannot be understated that the financial planning industry has made great strides over the past decades to enhance the diligence and processes required to provide something as simple as a recommendation intended to influence a person making a decision in relation to a particular financial product. Section 911A of the Corporations Act only allows financial services businesses (and their representatives) to provide financial advice under an AFSL so that a number of core obligations and conditions may be upheld. These conditions and obligations, as governed by ASIC, may be split broadly into two categories, namely (Parliamentary Joint Committe on Corporations and Financial Services, 2009):

i. Conduct obligations: Rules designed to ensure industry participants behave with honesty, fairness, integrity and competence;

ii. Disclosure obligations: Rules designed to overcome information asymmetry between industry participants and investors by requiring disclosure of information required to make informed decisions by investors.

In retrospect, it may be seen that the increase in regulation has definitely ‘raised the bar’ or driven a strong push for the improvement in the quality of advice provided by financial planners – for example, a basis for the advice was not even required until recent times. However, it should be highlighted that

VALUE OF ADVICE

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compliant advice and quality advice is neither one nor one in the same – compliant advice should instead be viewed as a closed subset of quality.

Quality

Compliance

As outlined in chapter 1.2 The problem, the current regulatory framework fails to impose any obligations or conditions which measure the actual outcomes or outputs of the advice provided. “Conduct and disclosure regulation does not involve any guarantee that regulated products and institutions will not fail and that promises made to retail investors will be met. Under a conduct and disclosure regime retail investors are still subject to [such] risks” (Parliamentary Joint Committe on Corporations and Financial Services, 2009). Gaps with compliant advice are easily exposed even with the application of the crude definition of quality given in the doctor example above. Clients obtain advice, from the belief that the advice provided is more complete, correct and beneficial to them in their circumstance, relative their ability to advise themselves. However, when advice is assessed under the compliance spotlight, none of these aspects are actually reviewed and assessed. For example:

i. Completeness: Did the advice cover all the necessary areas including tax and social security implications?

ii. Correct: Was the advice provided actually correct? Was liquidity risk associated with a financial product correctly understood by the financial planner and ultimately the client?

iii. Beneficial: Did the advice actually leave the client in a better position than they were before? Instead, compliant advice is achieved by merely demonstrating that a piece of the client’s circumstance was used in providing a particular recommendation or strategy. In this respect, compliant advice may be viewed as an enclosed subset of quality and whilst effort has been made to increase its bounds, relative to actual quality, it falls substantially short. Having made the distinction clear however, that is not to say that regulation and compliance is not required and is of no value. Regulation of advice as with regulation in other fields should be viewed as a way of expressing the minimum standards. For example, Australian Design Rules (ADR) for motor vehicles requires that certain minimum standards are met by the car at time of manufacture. However this is not to say that these standards cannot be exceeded or surpassed by a car manufacturer. Indeed, this is quite often the case, as evidenced by cars of varying safety ratings and performance. It is curious to note that the same cannot be said for financial services, whereby (perhaps due to greater difficulties) most Licensees have taken the route that compliance is instead the first and final goal of operations. This obviously places an interesting conundrum on the future of financial planning, as for an

VALUE OF ADVICE

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industry to be sustainable in the long run; it neither requires the elements of compliant advice or perceived value, but quality – actual value. By having compliance as the end and final goal, Licensees have intentionally stripped out and removed much opportunity for the progressive improvement towards quality, as compliance merely forms a subset of quality advice. Licensees have limited their ability to measure and observe aspects of quality and improvement which may be incorporated as feedback to their processes and operations. Licensees must recognise the need to broaden their approach and the tools used to address deficiencies in advice – “if your only tool is a hammer, then every problem is a nail”.

DIMENSIONS OF QUALITY

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Dimensions of quality

4.1 Quality in the absolute Licensees have typically shunned away from defining the word ‘quality’ in quality advice due to their difficulty in grasping its concept and meaning. A significant factor to this is the fear that by defining it incorrectly, imprecisely or too narrowly, there will be some (unknown) detrimental downstream impact. This is understandable; as conventional thinking dictates that a problem not well stated or defined cannot be measured or solved. Where stated key performance indicators and goals drive pay and bonuses for the majority of employees, any hint of ambiguity makes one uncomfortable and fear the loss in control. However contrary to these fears (or delusions), it can be argued that an abstract definition is sufficient for application and measurement. Whilst the conventional (‘hard’) approach focuses on single explicit objectives and solutions, the presented (‘soft’) approach “looks at much broader problems where the definition of the actual problem is often unknown”. We are instead “interested in uncovering trends in system behaviour which can be used to implement better policies and processes ... [We] seek to ... [facilitate] a systemic process of learning in which different viewpoints are examined and discussed in a manner that can lead to purposeful action in pursuit of improvement” (Fowler, 2009). In this regard, the obstacles with defining quality with absolute precision are removed. To illustrate this, consider the following example. If Emily aged 11 was asked to determine the quality of two glasses of milk, one edible and the other past due, she merely has to take a sip. By instinct, regardless of definition, she will be adamant that it is definitely not the glass that tastes astray. Similarly, this concept can be applied to the quality of advice, as regardless of your definition, if we were able to observe just one related (albeit not exact) measurement of an element of quality (i.e. the milk is at least edible), we have made progress. Many instead “dwell ad infinitum on the overwhelming uncertainties. Instead of making any attempt at measurement, they prefer to be stunned into inactivity by the apparent difficulty in dealing with these uncertainties “ (Hubbard, 2007).

4.2 Quality defined Given our discussion up to this point, we may simply define quality advice as:

“Advice which achieves the financial goals of the client” As explained in the following chapters, we will see how it is not this definition which matters most, but rather the elements and components associated with the definition which plays a much greater and important role in improving the quality of advice. To define quality in any greater specificity detracts from the need to adapt and flex to changes in the conditions of the client and that of the greater

DIMENSIONS OF QUALITY

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environment. Having said that however, this is not to say that there are no observable and measurable aspects, it is merely recognition of the dynamics of quality.

4.3 Components of quality Having defined quality at least in the abstract, we may now turn to investigating the practicalities of quality and how it may be examined and measured. Part II of this paper explores the concept of measurement and how measurement may be applied, however for the purposes of this chapter we will continue to breakdown advice and quality into its elements, which will play an important role in measurement later. Chapter 1.1 Context, briefly outlined the role of financial planners in respect to advice. In essence, it involved the development of a financial strategy which is appropriate to the circumstances and needs of the client. In this regard we may view quality advice (referred to henceforth as ‘advice’ for simplicity) to be a function of the quality of strategy, product and risk.

ff ((Quality) = Strategy + Product + Risk Strategy may be defined as “a plan of action to achieve a particular goal” (Oxford, 1989). Applied to financial planning, it is the plan of action recommended by the financial planner to achieve the stated goals of the client. Arguably the most complicated of the three components, it requires robust knowledge of not only the rules and regulations relating to the areas outlined in chapter 1.1 but also how these areas may be applied specifically to the circumstances or initial conditions of the client. For example, if the client had a large mortgage with two children, would it be appropriate for the client to enter into a margin lending arrangement in order to access tax deductions? Would the strategy change if the client had a steady stream of income? What if they also had large equity investments? Similar to economic ‘Game Theory’, given an opening set of conditions, the financial planner must decide which combinations of actions (i.e. the strategy) will result in the best payoff for the client. Deficiencies in either the application of technical competence or the factual knowledge of the client’s situation will impact upon the comprehensiveness of the strategy causing suboptimal results or perhaps in extremes, even disastrous failure.

DIMENSIONS OF QUALITY

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Graphically depicted below (each dot representing a possible choice or action), it should be noted that due to imperfect information it becomes increasingly difficult for one to predict the end state – decisions require constant reassessment throughout time. This is seemingly indicative (at least in principle) of the need for financial planners to review client circumstances as frequently as environmental conditions change.

Conditions

MissMiss Similar outcomeSimilar outcome Stated goal

Product quality refers to the quality and suitability of the financial products recommended by the financial planner to the client. Concerns were raised in the Parliamentary Joint Committee on Corporations and Financial Services’, Inquiry into financial products and services in Australia, where it questioned “whether poor investment outcomes [were] due to a failure of financial advice, or [instead] the products in which clients invest”. Specifically, it notes the view that “a financial planner is there to provide advice … based on what they understand around that product at the time … but there are instances where the product promise has not been delivered … The financial planner was not privy to what was going on and the company itself ended up collapsing and taking everything with it”. From this perspective, it may be seen that the planner may be deemed removed from any responsibility relating to product quality as it is in effect out of their control, so long as reasonable steps were taken to understand the product initially. However the report later offers a converse view which states, “most agreed that the crux of the problem is the advice that accompanies a decision to invest in certain products. The consequences of product failure will be greatly mitigated if the investment is only one part of a diversified portfolio that matches the client’s tolerance for risk”. Taking these views into account, we may infer that despite the issues surrounding poorly designed and performing investment products, product quality in relation to the role of the financial planner is twofold:

i. Initial investment decision: The decision to invest in particular financial products and the strategy used in constructing the portfolio (e.g. diversification);

ii. Cessation decision: The decision to retain or exit particular financial products given the financial planner’s knowledge of a product and its history of events (e.g. past performance).

These two aspects are key drivers in the financial performance of the client’s overall portfolio and is a central skill or value add that a financial planner provides to the client, who is not expected to have such expertise.

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The final element of quality relates to risk and risk management. Risk is a gamble or chance of financial loss (Miller, 2009) and underpinning any strategy or recommendation, should be consideration of the client’s tolerance to risk. Risk can be seen to permeate through both product and strategy quality, and should be considered comprehensively when providing financial advice. According to Modern Portfolio Theory (Markowitz, 1952), an investor will take on increased risk only if compensated by a higher level of expected return. Conversely, an investor who wants a higher return must also accept more risk. This is an important concept, as despite the desire of all clients to generate the maximum potential investment return, financial planners in providing advice must determine (through the information gathering process) the risk tolerance of the client, which may in turn limit the available investment return on a portfolio. Referring back to our definition of quality, a client losing a significant proportion of their capital at the wrong time of their life (e.g. at age 64 near retirement) will not be able to achieve their financial goals given their remaining timeframe.

Risk

Expe

cted

retu

rn

Portfolio return

Modern Portfolio Theory Similarly, this concept is also important to any strategies employed such as gearing, as the magnification of investment losses and the impacts of debt may hinder the client’s ability to achieve their financial goals within the expected timeframe. Risk management is about being smart about taking chances, always with the client’s end goal in mind.

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These three components drive the overall quality of advice provided, and the ability of the financial planner to perfect areas relating to all three components will determine the value of advice. Having examined the three components of quality, we may wish to depict quality in an alternate manner, having the components of quality as subsets of each other.

Risk quality

Product quality

Strategy quality

FACTORS IN QUALITY

Factors in quality

5.1 Uncovering the systemic structure Chapter 2.1 Conceptual approach, explained how quality advice can be thought of as a complex system – a system that we need to understand not only at the level of events and patterns, but also at the level of its systemic structure. Difficulties arise however, as in a complex system, the inputs or contributing factors may not directly influence the components of quality as one would like, but rather influence it indirectly through the interconnections of a systemic structure (which much like the interactions of trends can be difficult to observe directly). Diagrammatically represented below, by analysing how inputs flow through the interconnections of the systemic structure and influence the components of quality, we may then begin to predict the impacts and consequences of our actions, policies and decisions.

Some may argue that due to the number of contributing factors, uncertainties and variables affecting quality, any modelling attempt would be futile and inaccurate. Whilst this may be true to an extent, this does not mean that a high level approach to modelling is invalid or useless. Similarly, “we couldn’t possibly hope to predict the behaviour of a single molecule in a gas, but we can [however] use the laws of thermodynamics to predict the overall behaviour of the gas” (Fowler, 2009).

Quality

Strategy

RiskInherent systemic structure

Contributing factors

Product

FACTORS IN QUALITY

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5.2 Introduction to causal loop diagrams The first stage in uncovering the interconnections of the systemic structure is through the development of Causal Loop Diagrams. “Causal Loop Diagrams show the dynamic interactions between different variables in a system by displaying the causal links and feedback loops that explain the fundamental structure of the system” (Fowler, 2009). In essence, by constructing Causal Loop Diagrams, we are attempting to capture and explicitly highlight how one variable or input influences the other, which ultimately combine to govern the behaviours of the system as a whole. “For now, it is more important to understand the basic structure and causal relationships that make up the system, rather than trying to predict the exact response or behaviour of the system” (Fowler, 2009). The following diagram illustrates how Causal Loop Diagrams operate and how they are to be interpreted.

Birth rate Population Death rate

Fractional birth rate

Average lifetime

+ -

++

+-

Cases of cancer

-

-

A positive link means that if the variable or input increases, there will be a positive effect on the second variable, whilst the converse is also true; that is, a negative link means that if the cause increases, there will instead be a negative effect on the second variable. In this example, an increase in the birth rate will have a positive effect on the population, whilst conversely an increase in the average lifetime will have a negative effect on the death rate (as there will be fewer deaths due to increased longevity). It is interesting to note the complexity of such a seemingly simple system, as whilst an increase in the average lifetime results in a direct decrease in the death rate, this is not necessarily true for the effects of birth rate on the population. As noted by the two feedback loops flowing into population, whether or not the population increases due to direct increases in the birth rate will depend on how much is offset by the negative effect of increases in the death rate (Fowler, 2009). In this regard, this Causal Loop Diagram has shown how population is influenced by changes in the variables of births and deaths.

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5.3 Feedback diagrams Purely from the example above, it is not difficult to see how we may begin to discover common variables and themes. For example, if we were to expand the factors affecting death rate with additional variables such as disease, the three variables may be linked together to form a correlated ‘loop’.

Birth rate Population Death rate

Fractional birth rate

Average lifetime

+ -

++

+-

Cases of cancer

-

-

The following section will present a series of feedback diagrams created by this paper, which are designed to outline the key contributing factors to quality. As a variation or expansion of Causal Loop Diagrams, these feedback diagrams allow the exploration of the variables in quality and more importantly, the identification of the key themes that may begin to unravel the independencies of the systemic structure. This will be useful later in our attempts to rationalise the effects of certain variables or inputs on the holistic system of quality.

FACTORS IN QUALITY

This paper has identified four critical (macro) themes inherent within the systemic structure which influences quality. We term these critical themes, ‘views’ which will be analysed and explored in greater detail.

5.4 Education view The first view we will investigate is the view of education and training. Education and training aims to “protect consumers of financial advice by ensuring that those who provide the advice are competent to do so. Retail clients generally do not have the resources of expertise to assess whether their adviser has an appropriate level of competence to provide financial advice” (ASIC, 2009). ASIC Regulatory Guide 146 Licensing: Training of financial product advisers, makes the following comments of the knowledge and skill requirements:

i. Knowledge requirements: All advisers should demonstrate that they have met the generic knowledge requirements and specialist knowledge requirements relevant to their activities;

ii. Skill requirements: Advisers providing personal financial advice to retail clients should be able to apply appropriate skills in relation to their activities and the products and markets in which they operate.

Therefore, the key premise around education is that financial planners have the Licensee’s desired level of knowledge and skill. The education view presents those factors which contribute to the effectiveness of the Licensee’s ability to influence the knowledge and skill of their authorised representatives through education.

Quality advice

Education view

Compliance view

Recruitment view

Service view

FACTORS IN QUALITY

27

Other commitments

Number of tasks

Work remaining

Time availableWork completed

Time spent studying

Remaining content

Knowledge content

Motivation

Incentive

Cost of education

Knowledge retention

Previous experience

Opportunity for experience

Content difficulty

Education requirements

Desired level of knowledge

Performance feedback

Opportunity for discussion

Peer support

Previous education

Resistance to education

Effectiveness of delivery

Efficiency loop

Time loop

Knowledge loop

Feedback loop

Experience loop

Work loop

+

++

+

+

+

+

+

+ +

+

+

+

+ +

+

-

-

-

-

+

-

-

-

+

-

-

-

-

Education view Through highlighting the feedback loops in the diagram, we are able to discern that the key themes in the effectiveness of education are:

i. Work: The amount of work or outside influences impacting on the time available for study; ii. Time: The amount of time actually spent in gaining knowledge of study;

iii. Efficiency: The efficiency and effectiveness of the time spent at study; iv. Knowledge: The difficulty and level of retention of the content; v. Feedback: The amount of performance feedback and response;

vi. Experience: Previous experiences either practical or through previous education and training. From this analysis, we are able to see that if the Licensee wished to influence the effectiveness of training and education, focus should be spent on the above feedback areas. Although we have not quantified the exact influence of the areas on education (and ultimately quality) at this stage, we have begun to unearth the relationships between the contributing variables, defined particular reference points and have finetuned our focus.

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5.5 Compliance view The second view presented below is the view of compliance. ASIC Regulatory Guide 104 Licensing: Meeting the general obligations, imposes the obligation on Licensees to “do all things necessary to ensure [their] financial services are provided efficiently, honestly and fairly [and] comply with the financial services laws”. As such, the compliance view may be seen to express factors which contribute to the degree of compliance by financial planners to any policies or procedures set by the Licensee. Additionally, this may also be seen to encompass softer areas of ‘unwritten law’ such as alignment to the tone and culture desired by the Licensee.

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Complaints

Performance feedback

Key performance indicators

Monitoring by relationship managers

Number of file auditsNumber of files vetted

Number of errors

Likelihood of breach

Actual breach

Utilisation of tools

Appropriateness of tools

Use of paraplanning

Penalties

Terminated planners

Motivation

Seriousness of penalties

Resistance to compliance

Professional memberships

Reputation

Understanding of policy

Awareness of policy

Policy complexity

Policy requirements

Regulatory requirements

Ability to apply policy

Previous experience

Conflict of interest

Personal morality

File complexity

Time spent per file Cost per file

Complexity loop

Feedback loop

Ability to apply policy loop

Cost loop

Deterrence loop

+

+

++

+

+ +

++

+

+

+

+

++

+

+

+

+++

+

+

+

++

+

-

-

--

+

+

-

-

--

-+-

+

-

Compliance view

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Similar to the above education view, by examining the feedback loops we notice the following key themes in influencing the effectiveness of compliance mechanisms:

i. Feedback: The amount of formal and informal feedback (e.g. complaints) used as a gauge of compliance;

ii. Ability to apply policy: The understanding and ability of the financial planner to apply the established policies and procedures in practice;

iii. Deterrence: The effectiveness of deterrence mechanisms to motivate or dissuade authorised representatives from breaching set policies;

iv. Complexity: The complexity of established policies and procedures and the effort required to apply them;

v. Cost: The costs of compliance or non-compliance with procedures and the costs associated with meeting their requirements.

5.6 Recruitment view The recruitment view reflects factors which affect the innate ability of the financial planner to meet the desired knowledge and skill requirements. Whilst education and training provided subsequent to recruitment is one mechanism used to meet these requirements, the recruitment view depicts factors which reflect the potential of the candidates recruited.

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Amount of irrelevant work experience Irrelevant professional

memberships

Transferrable skills

Level of irrelevant education

Gaps in knowledge and skill

Level of award

Education marks

Relevance of previous education

Knowledge of industry

Relevance of previous work

Time in previous relevant work

Gaps in work

Professional memberships

Desired level of knowledge and skills

Desired knowledge of industry

Regulatory requirements

Internal recruitment requirements

Recruitment criteria

Desired cultural alignment

Cultural differences

Quality of referencesPositive bias in perceived skill gaps

Interview technique

Likelihood of miss-hire

Difficulty of assessment

Time taken to recruit Pressure to fill recruitment gaps

Costs of recruitment

Need for effective recruitment

Importance of recruitment timing

Age

Family and outside commitments

Career focus

Career motivation

Intensity of competing recruitment

Number of recruiting competitors

Remuneration

Number of candidates

Transfer loop

Relevance loop

Criteria loop

Market loop

Lifestage loop

+

++

+

- -

-

+

+

+

++

+

+

+

+

+

+

++ +

+

+

+

+-

+ -

+

-+

-

-

-

+

+

++

+

+

+

+

+

+

+

+ -

Recruitment view

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The recruitment feedback loops are derived as being functions of: i. Knowledge transfer: The degree of transferrable knowledge that the financial planner is able to

bring from their previous education and work to their current role; ii. Relevance: The degree and direct relevance of previous education and work held by the

financial planner prior to recruitment; iii. Criteria: The stringency of criteria used to assess candidates for recruitment – the desired level

of candidate quality; iv. Market: The intensity of market competition and the relative attractiveness of the Licensee’s

offerings; v. Lifestage: The stage of the candidate in their lives and its influence on their desires, ability and

expectations.

5.7 Service view Arguably the least influential of the four critical drivers, the service view represents the ‘value add’ of the Licensee to the financial planner and the Licensee’s ability to meet the service requirements of its authorised representatives. That is, whilst the other three views reflect longer term influences and factors, the service view reflects the ability of the Licensee to support their financial planners in meeting their short term needs and requirements through offerings such as technical help lines and communications.

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Complexity of regulations

Desire and need for strategy advice

Complexity of the file

Desire and need for product advice

Product related uncertainties

Research required

Need for external support

Time spent per file

Time available

Cost per file

Support tools available

Utilisation of tools

Availability of support

Effectiveness of support/advice delivery

Availability of non-licensee advice

Availability of licensee advice

Level of expected licensee support

Regularity of licensee communications

Frequency of file reviews

Need for file review

Rapidity of environment changes

Customer complaints

Relationship with client

Expectations of client

Awareness of client

Number of disputes

Regulatory body engagement

Difficulty in explaining recommendation

Disclosure requirements

Support loop

Complexity loop

Client loop

Review loop

++

+

+

++

+

+

+

+

+

+

+++

+

+

++

+

+

+

+

+

+

+

++

+

+

+

-

-

-

-

---

++

Service view

The ability of the Licensee to influence either the level of support required or meet the desired level of support are dependent on factors of:

i. Complexity: The complexity of the situation and circumstances and their impacts on the financial planner’s desire for support;

ii. Review: The rapidity of changing circumstances increasing the need for review and changes; iii. Support: The amount of Licensee and external support available and the effectiveness of the

mode of delivery; iv. Client: The expectations of the client and the ability of the financial planner to meet such

expectations over time.

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5.8 Factors in quality Although seemingly complex, in this section a generic model of the systemic structure has been developed and presented. Four key critical drivers, the views of education, compliance, recruitment and service have been developed in an attempt to explain the interdependencies of the systemic structure and how the factors or variables may influence quality. It is recognised that there may be significant deficiencies in the relationships presented and that the inputs may not influence the system as expected. The relationships may not reflect truth in the unqualified world; however the intention was never to present a model which was absolutely and verifiably accurate and complete. No diagram can be comprehensive or final, and no attempt should be made to make the diagram either of these. The diagrams should be built up and improved successively in steps over time, as modelling is the art of simplification (Sterman, 2000). They are intended to form the basis for creating and hypothesising about the effects of our decisions and will be key contributors to our model in determining the core factors in influencing quality.

Quality

Strategy

RiskInherent systemic structure

Contributing factors

Product

PART TWO A model for quality

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Design and execution As one begins to decompose and map the intricacies in the design of quality, it becomes increasingly apparent how complex the system can be. Whilst applying the concepts discussed previously can help to optimise and manipulate the structures we have in place, our capabilities to adequately do so are increasingly reduced and limited as the complexity of the system rises. It becomes increasingly easier to become entangled in the detail, disorientated towards what one originally set out to achieve. This part will explore the final piece of the framework, a model which integrates the concepts explored in the previous part. However, at this stage it may do us well to consider again the purpose of the paper in presenting a conceptual framework for quality. It is written under the belief that many of the issues and obstacles that an organisation suffers from are self inflicted due to policies causing unintended consequences – policies which result primarily from a poor or erroneous understanding of how organisations and their agents actually behave (Forrester, 1998). It is unfortunate that the activities of ‘modelling’ are often associated with negative connotations such as being expensive and time consuming, able only to achieve theoretical value at best. This is surprising considering that even the most informal organisation models its activities. “When individuals construct a vision in their minds about what they want and how things are – that’s modelling” (PricewaterhouseCoopers, 2010). Modelling is inherent in all organisations and systems, which may not necessarily come about as an overt and formal process, but rather as a result of informal decisions made by people over time – a series of informal models applied without explicit thought. “The models stitch together, either naturally as people adjust them when they bump into each other, or in ill-fitting collections of processes that people continually work around” (PricewaterhouseCoopers, 2010). Having said that, the greatest risks from modelling is that much resource can be spent trying to describe every possible factor and implication of every possible change that the model becomes too complex to use. Or conversely, applying the models too rigidly, attempting to apply it to all situations regardless of purpose (PricewaterhouseCoopers, 2010). Licensees must determine the balance of the complexity of the model to be applied and its potential value. Unfortunately, the nature is such that there cannot be a predetermined guide for the potential for value, meaning that transformations should be taken incrementally and in a way natural to the organisation. As stated previously, the aim of this paper is to only facilitate the broadening of the approaches and principles currently taken in decision making, regardless of whether the framework is formally adopted.

6.1 Commander’s intent Traditional command and control structures (in the military sense) consolidated strategy and power in a single commander who had the oversight over an entire battlefield and all those within it. At the time, this was effective in traditional armies that employed largely linear tactics (e.g. structured infantry formations) and emphasised blind obedience. However as armies grew, commanders quickly realised that the previously taken for granted elements of oversight, control and knowledge were no longer at

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37

their disposal. These structures were incapable of responding to “Napoleon’s method of waging war, which swept away the traditional armies and their ... intolerance of independent action” (Shattuck, 2000). Napoleon realised that battle was not governed by predictability and order, but rather by ambiguity and confusion. Although a far cry from the complexities and confusion inherent in army warfare and battle, it nonetheless remains true that there will always be a degree of ambiguity and confusion in all operations, including those of business and financial planning – whether it be because of limitations in communications between agencies within the Licensee itself, external parties such as regulators, or uncontrollable market influences. The framework must therefore recognise that despite the most detailed efforts to understand the interactions of the system, design and perfect the operating structures and establish the most clear and concise measurements and goals, there is always a risk of undesired consequences. The organisation system developed by Napoleon to reduce the uncertainty and complexity inherent in battle so that they could function individually was by decentralising the system, with armies thought of as collectives that contributed to a common goal (Howard, 2006). Further developed by the German army, this often meant that “leaders consciously traded assurance of control for assurance of self-induced action ... The superior trusts his subordinate to exercise his judgement and creativity, to act as the situation dictates to reach the maximum goal articulated in his mission” (Shattuck, 2000). This concept of individual mission-orientation is commonly known today as ‘commander’s intent’ and shall form the fundamental overarching principle in the framework. Businesses are no stranger to commander’s intent and it is often given under other terms such as in a company’s mission statement or objective. In essence, it is a broad conceptual vision of their purpose and it expresses what the business wishes to accomplish. It forms the logic and guidance for all operations and provides a unifying theme aligning all parties involved, at all levels of the organisation (US Department of the Army, 2006). Applying commander’s intent to the framework for quality, it may simply be given by the intent to:

“Strengthen advice which achieves the financial goals of the client”

Evidently a rehash of our definition of quality, this short statement of logic allows for “Subordinates’ imitative – the assumption of responsibility for deciding and initiating independent actions ... when an unanticipated opportunity leading to the accomplishment of the commander’s intent presents itself” (US Department of the Army, 2006). Whether it is applied to decisions of policy, compliance or education, so long as the initiatives aid in strengthening advice which achieves the financial goals of the client, they may act in good faith that their contributions to the good of the whole will be supported. Besides the formal policies and procedures put in place to achieve similar aim, it is important that the principles of commander’s intent also permeate through the entire framework, as whilst the established mechanisms for monitoring, measurement, feedback etc. remain relevant, it is impossible for the Licensee to predict all consequences or risks that may obscure achieving the desired outcomes.

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Military doctrine also leaves us with a number of core principles which can be directly applied in developing commander’s intent for the advice framework (Shattuck, 2000):

i. Start early: There is a strong need to establish a healthy climate with appropriate reward structures based on desired values;

ii. Acceptable operating limits: Despite the aligned purpose, there should nevertheless be constraints and restrictions on the ways a task can be accomplished (e.g. rules and regulations);

iii. Rationale: Explaining the rationale assists in understanding and developing similar decision patterns;

iv. Feedback: Feedback should be sought regularly as the potential for misunderstanding is great in some circumstances;

v. Individual differences: Recognise individual differences and interact with these differences accordingly.

An overlaid commander’s intent and the application of these guidance principles greatly increase the effectiveness of collective decision making in our framework.

6.2 Design and planning Chapter 2.3 Complex problem solving, gave the implication that the features of complex problem solving are much the same as those found in complex systems. One of those features was the aspect of polytely – that a problem or system may concurrently have multiple and in some instances conflicting goals. While attempting to solve or uncover an intensely complex problem or system, “the solution of one of its aspects may reveal or create another, even more complex, problem” (US Department of the Army, 2006). It should be apparent from the interdependencies depicted in the presented feedback diagrams that the systemic structure is such that a single input may result in a number of seemingly uncorrelated events. Therefore, it is logically expected that a solution or apparent solution to one aspect may also cause unexpected results and must be compensated by understanding the system as a whole (refer to the concept of emergence in chapter 2.1). Discussion up to this point in regards to the factors and components of quality has mostly been restrained to extensions of reductionism; considered mostly in isolation of each other, we have not yet considered the impacts of emergence and of the system as whole. This approach is limited in addressing issues of polytely, as it does not allow for a clear understanding of the complex environment of the situation. Although the need to consider operations as whole is generally recognised by Licensees, the difficulties and challenges they face in doing so often force the agencies of the Licensee to revert to operating in ‘silos’, where although the intent and processes are aligned, there are great barriers to the dissemination of information and feedback across functional areas. As staff are rarely in a position with complete understanding (and authority) of the system, their activities are largely limited to those of planning rather than of design. “Planning applies established procedures to solve a largely understood problem within an accepted framework. Design inquires into the nature of a problem to conceive a framework for solving that problem ... It has an intellectual foundation that aids continuous assessment of operations” (US Department of the Army, 2006). Licensees often have mechanisms for focussing on the physical actions

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Design and planning continuum – US Department of the Army (2006)

• Problem solving • Physical • Procedural • Paradigm accepting • Patterns and templates activity

intended to directly affect the environment; the mechanisms are adept in breaking down the problem into separately manageable tasks using the resources available and are based on established understanding of the situation and norms (e.g. the identification and rectification of a single incident). Rarely is there a means to consistently conceptualise and hypothesise about the underlying causes and dynamics that explain an unfamiliar problem, which may not have a stepwise process (US Department of the Army, 2006). Design mechanisms must form a core aspect of the advice framework, which focuses on challenging existing perspectives and the reframing of the problem rather than developing courses of action. This is necessary as at the present time, so little information and understanding is available on the concept of quality and its true drivers. The premise is that once the Licensee is able to “achieve a level of understanding such that the situation no longer appears complex, they can exercise logic effectively” so much that the results of their decisions and actions are always positive in pursuit of advice quality (US Department of the Army, 2006).

Design Planning• Problem setting • Conceptual • Develops understanding • Paradigm setting • Questions assumptions and methods

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6.3 Design for quality Considering the above, one of the fundamental aspects in our framework for quality, is that it must be design focused (not planning focused) and iterative. The nature and opaqueness of the system is such that one attempt to validate or derive the influences of Licensee decisions and policies will not be successful due to the limited amount of feedback that can be obtained. Consequently, “efforts require repeated assessments from different perspectives to see the various factors and relationships required for adequate understanding” (US Department of the Army, 2006). The aim should be directed towards achieving incremental improvements, rather than a single one off victory. We do this through observing events (the components of quality) and constructing logical explanations of these events (using feedback diagrams) that unravels the system. Through assessments and feedback over a period of time, this mechanism will form the basis for learning, adaptation and subsequent design adjustments to our framework for quality. “The design can be viewed as an experiment that tests the operational logic, with the expectation of a less than perfect solution. As the experiment unfolds, interaction ... reveals the validity of assumptions, revealing the strength and weaknesses of the design” (US Department of the Army, 2006).

The design cycle

Although seemingly simple, it is often the opaqueness of the system, the difficulty in which to obtain correlated and direct feedback from the system which deters most Licensees from adopting such a progressive quality framework. Nevertheless, despite the challenges, this is the approach that must be taken to successfully achieve the desired end state. Key design considerations which may assist in accomplishing success include the following (US Department of the Army, 2006):

i. Critical discussion: Rigorous and structured discussion provides opportunities for interactive learning and leverages collective experiences ;

ii. Model making: Approach taken to hypothesising and developing the model; iii. Intuitive decision making: Ability to reach conclusions based on knowledge, judgement,

experience, education, intelligence and perception;

Design

Execute

ObserveLearn

Rationalise

DESIGN AND EXECUTION

41

iv. Continuous assessment: Identify where and how the design is working or failing and to consider adjustments to the design and operation;

v. Structured learning: Development of reasonable initial designs and then learn, adapt, and interactively and continuously improve that design as more about the dynamics of the problem become evident.

6.4 Logical lines of operations Having developed the framework for design, we now turn our attention to the principles associated with the execution of plans intended to influence the system. As mentioned above, the nature of the system is such that apparent solutions for one particular issue may lend itself to create other issues or even change the dynamics of the system entirely. Additionally, in practice, unlike in controlled physical experiments one is unable to keep all other constants equal and the same, while variables are selectively targeted. To be relevant, the framework requires synchronised application of changes to multiple areas such as of education, compliance and recruitment. An effective means of executing such plans is through the controlled application of logical lines of operations (LLO). Logical lines of operations is a method used to unify and synchronise multiple plans towards a common goal or purpose. Logical lines of operations allow us to visualise the key plans that will allow achievement of the end state along with the required goals and objectives (which will form a critical input into the later learning and assessment stages). It may be thought of as the execution of the hypotheses in our design model, with success in one logical line reinforcing successes in the others (US Department of the Army, 2006).

There are no limits to the number of logical lines of operations nor are there restrictions on the areas they may relate to. The key premise is the continual refinement of the established logical lines of operations and the effectiveness of progress along each line.

Enhance financial competency

Enhance risk profiling capabilities

Streamline documentation requirements

Reduce costs to serve

Non-compliant

Quality advice

Compliant advice

Quality advice

Compliant advice

Starting conditions End state

Example logical lines of operations for quality

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Drilling down further, the below presents one simplistic example of the individual line for enhancing the financial competency of financial planners. On the left side are the four key critical causal factors that were previously determined, which are used to determine the steps and objectives required along the LLO to achieve the desired end state (the initial predictions of the effects of our decisions and actions). It should be noted that different factors may have varying degrees of influence in achieving the end state and thus may have a comparatively reduced number of tasks (as with factors of recruitment and compliance in this case). On the right side is the desired end state, with goals associated with enhancing financial competency translated into observable and measurable aspects. Although these may be either qualitative or quantitative, focus should be given to the observable measures derived from the components of quality (which is explored later).

EducationReview material content

Enhance material delivery

Review assessment ciricculum

ComplianceEnhance

policy principles

Enhance financial competency

RecruitmentIncrease

education requirements

ServiceEnhance

research tools available

Enhance transparency of

market info

Increase timliness of

product updates

End State

Increased awareness of policies Reduced deviation from performance benchmarks Decrease in time spent on product research Reduced number of financial product complaints

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Assessing quality

7.1 Learning and adapting The design cycle is only complete with a mechanism for learning and adapting. Executing changes to processes, operations or policy is ineffective on their own without an assessment of their influence on quality. This is the key aspect which separates a progressive framework based on outcomes from that focused purely on compliance and activity. It is only through assessment and evaluation that one is able to adjust and adapt accordingly, based on the effects of previous decisions. Assessment is the monitoring and evaluation of the current situation and progress. It involves the comparison of progress with the end state as derived from the commander’s intent, and determining why and when progress is being achieved along each logical lines of operations. In general, it involves two main tasks (US Department of the Army, 2006):

i. Continuously monitoring progress; and ii. Evaluating against established criteria.

Traditionally, progress is usually evaluated through the use of key performance indicators which target specific areas of performance, using either qualitative or quantitative discrete measurements. Whilst this is useful in assessing the short term effects of decisions on progress and whether the predetermined end state observables are being achieved, there are limits to their use when evaluating complex systems. The systemic structure of complex systems is such that while a decision may have a positive effect on the indicator being measured, it may also have negative effects on an area which is not being measured (refer to chapter 5.1 Factors in quality). “For example, in South Vietnam U.S forces used the body count to evaluate success or failure of combat operations. Yet, the body count only communicated a small part of the information commanders needed to assess their operations. It was therefore misleading ... [and] did not measure several important factors: for example, which side the local populace blames for collateral damage” (US Department of the Army, 2006). We therefore infer the need of having broad indicators of progress, in addition to those specifically targeted on aspects of plan delivery and execution. Put another way, assessments must be performed at two distinct levels:

i. Level of components: Individual logical lines of operations, with observables tracking the steps in achieving the end state for that line; and

ii. Level of emergence: Holistic assessment, able to assess the interactions of all logical lines of operations and the achievement of the overall end state (quality).

Some may argue that due to the complexities of the system and the abstract nature in which we have defined quality, the proper measurement and assessment of quality at either level is difficult at best and requires high degree of subjectivity – nullifying its value. Again, while true to an extent, this is not to say that any attempt at measurement and assessment is not valuable in determining why and when progress is being made – it merely requires an adjustment of one’s mindset towards measurement.

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7.2 Concepts of measurement Conventional ‘hard’ approaches to problem solving requires precise and quantitative measurements directly attributable to the object at hand – as stated by Thomas Reid the philosopher, “there is no greater impediment to the advancement of knowledge than the ambiguity of words”. However, this is somewhat of a misconception, as one may simply define measurement as “a basis for comparison, a reference point against which other things can be evaluated” (Miller, 2009). Therefore, one approach to measuring quality is to not measure it directly, but to instead observe and measure its constituent components, thereby enhancing the reference point used for evaluation. Much like gravity, one does not attempt to measure gravity directly, but views it instead as a systemic system “of specific equations that [link] gravity to such concepts as mass, distance, and its effect on space and time” (Hubbard, 2007) which are the components that are observed and measured.

Additionally, besides the absence of the need to measure the subject matter directly, measurement also does not require absolute precision but rather relative precision. For example, if one were to ask of your current age, it would not be helpful or acceptable to answer that it was 189,396,000 minutes. Referring back to the definition of measurement as a reference point, when measuring larger subjects such as the distance to the moon, it is often more useful to use a broader coarse measure such as kilometres, so long as we are able to utilise that measurement or reference point effectively and suitably for our needs. Similarly, when applied to quality, relative to the current practice of having no definition for quality and no measure of quality, it is already valuable and useful to have a broad and coarse measure, so long as it can be developed and expanded in the future as more advanced tools develop.

7.3 Current monitoring approach Supervision and monitoring forms a key component of any standard of advice framework, and may incorporate controls to monitor the provision of advice through mechanisms such as paraplanning, vetting and planner self assessment. However, the most common and deemed most effective method currently used is that of file reviews or an assessment of the Standard of Advice. This involves specialised personnel obtaining and physically reviewing written Statements of Advice for all or certain financial planners operating under the Licensee. “Financial planner file reviews tend to be the key mechanism used to provide comfort to management, committees and boards that advisers are complying with internal and legislative requirements” (PricewaterhouseCoopers, 2009). It is interesting to note however, that despite the review’s importance, there is no best practice or standard for the number of files reviewed, with numbers varying from a meagre three files per financial planner to six fold that, at 18 (PricewaterhouseCoopers, 2009).

Einstein’s field equation for general relativity

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Fundamentally, the practice of reviewing the Statements of Advice provided to clients is sound and provides for a logical way of “identifying if the [financial planner] is making reasonable client inquiries, if they are considering and investigating the subject matter of the advice as is reasonable in all the circumstances and if the advice is ‘appropriate’ for the client” (Parliamentary Joint Committe on Corporations and Financial Services, 2009). Following such review, it also appears logical that one is able to put in place corrective actions and take appropriate steps should the methods of the financial planner be found to be deficient. The fallacy however is not in the fundamental approach taken, but in the detail. There are number of limitations with this approach, some of which are expanded below which detract from its effectiveness:

i. Resource: Due to the time and effort required to perform the file reviews, most Licensees adopt a ‘risk based’ approach, which means the scope of the reviews are more targeted and narrow – a number of representatives deemed ‘low risk’ may not even have their files reviewed at all. Additionally, for those representatives that are reviewed, the number of files selected (i.e. 3 in some cases) may be insufficient to detect any deficiencies given the overall population of files and the different types of business written by the financial planner throughout the year;

ii. Responsiveness: Deficiencies noted through file reviews are lagging indicators based on information in Statements of Advice issued months prior. During times of economic crises, a desire to increase monitoring activities will be ineffective in causing any practical change as a period of time must pass before deficiencies can be identified and the effects of changes are able to be observed;

iii. Information: Reviews are undertaken on files prepared months prior, as such it may be difficult for assessors to adequately discern certain elements or thought processes due to inadequate or ambiguous file documentation. Financial planners are often given the ‘benefit of the doubt’ and heavy supplementary commentary from the financial planner is often required as part of the file review process;

iv. Static: File reviews are generally static and assess the representative’s due diligence and recommendations at that initial point in time. It generally does not incorporate the appropriateness of subsequent recommendations and actions taken subsequent to initial recommendations (e.g. frequency of follow up reviews);

v. Product quality: File reviews focus on the general due diligence processes and does not adequately assess the elements of product quality – that is, the process for product selection, monitoring and exit;

vi. Remedial actions: Due to its nature, corrective actions following file reviews are generally short term, isolated and limited to that of the file or financial planner. File reviews alone is not an effective medium for driving core operation process change, as the deficiencies identified often appear disparate and unrelated due to the multitude of scenario possibilities;

vii. Exclusion: Due to the incentives to utilise the Licensee tools available such as paraplanning or vetting services, exclusions or exceptions are often extended to representatives for some files reviewed, as such appropriate assessment or penalties may therefore be left unapplied;

viii. Objective: At the present time, the focus is much on compliance instead of quality as the basis and objective of the review. This is an important distinction as this objective sets the tone for how the file reviews are carried out, the questions that are asked and the criteria used in the assessment. A perfect score using a compliance driven assessment tool may not yield the same results when assessed for quality.

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Further, “organisations [that] have identified a relationship between their definition [of quality] and their supervision and monitoring approach, [are] becoming more targeted and even reducing the number of files reviewed” (PricewaterhouseCoopers, 2009). Quality in this respect may be seen as a mechanism for extending the ‘risk based’ approach to overcome difficulties of resource rather than a mechanism of progressive improvement as that proposed by this paper.

It is the position of this paper that an assessment focused on quality may overcome a number of the issues highlighted above and is able to incorporate all elements of compliance – as discussed previously, quality is all encompassing, of which compliance forms a subset. As time goes on, it is more likely that organisations will be looking to establish formalised guidelines supporting the increased need for robust “up-front and ongoing product and client-need due diligence and comprehensive client reviews ... These requirements will then need to be promulgated into relevant policies and procedures, training, KPIs, monitoring and supervision and incentive arrangements” (PricewaterhouseCoopers, 2009).

7.4 Measuring and assessing strategy and risk quality One of the most influential contemporary theories about the nature of law is that of ‘Interpretivism’. In part, it is the view that legal rights and duties are determined not just by legal requirements, but is also sensitive to the facts, values and principles of those applying law and of the community (Stavropoulos, 2008). Similarly, the dynamics of both strategy and risk quality leads to the view that there is no ‘right’ answer, that the recommendations or decisions that a financial planner proposes is also sensitive to the facts of the client and the values and principles of the client and of themselves. As described in chapter 4.3 Components of quality, strategy quality in particular, is achieved through the cumulative effect of numerous decisions and recommendations made throughout the entire process and over a period of time, rather than there being just one ‘right’ recommendation. Strategy and risk quality is therefore a nature of thought process, which requires the financial planner to apply a particular style of thinking in every decision and choice. When Statements of Advice are conventionally reviewed, the assessor is typically attempting to discern in retrospect whether the recommendation made was ‘right’, whether the recommendation made had merit or basis of support in that particular circumstance. This approach is particularly narrow and has limited effectiveness in assessing the core of strategy and risk quality – in assessing the thought process. Instead, Licensees in their assessment should be determining how effectively the financial planner is able observe and apply the principles established by the Licensee in making their decisions. In advice, much like the absence of a ‘right’ decision, there is also typically the absence of an absolutely ‘wrong’ decision – there are generally only decisions which create suboptimal results. Whilst conventional file reviews may have identified circumstances where decisions may have resulted in suboptimal results, as we have not assessed the thinking and thought processes, this is of little use when we attempt to apply or extrapolate issues to a different set of initial conditions. Therefore, in measuring strategy and risk quality, the method must be aimed at assessing how effective the financial planner is in observing and appraising the merits of potential choices and decisions using the principles desired by the Licensee, as it is the proper application of these principles which will steer the financial planner and ultimately the client away from suboptimal or at least disastrous results.

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The method proposed is a form of scenario or case based assessment where a fictional but realistic scenario is presented to the financial planner which requires their consideration and analysis. This is in essence the approach taken when ASIC ‘shadow shopping’ exercises are undertaken and volunteers are asked to obtain a comprehensive financial plan which is then used for assessment. However in the case of Licensee assessment, this general approach would be modified slightly so that at particular stages, the thought process of the financial planner would also be assessed for deficiencies. The comprehensiveness of their decision making process would be the key observable indicator of their ability handle complex situations and apply the principles desired. For the financial planner, in addition to being a required assessment by the Licensee, this method has a number of advantages including being:

i. Learner centric: The assessment allows for an element of discussion and the financial planner is able to engage and defend their decisions or clarify areas of misconception;

ii. Interactive: In addition to being an assessment, the session is interactive and provides valuable practical experience for the financial planner;

iii. Practical: Rather than being a theoretical reiteration of Licensee policy (which is often the case in addressing deficiencies noted in file reviews), the financial planner is able to observe the application of the policy in practice.

Similarly, for the Licensee, there are also a number of advantages over the conventional methods of file review, including that it is:

i. Holistic: It is able to assess all aspects of the advice process and is not plagued by the same file review limitations of inadequate documentation/information resulting in the ‘benefit of the doubt’;

ii. Comprehensive: It can be tailored to address any type of advice or circumstance and is not restricted by the amount or type of business written by the financial planner (often, planners that have written no new business during the period may not be reviewed);

iii. Responsive: The assessment is a type of leading indicator and efforts can be increased in times of duress if required;

iv. Efficient: Depending on the assessment’s structure, it may assess more areas of the advice process in a lesser amount of time compared to reviewing an increased number of files (as client details must be reviewed and reassessed in for each file);

v. Focused: As the financial planner’s thought process is constantly assessed against ‘best practice’ principles, the focus is always retained on quality rather than compliance;

vi. Remedial: Due to its interactive nature, the focus is instead on improving the thought process and investigating why the financial planner may have difficulties in applying certain principles, rather than the application of consequences and penalties which often result from pure file reviews (which is more of a deterrent nature than corrective).

7.5 Measuring and assessing product quality It appears surprising that despite the criticality of product quality in advice, so little effort is directed towards its measurement and improvement – especially considering that relative to strategy and risk quality, its effects and impacts are inherently easier to quantify and observe. When Licensees assess

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product quality, most effort is instead directed towards risk quality (i.e. the appropriateness and suitability of the financial product for the client) rather than of product quality itself. Whilst understandable to a certain extent, in that many of the factors affecting product quality may be outside of the financial planner’s direct control (and therefore it would be unfair or immoral to impose such an assessment), as explained in Part I, there are nevertheless controllable aspects of product quality which should be measured. Even if one was to consider for an instance that product quality lied entirely outside of a financial planner’s direct sphere of influence, product quality is such a core component to the value of advice that it would simply be unfathomable not to consider its effects, as after all, it is the performance of the financial products invested which determine whether the financial goals of the client can be achieved (quality). We made the statement above, that currently most Licensee effort was not directed in measuring product quality itself but in measuring risk quality. This may appear fair as given the intricacies intertwining risk and product quality, it would not be sensible to consider product quality in isolation. As previously explained, one is only willing to accept greater risk if the expected return from the financial product is higher, relative to an investment which does not present such a elevated degree of risk. An extension of this concept is that whilst certain types of investments in isolation (e.g. international equities) may be inherently risky, when combined with less risky investments (e.g. bonds) their contribution of risk to the portfolio, or the risk of the portfolio overall is reduced (indeed in theory, it is only through a combination of different types of investments will the greatest portfolio return for a particular degree of risk be achieved).

Example portfolio composition

Australian equities Australian bonds International equities Private equity Cash

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Financial planners or Licensees often break the spectrum of portfolio risk down into bands or ‘buckets’ of risk, which conceptually means that certain compositions of portfolios will effectively place them in a particular tier on the spectrum of risk. For example, portfolios comprised mostly of cash will be classified as being ‘conservative’ while portfolios comprised evenly of ‘defensive’ assets such as cash and bonds with a mix of ‘aggressive’ assets such as equities will be classified as being ‘balanced’.

Risk

Expe

cted

retu

rn

Portfolio returnRisk free

Conservative

ModerateBalanced

Aggressive / Growth

Modern Portfolio Theory

This is important, as much of the financial planner’s work goes into assessing the ‘risk profile’ or classification of the client based on the information gathering process and then constructing a portfolio which falls into the respective risk band. Correspondingly, most assessments of the standard of advice performed by Licensees and regulators are limited to determining whether the financial planner had adequately assessed the client in terms of ‘risk profiling’ and whether they recommended a portfolio which was suitable for the client’s classification of risk. However, while this may adequately assess the areas of risk quality, it fails to address issues relating to product quality. Chapter 4.3 Components of quality, identified two key decisions of product quality falling directly under the financial planner’s responsibility – decisions relating to the initial investment and decisions relating to the cessation of the investment. These two decisions are the culmination of the due diligence processes undertaken by the financial planner, incorporating areas such as identifying the ‘universe’ of financial products, researching the financial products themselves, considering the risk classification of the client, and applying the principles established by the licensee in monitoring the ongoing performance and appropriateness of the financial products selected for the portfolio. These areas of process and principle can be seen as extensions of strategy and risk quality (although we have technically termed them as areas of product quality) and can also be measured using the methods outlined in measuring strategy and risk quality. However, one is only able to gauge the effectiveness of how we applied the two product decisions by reviewing the performance of the portfolio over a period of time relative to expectations. This is the feedback necessary to validate the product quality principles and decisions previously made, as proper adherence to established Licensee principles (i.e. the financial products selected perfectly matched the risk profile required and inappropriate financial products were exited at exactly the right time), should always result in an overall portfolio return in excess or at least equal to the relative benchmark.

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As mentioned above, much effort goes into risk profiling clients and determining the risk categories in which they fall. It is also quite common for Licensees to establish ‘model portfolios’ which are portfolios that the Licensee believes to be appropriate for the respective risk bands. These model portfolios are ideal relative benchmarks for the assessment of product quality as they represent the application of Licensee principles and processes in their entirety. By comparing client portfolios to the corresponding model portfolio, one is able to gauge how effectively and aligned the authorised representative is in applying the desired principles of the Licensee whilst incorporating the effects of external market influences. Additionally, by performing such an assessment over a period of time, we begin to develop a mechanism for observing the effects of decisions and policies made by the Licensee on product quality, as we expect the deviations from the respective model portfolios to recede as representatives become more aligned to Licensee principles.

This section has developed methods for measuring the three components of quality. Through the development of these methods, it should begin to become quite clear that the components of quality are not disparate, but instead are intrinsically intertwined, despite our attempts to keep the components distinct. These observables should form the key inputs into evaluating the progress of individual logical lines of operations and what we envision the end states to be.

0%2%4%6%8%

10%12%14%16%

T T+1 T+2 T+3 T+4

Example deviations from growth benchmark over time

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7.6 Holistic quality The final piece of the framework is the assessment of holistic quality, an assessment of the complex system in its entirety. As the focus of this measure is longer in vision and a measure of the overall effectiveness of the logical lines of operations rather than a direct input in to any particular line, this measure should not be discrete but encapsulating. It aims to answer the question of “Are we achieving results that move us towards the desired end state, or are additional alternative actions required?” The most effective way to achieve this is through the periodic random sampling of a number of clients. Sampling is the practice of using a number of individually selected clients with the hope that the observations obtained are able to yield some knowledge or is reflective of the circumstance of the entire population. For example, if 20% of 30 clients reviewed had achieved quality, one may be able to infer (if sampled correctly) that 20% of the population of clients had also achieved quality outcomes. Additionally, rather than targeting any financial planner in particular, this would involve a comprehensive review of the client’s circumstance in its entirety and whether we have successfully achieved holistic quality for the client – whether they have achieved their desired financial goals. This is the only question that needs to be answered and it is irrelevant at this time to distinguish or exclude external factors of negative contribution, as the probably of achieving quality should also factor into the value of advice. Although factors such as client timeframe may return results which are undesirable, it should not be expected that quality is achieved one hundred percent of the time, but rather that there is some indication of improvement in the population. In this respect, random sampling is the simplest and most effective measure and gauge of the influence of not only the advice provided but the entire advice process.

“Much can be learnt merely from the faces of the population in villages that are subject to clear-and-hold operations, if these are visited at regular intervals. Faces which at first are resigned and apathetic, or even sullen, six months or a year later are full of cheerful welcoming smiles. The people know who is winning”

- Sir Robert Thompson

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Empirical analysis

8.1 Quantitative methods This paper has outlined the general concept and framework for developing hypotheses and direction in pursuing quality. The next logical extension of the framework is the application of empirical analysis. Quantitative methods allow us to verify and more importantly simulate the impacts of our efforts so that the areas of focus can be further refined. In section five Factors in quality, we developed means through feedback diagrams of explicitly highlighting the interdependencies of variables forming the systemic structure. However one aspect that we were unable to address at that stage was to what extent each variable influences the feedback loops identified. For example, although the ‘time spent per file’ was identified as one variable which influences the ‘cost per file’, we are unable to determine through feedback diagrams alone, how much influence and how much energy should be spent in on the variable of ‘time spent per file’. This was instead left to the application of logical lines of operations, however even so, the amount of feedback we are able to obtain from the system to determine the exact influence of ‘time spent per file’ is expectedly considerably low – only able to determine its general effects on the components of quality overall. Empirical analysis provides one means of verifying and simulating the influence of variables in causal feedback loops, greatly enhancing the accuracy in which we model the systemic structure and the design of our logical lines of operations. Indeed this was the approach taken by Bluethgen, Meyer, & Hackethal (2008) in applying univariate and probit regression analysis to simulate the influence of a financial planner’s education, experience, commission income and degree of irrationality on the probability of them recommending an index fund. Although this paper will not present extensive quantitative analysis due to the limited amount of empirical data currently available, the following chapters will outline the general approach that is commonly taken by current literature.

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8.2 General application Having developed a general model for quality and mapped the interdependencies of variables in causal feedback loops, we may apply subsequent empirical analysis to test relationships in order to determine whether they behave as is expected. The generalised mathematical model for empirical analysis of this type can be described as follows (Davis, 1996):

OI = f (Ai , Bj)

Where: OI = dependent variable or outcome information; Ai = controllable independent decision variables; Bj = uncontrollable independent variables; f = indicating a functional relationship between the independent and dependent variables

This general model can be applied to the causal feedback structures by making some standard assumptions. For example, if applied to the ‘transfer loop’ taken from the recruitment view, it may be expressed as:

Transferrable skills = f ( b0 + b1EXP + b2EDU + b3MEM + e )

Where bi (i = 0, 1, 2 ...) are the coefficients, e the error variable and the independent variables EXP, EDU and MEM representing work experience, education and professional memberships respectively (the variables identified in the causal feedback loop) (Nguyen, 2001). Having developed the model, we subsequently test it through a relationship proposition. Each causal feedback loop can be thought of as a hypothesis, that is, we are hypothesising that certain variables have negative or positive effects on each other within the loop. These in turn can be alternatively thought of as “expected effects” which gives an overview of the hypothesis for that loop.

Table 1 Measure : Transferable skills Derived from the time taken until issue of first Statement of Advice Variable Definition Expected effect Work experience Years of irrelevant work experience + Education Years of irrelevant education + Professional memberships Number of irrelevant professional memberships held +

Overview on expected effects, variables in the ‘transfer loop’ – recruitment view

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Through the collection of data and research efforts, we may for example derive the following measures from regression analysis with the dependent variable being transferrable skills:

Table 2 Transferrable skills* Work experience -0.664 Education -0.042 Professional memberships -0.014 Constant 2.435 Observations (n) 51 R-squared 0.32

*Note that lower values indicate greater transferrable skills (as less time is taken, refer Table 1) Referring to the above measures, this is one example which may substantiate our hypothesis that transferrable skills are positively influenced by the variables of work experience, education and professional memberships held (as observed by the negative coefficient signs). Additionally, it provides us with insights that show that the main variable of influence is the number of years of irrelevant work experience held at time of recruitment – an aspect that we were not able to previously discern.

8.3 Further investigations Whilst only a theoretical example, this section has illustrates how empirical analysis provides a promising step towards verifying the assumptions made and improving efforts to target variables that most drive change. However, the greatest obstacle in applying empirical analysis to quality advice is not in the development of the general models themselves, but due to the unavailability of data. Quite often the variables that the causal feedback loops incorporate are of a qualitative nature or are of metrics that are not currently captured. Often substitutes or dummy variables are used in place of ‘true’ measures for the variable, such as how a variant of time was used in the above example as a dummy for transferrable skills. Whilst techniques such as surveys or questionnaires can be used to measure qualitative values (as was used by Bluethgen, Meyer, & Hackethal (2008) to determine ‘irrationility’) , this can cause misleading or skewed results. Nevertheless, the beginning of research undertaken by Licensees is the important first step in validating many of the assumptions incorporated and in improving the measurements used for qualitative variables.

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Holistic Quality

Framework for quality With the final piece of the puzzle now in place, we have completed the development of a comprehensive conceptual framework for the quality of advice. We have explored in this paper two parallels, the first parallel reflecting upon the idea and concept of quality and the second parallel, exploring a comprehensive mechanism for capturing the essence of quality for process improvement. There are no simple and quick solutions to achieving quality of advice. The dynamics of the system is such that the decisions and changes in the environment may in fact cause the systemic structure and the causal feedback loops to shift entirely, thereby requiring on-going learning and assessment even for sustainability. These are areas have largely been untouched by this paper, but are aspects which must be considered in practice. The final section of the paper will briefly explore some issues that this paper has identified as key for future consideration.

Quality

Strategy

RiskInherent systemic structure

Contributing factors

Product

Logical lines of operations

Logical lines of operations

Learn

Redesign

Commander’s Intent

Starting conditions

Causal feedback loops

End States

Measuring components of

quality

Part II: Model for quality

Part I: Defining quality

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Sustainment

9.1 Quality mindset Despite the concepts discussed in this paper, the greatest obstacle to achieving real improvements in the quality of advice is not the inadequacies of process or methodology, but the mindset at all levels, that compliance and growth are distinctly two separate areas. Licensees are plagued with the mindset of thinking in terms of the ‘costs of compliance’, instead of thinking of improvements in the standard of advice as a channel to growth. PricewaterhouseCoopers (2009) notes that changes to the current advice approach will “take time and lead to increased compliance costs”. In manufacturing products such as cars, regulations aim to protect and ensure the safety of consumers. Standards must be met to ensure that not only are the manufactured cars high performing, but that they are also road worthy and safe. This is required as it is entirely possible to build a car which is fast and unsafe, some performance cars may in fact even be made out of wood. However, this same principle does not extend into the space of advice, as it is logically impossible for quality advice to be non-compliant (obviously excluding the practicalities of disclosure etc.). Quality advice is by its very nature compliant, as that is in fact what the regulations aim to achieve, to ensure that a minimum standard of quality is achieved in the advice provided. Why then should we consider efforts to improve quality separate from efforts to achieve compliance? Logically they should be one in the same, and efforts to achieve growth should incidentally include areas of achieving compliance. One potential cause is that the Licensee structure is such that authorised representatives are often not employees of the Licensee, but self employed representatives operating under their AFSL. Whilst few would hesitate to invest in employees and consider such investments as investing in potential growth, this cannot be said of financial planners operating under this structure. It may be because of this structure that Licensees are hesitant to provide the same extent of commitment and dedication to financial planners operating under their own businesses, as there are less conceivable returns on invested capital and it may be easier to simply remove those struggling from the dealer group.

9.2 Conflicts of interest Many Licensees are often part of larger financial services groups that are vertically integrated, with arms of both distribution and product manufacturing (which we will term as the ‘greater corporate’). Despite the efforts to push for non-commission based advice, there is nevertheless the inherent conflict of interest (at least from the perspective of the greater corporate) to increase funds under management. In theory, these two entities (i.e. the product manufacturer and the related Licensee) should be legally and principally separate and independent. Decisions of the Licensee to restrict or allow certain products to be recommended by their authorised representatives, should not favour those of the greater corporate. The greater corporate in this respect is ‘double dipping’ by earning a Licensee fee for the initial or on-going advice provided by the authorised representative and earning a second fee for the management of funds invested in their products. It is extremely difficult in such a circumstance for the Licensee to

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pursue true quality in advice with this mental obstacle – especially when financial quality is such a key component.

9.3 A social science This paper has been developed on the assumption that the Licensee has or is in fact able to develop exact methods and principles for achieving quality of advice – that for example, through the application of fundamental concepts such as diversification, quality can be achieved. Unfortunately in practice, sciences of finance and economics are based on social interactions rather than that of the physical. Many of the laws governing finance and economics are still undiscovered and consequently, what may sound logical and appropriate in principle may not actually lead to the desired results. Especially when dealing with future events, it is extremely difficult even for professional economists of many years to make accurate and correct predictions. This obviously has fundamental impacts on whether quality advice can be achieved and in essence questions whether any value can be obtained from advice, if at all (although it can be argued that there are independent aspects of strategy quality which are nevertheless valuable such as encouraging the client to save regularly).

9.4 Resistance to change The approach outlined in this paper may be deemed by some as quite ‘radical’ and extensive. As is to be expected, even the most minor element of change will be met by some degree of resistance, either from the financial planners or even by the Licensee. This is one major challenge that must be overcome, as the framework towards quality requires strong long term commitment, the results of which may not be visible in the initial stages. The framework also requires a set of skills which may differ entirely from those employed today. The critical aspects of rationalising and learning requires strong collaboration and levels of increased overall accountability which may be foreign to traditional structures of functional units. Whilst cross functional teams have been thought of as a means to address these issues, due to their ability to coordinate across functional areas, previous application has been inconclusive on their impact on end performance (Fowler, 2009).

9.5 Future direction

A powerful model is not simply an accurate model, but rather one where those who use it have faith in its applicability. In this regard there are two obvious areas that future research or application can focus their efforts. The first is to improve the accuracy and preciseness of the model, by incorporating feedback and changes over time – by refining the generic model into one that closely reflects the bounds and interactions of the real world. The second area of effort should be focused on obtaining

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data and quantifying aspects that were previously thought of only in the abstract - improving the model by incorporating quantitative data from real world trials, eliminating the assumptions and estimations. Assumptions should [always] be challenged and all ideas should be discussed and incorporated based on their merits (Fowler, 2009).

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Conclusion It must be said that the complexity of the framework presented is not of a degree that Licensees or even businesses in general, are normally accustomed to. Business theory is still very much in its infancy, without the same foundations and laws that you would expect from a similar science of a physical nature. It is consequently difficult to determine and quantify the marginal benefits of any transformation process without proper (and conceivably lengthy) experimental trials. Without proven supporting data, there is a degree of associated risk that cannot be removed (although not unlike those created by any change or transformation process). Licensees will need to critically appraise the marginal benefits that are to be obtained from a refined focus on quality, to that which is reactive and responsive to the regulations and obligations stipulated by ASIC. It cannot be disputed however, that despite any degree of industry resistance, change is inevitable as clients expect and demand an increasingly higher degree of transparency.

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