data & analytics … and the finance function part 1 of 2 · profitable than their...

November 2017

Data & Analytics

… and the finance function

Confidential information for the sole benefit and use of PwC’s client.

James Larmer

Part 1 of 2

PwC Data & Analytics

Today

1. Change: Mega trends and technology

2. Decision Making: The new competitive frontier

3. How can Analytics Support decision making?

4. Analytics @ Work

Internal Audit

Intelligent Automation

5. Artificial Intelligence

6. Creating a Data-Driven Enterprise

7. Q&A

PwC | Analytics Forum 2016 | page 3

Digitization of everythingDigitization is transforming how people discover, engage and transact

with business and with each other

Mobile Internet Users by 2020

3.8 BillionInternet Users by 2020

5 Billion Photos uploaded and shared

each day

1.8 BillionSensors and devices in use by

2020

50 Billion

Big Data - New sources of data…

…structured, unstructured, audio, image, video, sensors, and social

Volume, Variety, Velocity, and Veracity of Data is driving the ‘Big Data’ Revolution

Big Data Technologies - New emerging technologies

…distributed stream processing, GPU’s, NoSQL databases

Cloud computing, open-source software, multi-core processors, are fuelling big data technologies

Big Data Analytics - New Analytic Techniques…

…backward & forward looking, statistical & computational, art & science

Graph Networks

System Dynamics

Descriptive, Diagnostic, Predictive, and Prescriptive Analytics are enhancing the value of Data

PwC

An Accelerating Pace of Change

Are we in a Second Machine Age?

Acceleration Laws Apply to Analytics & Data

1900 1960 2000 20201980

“Computers and other digital advances are

doing for mental power … what the steam

engine did for muscle power”

- Andrew McAfee -- Erik Brynjolfsson -

PwC

Computing power doubles every 18 months

- Gordon Moore -Co-Founder of Intel

Exponential impact on:- Transistors / Chip- Gigabytes per $- Internet speed

- Energy efficiency- Supercompter Speed

Moore’sLaw

The Impact of Moore’s Law

ASCI Red Sony Playstation 3

Introduced in 1996Cost: US$55 millionSize: 100 Cabinets, 1,600 Sq. Ft.

1997: 1.8 teraflops of speed

Introduced in 2006Cost: ~US$500Size: 1/10th of a sq. meter

2006: 1.8 teraflops of speed

Technology is seen by CxO’s as increasingly disruptive

GLOBAL

Source: PwC, 1st Annual Global CEO Survey and 20th CEO Survey. Base: All respondents (1998=377; 2017=1,379)Q: To what extent do you think technology will change competition in your industry over the next 5 years?

20th CEO Survey

1%8%

20%

30%

59%33%

20%27%

1998 2017

No impact

Moderate impact

Significant impact

Complete reshape industry

CEOs’ predictions of the impact of technology were pretty accurate 20 years ago. Today, an even larger proportion expect their industries to be reshaped by it.

Five “Megatrends” are resonating across sectors, organisations and even Finance functions

Industry

Finance

Organisation

Volatility Innovation ResilienceChanging Business Models

Challenges and Opportunities

I. Role of Finance

II. Insight, Efficiency, Control

III. Enablers: People, Technology & Systems

Shift in global economic power

Technologicalbreakthroughs

Accelerating urbanization

Demographicshifts

Climate change & resource scarcity

#1Performance enhancer

#2Enterprise transformer

MASSIVECFO

opportunity

#3Brilliant steward

11

DATA & ANLAYTICS and TECHNOLOGY

THESILVERBULLET?

….analytics, cloud, social, robotics

13

Decision Making:The New Competitive

Frontier


Business value from data and analytics

“Companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more

profitable than their competitors” - Big Data: The Management Revolution, HBR, Andrew

McAfee and Erik Brynjolfsson, October 2012.

Of CEO’s place a high value on Data & Analytics

85%

Zettabytes per year of IP traffic by 2016

1.1

Was VC funded in pure play AI companies

$700m

Relied on data and analytics to make Big Decisions

38%

Is there a better way to combine Art & Science in Decision Making?

Science:

Analytics & Data

“We are now able to get responses back from the technologies in a fraction of the time, and over a large dataset.”

Art: Experience & Advice

“… Another part is management intuition based on market insight, feet on the street, which the data may not tell you.”

The Art & Science

of

Decision Making

ExecutiveWestern UnionPwC Big Decisions Survey

PwC Data & Analytics 16

Relative to their current capabilities, data-driven companies sense they have a high hill to climb in both speed & sophistication

Global - Current Global – Need to be in 2020

Rarely data-driven

Somewhat data-driven

Highly data-driven

Current decision making capabilities are diverse in terms of Speed & Sophistication

Respondents anticipate significant future improvement in Speed & Sophistication

Source: PwC Big Decisions Survey 2016; pwc.com/bigdecisions


Decision making processes are most effective when you consider how data & analytics can help you make faster and more sophisticated decisions

Accelerated Agility

Master the Chess Moves

Intelligence in the Moment

Cover the Basics

Low High

Sophistication

Speed

Low

Hig

h

Decision Archetypes

Data-driven decisions trump intuition

Hindsight & foresight with all available data

• Intuition based decisions – little analysis

• Descriptive reporting with internal data

• Low frequency data and model refresh

• Reporting structures link decisions to actions

Cover the Basics

Data & intuition drive decisions

Hindsight & foresight with all available data

Master the Chess Moves

• Data-driven decisions trump intuition

• Hindsight & foresight with all available data

• Slow consensus driven & analytic decisions

• Finanical metrics tied to perational metrics

Accelerated Agility

• Speedy decisions trump analysis /consensus

• Descriptive reporting with internal data

• Rapid analyse-decide-act feedback loop

• Operational metrics focused on efficiencies

Intelligence in the Moment

• Data & intuition drive decisions

• Hindsight & foresight with all available data

• Advanced analytics with feedback loop

• Adaptive & linked financial & operational metrics

Specifying the Value and Differentiation of the Opportunities


The challenge for Finance leaders is to look beyond the often quoted clichés of adding value to the business, business partnering and efficient operating models…


Many processes can be made more efficient, leaving more time for driving insight


Finding new ways of orchestrating better performance, offering more diverse and rewarding communication, enhancing resilience and providing a more connected and cohesive Finance offering

The evolving role of the finance function


The evolving role of the finance function

The technology revolution has moved us to a world of digital, data and devices… Finance is expected to change significantly in response to data revolution…

22

How Can Analytics Support

Decision Making?

“The goal of forecasting is not to predict the future but to tell you what you need to know to take meaningful action in the present”

– Paul Saffo, HBR, 2007


Data and Analytics help solve business challenges


Data & Analytics Scope

1. Business Value Chains

Strategy & GrowthMarketing & Customer Exp

Sales & Distribution

Products & Pricing

OperationsSupply Chain Management

Finance, Risk & Compliance

2. Data Access, Visualization & Distribution

Adhoc Data Analysis

Search & Discovery

Data Export APIReporting &Dashboards

Custom / Interactive Visualization

Website Front-end

Real-time Alerts

3. Analytical Processes

Discovery Descriptive Diagnostic Predictive Prescriptive Autonomous

7. Data Ingestion & Integration

Data Integration Data Acquisition Data Quality Data Architecture

4. Data Repositories

Relational Database Distributed Systems In-Memory NoSQL Operational Data Storage

6. Execution / Data Processing

Execution / Data processing Resource Management

5. Data Sources

Public Data Client Data 3rd Party

8. Infrastructure

Servers Storage Build / Deploy Operations On Premise Co-LocationManaged Services

Cloud

9. Governance & Security

Identity & Access Management

Data Governance

Security & Monitoring

Infrastructure Services

Data Storage Services

Analytics Services

Business Services

Data Sources & Processing

Services


Descriptive Analytics


Predictive Analytics

Customer Insights Platform


Optimization and Prescriptive Analytics

Alternative Fuel Vehicle AdoptionSimulation Model


There are a number products across the areas of data management, analytics and visualisation.

The Gartner Magic Quadrant

29

Analytics @ Work


Unlocking the value in data and analytics starts from business outcomes…

Discovery Insights Actions Outcomes

Insight Driven Organization

Find value in your internal and external data

Apply new techniques on existing and new data for

tailored, value creating insights

Make decisions, deliver analytics & data quick wins

and operational capabilities

Unlock value, build talent and improve

financial, market and risk metrics


Reduction in crashes

from accident

prevention technologies

which in turn lower

claims costs

20-35%1

Reduced Loss Adjusted

Expenses through 2018

using data from car

sensors

40%2

Savings yield from Automatic ERS,

ACN, and hence FNOL initiation

5-15%1

Savings realized from telematics data

enabled technologies to reduce fraud and

theft

30-80%1

Driver Needs

Using Google Maps to

navigate their trip using the fastest route

Sources: 1. Cisco IBSG estimates, 2011; 2. PwC research and analysis, Celent forecast

Telematics digitizes ‘ride behavior’ providing insurers with a new way to engage customers, while also supporting the claims process

Insurance


The Insurer’s capability to assess Risk or to access a Loss will depend on two key factors:

• Ability to capture complete and accurate data in any required format

• Ability to perform a deep analysis of available multi-format data

Data Capture

Companies such as Airware provide all the technology required to get to the “Risk” location and use onboard sensors to capture all relevant data (Video and images, temperature, humidity …)

Data Analysis

Companies such as Airphrame are specializing in 2D and 3D imaging, providing capabilities relevant Risk and Loss assessment

Source: CalIT2, USCD

Drone technology with onboard cameras and sensors to capture Risk information, with impact on efficiency and effectiveness (specially around big structures)

Ability to convert captured data into a digital representation of the Risk for deep analysis and accurate assessment (even comparing pre and post CAT images)

Real time remote access to Risk allows best talent to be directly involved

Aerial and satellite images digitize physical assets enabling new ways of risk and loss assessments for insurers

Insurance

PwC

Effective segmentation has been used to massively reduce money laundering

activity

E.g. Proposed segmentation model

defines 13 new segments instead of the 21 segments used in the

current model

Analytics @ Work

Banking

0.07

0.12

0.10

0.05

0.06

0.03

0.06

0.20

1.39

1.30

1.42

0.10

0.07

0 1 1 2

Advance: High

Advance: Medium

Advance: Low

Commercial: High

Commercial: Medium

Commercial:…

Commercial: Low

Mass Market: High

Mass Market:…

Mass Market: Medium

Mass Market: Low

Premier: High

Premier: Low

Millions

< 0.010.28

< 0.01< 0.01< 0.010.080.03

4.320.12

000< 0.01< 0.01< 0.01< 0.010.18

< 0.01< 0.01< 0.01< 0.01

0 2 4

Advance: Sole ProprietaryAdvance: Individuals

Advance: OrganizationsCommercial Banking: Sole…

Commercial Banking: IndividualsCommercial Banking: Organizations

Mass Market: Sole ProprietaryMass Market: Individuals

Mass Market: OrganizationsTrusts: Sole Proprietary

Trusts: IndividualsTrusts: Organizations

Global Banking: Sole ProprietaryGlobal Banking: Individuals

Global Banking: OrganizationsPremier: Sole Proprietary

Premier: IndividualsPremier: Organizations

Private Banking: Sole ProprietaryPrivate Banking: Individuals

Private Banking: Organizations

Millions

Current Segments Proposed Segments

Mass Market Clustering

Cluster # of Accounts % of Accounts Mean Median 90th Percentile

CYD1 Xxx 33% $ 3,245 $ 3,131 $ 6,500

CYD2 Xxx 30% $ 10,707 $ 10,383 $ 15,288

CYD3 Xxx 32% $ 62,504 $ 30,724 $ 96,118

CYD4 Xxx 5% $ 159,178 $ 67,080 $ 277,241

Nature of Business DistributionsProfessional

Services

Heat Map of key Cluster s

D15 D27 D35 D85 D87

PwC 34

Man-Machine combination to create

unique advantage

Large teams of people watched and tagged

movies and shows (36 page manual)

Created 76,897 micro-genres

35

Internal AuditAnalytics @ Work

36

Applying data & analytics to Internal Audit

Internal Audit organizations that are transforming in pace with the business are more advanced in their use of data analytics, including its wider application:

RiskIdentification

Audit Planning

ContinuousAuditing

ContinuousMonitoring

82% of Chief Account Executives reported

they use analytics in some audits for audit execution but

only 48% use analytics for making

scoping decisions and only 43% use

analytics to complete their risk assessments.

37

Applying data & analytics to Internal Audit

When fully integrated, the data analytics initiative is not a program; rather, the techniques are fully embedded into all elements of the audit lifecycle.

Deliver solutions.. not problems

Risk AssessmentAudit Planning Fieldwork

and Execution Reporting

• Project level risk assessment• Audit scoping and planning• Risk attribute sampling

• Enterprise risk management• Annual risk assessment• Risk monitoring• Business unit or site level profiling

• Audit reports• Executive and AC reports• Issue quantifications• Compliance metrics

Embedded and sustainable analytics

• Multi-unit auditing• Data-driven testing• 100% coverage• Process / control validation (end to end testing)• Root cause identification

38

Internal Audit Analytics in Action

Profiling Sampling Testing Monitoring

Profiling: Analyze transactional activity and develop a baseline understanding which can be used to identify outliers.

“Intelligent” sample selection: Identify specific anomalous, unusual, or higher-risk transactions for follow-up and investigation.

Exception testing: Identify transactions that specifically violate company policies or represent internal control breakdowns.

Analytic

Monitoring: Operationalizing analytics created in prior phases and test risk areas in a sustainable and repeatable way.

Understand data completeness and quality Set standard benchmark to evaluate totals.

Test 100% of all transactions that exceed Company's policy limits or activity thresholds.

Create continuous monitoring solution around a specific use case providing access to Audit and Compliance as well as business stakeholders.

Outcome

39

Intelligent AutomationAnalytics @ Work

40

Why the demand for Intelligent Automation?

Market View

90% cheaper than the average wage of a full time staff member in

Australia

50% cheaper than the average wage of a full

time staff member in Philippines

34% cheaper than the average wage of a full time staff member in

India

• The global RPA market size alone is expected to reach US$5 billion by 2020 growing at a CAGR of 61.3%

• RPA comes with the promise of up to 45% automation of work activities and US $2 trillion savings in global workforce cost

AI and Automation will contribute to

$15.7 trillion (14%) of global economy by 2030

The impact in ASEAN countries could be up to 44% of the workforce

~40% ~24%Insurance Asset Management

& Wealth

Different industries vary widely for the opportunity

~50%Shared Services

What our clients are saying

Digital Agenda

Scale and Efficiency Legacy Platforms

New Ways of Working

We have a heavy reliance on a complicated landscape of legacy systems and process

that does not support our plan

We must meet our demand to improve efficiency and operational excellence to support scale and speed

We must rapidly introduce new products, services, with a focus on fast experience that demands front-

to-back STP with automation

Disruption to traditional operating models – we must work more with more agility

and intelligently

* STP = Straight through processing

41

Robotic Process Automation (RPA – known as ‘automation or robotics’) is a category of software configured “robots” that sit on top of existing systems to perform tasks normally performed by a human

Creates a virtual digital workforce able to mimic human execution

Undertakes structured, repeatable and computer based tasks

Interacts non-invasively with existing applications and systems

Captures all details of the process and stores it for potential auditing

Uses workflow enabled interaction with people.

@

@

What RPA is What RPA is NOT

Something that can entirely replace humans

Something that replicates human cognitive functions… yet

Purely just another cost play

A humanoid robot

42

What is Intelligent Automation? – digital technologies across an automation spectrum combining to create a ‘digital workforce’

Intelligent Automation is the introduction and layering of digital technologies across an automation spectrum that can replace, augment and enhance a traditional human workforce – it has profound transformational benefits and organisational implications

AugmentedIntelligence

Leveraging Cognitive/AI capabilities to augment core

human-driven processes such as data manipulation, exception management and continuous

STP improvement

+machine learning

Conversational Intelligence

Virtual Agent / Chat Bot technologies for internal employee and external B2B/B2C interactions, enquiries and support

+natural language processing

Unlock Value &Real-time Decisions

DATA-DRIVENINTELLIGENTENTERPRISE

Unlocking the value of data to provide deeper insights enabling real-time decision making

throughout the value chain

+deep learning

Robotic Process Automation

Driving efficiencies across the value chain via RPA for

repetitive manual activities �critical to create operational data and capacity that

traditionally has not existed

ENTERPRISE TODAY

24x7 Workforce & Capacity Release

Scalability; Reduced Cycle

Times

Employee Value Proposition

Service Quality Improvement

B2C/B2B Experience and Time to Market

Compliance & Control

Rapid, Non-Invasive

Implementation

43

Finance & Accounting: Opportunities for Intelligent Automation

F&A : Value Chain View

Transactional Data Management

Accounting & Reconciliation

Consolidation & Ledger Close

Data Capture & Processing

Reporting & Analytics

Top Picks for RPA

• Account Receivable/ Order to Cash cycle – Rule based repetitive steps including Customer master data maintenance, Order management, Manual billing, Cash application

• Record to Report cycle – Rule based repetitive steps including Master data management, Fixed assets, Bank reconciliations, Intercompany, Consolidations, Balance Sheet Reconciliation

• Bridging the gap among non-integrated systems – Number and extent of opportunities depends on enterprise architecture

Low HighMed Virtual Agent/ AI/ML Opportunity

Legend:

Top Picks for Cognitive

• Tax classification of Capex – Application of machine learning to determine appropriate tax classification of capital expenditure (PwC has an offering in this space - Swift.CAPEX)

• Order to Cash Customer Servicing / Account Payable Helpdesk – Virtual Agents and Email Bots to support call centre/chats as well as external emails in natural language

• Exception Handling for RPA use cases – Pattern identification aided by machine learning to resolve exceptions

Systems in F&A Ecosystem

F&A: Key capabilities & processes

Operational Accounting

Master Data Management

Event Data Capture

Manual Journal Entries

Planning & Forecasting

Data AnalyticsManagement Reporting

Financial Reporting

Internal Reporting

Data Transfer

Data Processing/ Audit

Journal Entry Preparation

Journal Entry Processing

Inter-company Transactions

Allocation & Adjustments

Consolidations

Reconciliations Legal Entity Closing

Discrepancy Management

Audits and Approvals

Managerial PlanningFixed Asset Accounting

Billing & Collection Account Receivables

Invoice Matching

Invoice Receipt and Data Entry

Helpdesk & Issue Resolution

T&E Claims Processing

Ledger Closing

Tax Classification of Capital Expenditure

Lines of Business(Data

Sources)

DW/BIReporting Engine

Document Mgmt System

General Ledger System

ERP Solution(s) AP/AR etc.)

Data transfer, integration, validation, ETL

General Accounting

44

Although a digital workforce provides great benefits, it also fundamentally changes the operating paradigm and associated risk profile – Why?

Knowledge Management… what happens when staff leave

their roles?

Up/Down Stream…Will there be an impact

to other processes

Organisational Change…How will this impact my team?

Training…How do we operate the Robots and this new way of working?

Systems Change Management…

Who makes sure the Robots keep working with the systems change?

Business Continuity…how do I continue running my business if the Robots stop?

RPA and Cognitive Science/AI efforts often fail because they do not address specific operating model, governance and change management requirements

Oversight…Who is making sure the Robots are working?BPO/3rd Parties…

Will this impact our contracts/KPIs? Operational Risk…

Will this change my operational risk profile?

CyberSecurity…Are these Robots safe?

A digital workforce fundamentally changes the nature of your operations and touches every part of the organisation –Governance and change management are essential to the success over the long term

45

PwC has identified 100 +criteria in 30 risk areas across 5 risk categories for management’s consideration during an IA implementation

Functional Risk

� Business Process Controls� Business Process Redesign� Functional Requirements� Performance� Methodology

Operational Risk

� Regulatory Compliance� Governance� Operating Effectiveness� Benefits Management � Business Continuity� Disaster Recovery� Control Centre Governance� Monitoring / Quality Control � Audit

Program Management Risk

� Planning� Scope

� Integration Oversight� Decision Making

Executive Risk

� Commitment� Sponsorship� Business Objectives� Organisation Priority� IT Asset Management

Execution Risk

� Methodology� Configuration� Testing� Operational Readiness� Deployment� Legacy System Changes� Information Security� Data and Privacy Security

Leverage PwC’s illustrative Automation controls library

to design controls to effectively mitigate the risk

Applying PwC’s Risk and Controls Framework

Understand and identify inherit Automation risk

using PwC’s Risk Framework

IPA Risk Framework

data & analytics … and the finance function part 1 of 2 · profitable than their...

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