Driving productivity gains by aligning risk management and IT

November 2012Sohail Farooq, Sr. Director and ME Consulting Practice Leader, Moody’s Analytics

1. A business case for change

2. Operating model – design considerations

3. Data & MIS considerations

4. Technology

5. Investment and associated returns

Contents

A business case for change

Pressure on the credit process to evolve remains relentless

The credit process re-alignment is driven by a number of internal and external drivers:

– External drivers

Regulatory: Basels II & III

Evolution of market conditions: focus on core business in post write-off period and cost savings

– Internal drivers

Market conditions – focus on cost drivers, i.e. loan losses, productivity, etc.

IT consolidation (i.e. target architecture): Consolidation of credit risk MIS and reporting systems

Introduction of new performance metrics (e.g. Economic Profit, RAROC)

Integration of objective risk measurement models and other credit decision tools

Redesign of credit policy (e.g. limit setting)

Workflow automation, e.g. auto accept and auto reject

Strategic and organisational changes (e.g. separation of sales and credit, centralisation)

The credit process re-alignment can potentially unlock significant cost savings

Pressure on the credit process to evolve remains relentless

Often credit process upgrades are rendered incomplete due to organization or structural complexities

Streamlined Credit Approval Process

RMs vs. credit officers continue to retain full responsibility for credit assessment

The rating tool decisions support not fully recognized

Primary basis for lending remains unchanged as security value

Written credit application replicates analysis covered by rating tool

Classic business process upgrade illustration

Lack of integration with value-adds

Economic framework for discussing credit decisions based on risk/return, not risk control

Explicit risk/return approach to be aligned with shareholders` view of how credit decisions should be made

Value Destroying Business Units

Value Creating Business Units

% of Bankwide Economic Capital

RAROC

BU 1

BU 4

BU 2

BU 3

BU 5

BU 8 BU 10BU 6 BU 7

BU 9 Hurdle Rate= 10%

RAROC Skew By Business Unit

-20%

0%

20%

40%

60%

80%

100%

120%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Before ratings

17%Administrative

15%Client

Servicing

50%Credit

Activities

18%Sales and Marketing

27%Credit

Activities

40%Sales and Marketing

15%Administrative

18%Client

Servicing

After ratings

Often credit process upgrades are rendered incomplete due to organization or structural complexities

Streamlined Credit Approval Process

RMs vs. credit officers continue to retain full responsibility for credit assessment

The rating tool decisions support not fully recognized

Primary basis for lending remains unchanged as security value

Written credit application replicates analysis covered by rating tool

Classic business process upgrade illustration

Lack of integration with value-adds

Economic framework for discussing credit decisions based on risk/return, not risk control

Explicit risk/return approach to be aligned with shareholders` view of how credit decisions should be made

Value Destroying Business Units

Value Creating Business Units

% of Bankwide Economic Capital

RAROC

BU 1

BU 4

BU 2

BU 3

BU 5

BU 8 BU 10BU 6 BU 7

BU 9 Hurdle Rate= 10%

RAROC Skew By Business Unit

Before ratings

17%Administrative

15%Client

Servicing

50%Credit

Activities

18%Sales and Marketing

After ratings

Often credit process upgrades are rendered incomplete due to organization or structural complexities

Perceived lack of clarity is often driven by internal constraints

Unclear Strategy

Revenue Culture

Strategy not widely understood

Little/no obvious competitive advantage in some actively pursued businesses

Some businesses with bad strategic f it

$1 of lending revenue equals $1 of non-lending revenue

Poor cross-sell due to ‘patchy’ non-lending products and limited customer f ranchise

Structural Factor Front-Office Behaviour

Excessive focus on f ront-end process (analysis/approval) at the expense of monitoring, intensive care and workout and recovery

Revenue targets (to determine bonus)

No capital charge

Organisational Misalignment

Multiple legal entities (group companies)

Poorly def ined roles & responsibilities

Committee culture: Lack focus on risks but focus on adjudication

Focus on Lending

Lack of competitive advantage results in ‘revenue chasing’ / riskier transactions

‘Revenue Chasing’

Outcome

‘S lipping Through the Cracks’

‘No’ does not mean ‘NO!’

Lack of clarity allows some transactions to ‘slip through the cracks’

Weak Origination Discipline

Heavy/Costly ‘Front-End’ Credit P rocess

Limits not enforced, because the rationale for having them is not shared

Lack of focus on identifying realistically achievable cross-sell revenues to pay for lending subsidy

Lack of consideration of economic cost of credit at origination

Incremental credit requests require a disproportionately heavy credit process

‘Revenue chasing’ results in multiple submissions of the same application

Lack of Focus on Middle/Back End

Perceived lack of clarity is often driven by internal constraints

Wholesale Revenue/RWAs Annual Average

Loan Losses/RWAs Annual Average

Bank AA

Bank BSBank BP

Bank Y

Bank CS

Bank D

Bank B

Bank HC

Bank X

Bank J

Bank SG

Bank US

Bank L

Bank S

Bank CBBank BB

0%

2%

4%

6%

8%

10%

12%

14%

16%

0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4%

Source: Examples

Strategy ARisk: -80%Revenue: +80%

Strategy BRisk: -60%Revenue: +130%

Strategy CRisk: -15%Revenue: +190%

A

B

C

Bank X options

Develop a common languageDevelop a common language

Operating model – design considerations

Traditional functional organisation design can undermine value optimisation

Finance Adversarial relationship

No party with holistic value responsibility

Over-emphasis on intermediate (non-value) metrics (e.g. loss rates, response rates)

‘Silo mentality’

ProblemsTraditional model

Marketing

Credit Operations

Organisational redesign remains the key to improving credit processing

Application Process Ad hoc Paper-based application form(s)

Standardized application form, typed into electronic file and spreader

Integrated form and rating model, simple rules to screen applicants

Green/yellow/red tool drives fast-track/normal/autoreject decisions

Credit Analysis “Character Lending”

Simple analysis and loan grading by Account officer

Analysis and rating by account officer, reviewed by credit

Joint AO/Credit analysis, supported by a rating tool

Rating tool assigns risk grade, simple risk vs. return

Risk vs. return in a portolio context

Credit Approval Account Officer, signature from Branch Manager

Multiple committees, tilted to commercial

Single committee, tilted to credit

‘Four Eyes’ (AO + Credit) committee by exception

Fast-track approval based on hurdle rate

Approval and pricing subject to portfolio management policies

Formal separation or Origination and Portfolio Group, transfer price based on NPV model

Monitoring and re-affirmation

Annual review for all accounts

Smallest, highest grade borrowers by exception

Simple, early warning tool in addition to annual review process

Robust early warning tool replaces annual reviews for most loans

Portfolio-driven investigations of credit files

Managed as a trading book

Lagging Market Standard Leading

Organizational models for credit origination and adjudication

Many different relationships exist between recoveries and the business units which originate the assets

Undifferentiated

Specialist within the Business

Units

Specialist Credit Offi cer

Standalone Business Unit

Asset OwnerAsset

Servicer

Business Unit owns assets

Account Of f icers manage problem accounts

Undif ferentiated P&L

Business Unit owns assets

Specialist Recovery Unit reports to Head of Business Unit

Undif ferentiated P&L

Business Unit owns assets

Specialist Recovery Unit reports to Credit Department

Recovery Unit acts as cost centre

Business Unit owns assets but responsibility is blurred

Standalone recovery Unit (reports to Board)

Constructed P&L based on costs and interest income

Asset ownership is transferred to Recoveries

Recoveries as Business Unit

Full P&L based on transfer price

Business Unit retains ownership of the assets

Recoveries acts as Business Units

Full P&L based on fees

Organisational models for recoveries

Laggard Market standard Leading practice

There is often an intermediate ‘waypoint’ in the migration toward a decision-focused organisational model

Traditional

‘Policy & analysis group’

Eventual

Finance

Classic structure

Conflicting goals

Front office Finance Front officeFront of f ice

Integrated analytical approaches

Integrated data strategy

Coordinated use of value measures

‘Membership’ from all relevant functional disciplines

Value-based structural decisions

Design of value-based operational decision tools

Single analysis team

Profit responsibility

‘Strategic dec ision support’

Transitional

Credit Operations Credit Operations Operations

Policy and analytics

Finance

Credit

Strategic decision support

‘Functional adversaries’

Data & MIS

A few key principles should be followed while designing the data and technology architecture

Data warehouse, modelling data environment and model implementation data should be on separate platforms

Trying to create a global, cross-customer data warehouse is too ambitious – design consistency is more important than a single database

Cost of data storage is much lower than value of data

Err on the side of storing more rather than less data

Processing systems:

– Flexibility to add and change product features at account level is key

– Ability to capture data from processing systems is also critical

Decisioning systems:– Good for managing workflow and

triggering actions based on events, but generally not for running value-based decision models

– Ease and flexibility of updates should be important selection criterion

Systems-spec ific princ iplesData-spec ific princ iples

Don’t let technical constraints, e.g. preference for simplicity, drive business tactic and process decisions

Don’t hand off data and technology upgrade projects to IT

Overarching princ iples

There are broadly two organisational approaches for analytics functions across products/businesses

Central analytics group supporting all businesses

Larger businesses may have ‘local’ analysts, but models and strategies need to be approved by central function

Trade-off between scale and local customization

However, customisation can be achieved by creating product/business-dedicated teams within central unit

Easier to drive technical innovation

Decisioning analytics resources housed within each business

‘Network’ or analytics steering group consisting of analytics leaders from each business

Analytics group sets standards, drives innovation and ensures knowledge transfer

Has the benefit of more integration with business, better buy-in of output and more pertinent decisioning solutions

Maintaining consistency can be a challenge if ‘network’ is weak

‘Networked’Top-Down

Technology

We see optimisation as the integration of three key elements

Operating Model & Organisational Structure

Technology

Processes & MIS

Optimising credit processing

Workflow optimization exampleTechnology drives productivity gains

Collection and recovery technology

Lifecycle of a file

Origination Cross-sell/management

Problem loanidentification

‘Turnaround’ ‘Workout’ Collections Pre-litigationrecovery

Litigation Recovery orwrite-off

Rating tools0 dayspast due

Early warning tools

90 dayspast due(official default)

Active file management

Process automation

Data based decision support

Collectionmodel

Recovery model

Litigation mgmt.

Rating tools: Stable, stratif ied predictor of default Early warning: Sensitive, behaviourally-based problem loan detector Active f ile mgmt.: Parameterized f ile classif ication and allocation Process automation: Powerdialling, automatic letter production Decision support: Outsourcing, strategy, file allocation, ‘champion vs.

challenger’ Collection model: Probability of collection Recovery model: Probability of recovery Litigation mgmt.: Automated agenda, tasklist cost management

K ey Attributes

Focus resources on the analytics and information value chain

Electronic agenda

Origination Dec ision Back-office Workout &collec tions

Recovery &litigation

DB c redit

Rating

Screening

Pricing

Share of Wallet

EarlyWarning

CollectionPredictor

IBDS

Elec tronic file

Application form

Solicitation

Documentation

Contracts

Dec isionmanagement

Valuation tool

RecoveryPredictor

Routing

Electronic sign off

Scenario testing

Marketing

Business areas

Databasemanagement

Data management

Data mining

‘Silo’ Management

Reporting (regular and ad hoc)

Single data entry

User-f riendly interface

information transfer

Automatic f ile routing

Automatic “state” categorisation

Automatic data update

Process management

Power-dialling

Skip tracing Letter production

Portfolio management

Analysis and insight

Performance measurement

Reporting

Tool improvement

Tool development

Advanced analytics

Information-based decision support

Target setting

Performance monitoring

Business intelligence

Investment and associated returns

Recurring cost savings pay off for the investment automatically

Cost – Mortgages and P. Loans* Cost savings in Risk are derived from lower requirements

of analyst time, largely due to lower number of applications that reach the analysts for sanction

– ~ -75% Required analysis hours/year: ~31,500h to ~7,800h

Cost savings in the network beyond those achieved via the integration project are thought to be small and often hard to analyze, given that the optimization project contemplates:

– The transfer of main administrative tasks from network to central back-office function

– The elimination of the duplication of work to collect data and process applications

– Estimated cost savings assume time required for ‘Risk Audit’ is the same as today’s (although this should be tested during pilot)

Comments

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

Pre-TOM Post-TOM

$ '00052,000 applications x $24 Unit Savings ~ $1,2 MM

Network

Analysts

Committees

Network

AnalystsCommitteesNetwork

Analysts

Committees

Case study

Original Value proposition – Entry approach

Category killer approach

Offering of only high interest, no fees, simple savings account with great customer service

New value proposition – Evolving focus

Once ING Direct has successfully built a large enough customer base, it starts to expand into other product areas (cross selling)

In mature markets and where ING Direct is established such as e.g., Canada, it offers a full-fledged product set comparable to a standard Retail banking offering

Business model

Business built on 4 pillars and it competes success-fully in large, technologically sophisticated markets

ING Direct does nothing that creates complexity and therefore inefficiency

Same operating platform in all countries with minimal adoptions only (e.g., for regulatory reasons)

Strong brand building to create traffic

Clients (‘000)

Funds entrusted (BN)

Rank within savings market

Canada (5/97) 1,491 12.3 7

Spain (5/99) 1,455 13 6

Australia (8/99) 1,414 11.2 6

France (5/00) 626 12.3 9

USA (9/00) 4.629 36 21

Italy (4/01) 792 14 7

Germany (8/01) 6,005 60.6 6

UK (5/03) 1099 36.3 8

Total 17,511 M 195.9 BN

No. of clients and deposit volume (estimates)

Retail deposit market share since inception

Market share

Years after launch

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

0 1 2 3 4 5 6 7

UK

Aus.

D

USA

F

E

Can

ING Direct Model

1. Cost efficiency/minimum

complexity

2. Brand mgmt./ high marketing spending

3. Focused Product mgmt.

4. Effective Customer

service

Direct Banking InnovationThe concept of direct banking as practiced by ING Direct globally

Conclusion & Parting thoughts

The market sets the price – we can only respond

If we tamper with customer pricing, attrition will be the result

I do not believe too much money is left on the table in any event

What we ‘lose’ in one area we make up in another

We cannot be seen as ‘sticklers’ on price in the market

Organizations that command better pricing simply have a better value proportion

We would like to price better but don’t have the tools and resources to do so

Strongly Disagree

Neutral Stance

Strongly Agree

1 3 5

1 3 5

1 3 5

1 3 5

1 3 5

1 3 5

1 3 5

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