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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