Transcript Advanced Origination Framework: Effective Credit Evaluation

Advanced Origination Framework:
Effective Credit Evaluation
Paul Waterhouse
Head at The Analytical Cooperative
Why Use an Advanced Framework?
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Scarce (expensive) capital or funding
Low (zero) growth
Disparate work-flow systems
Process bottle-necks leading to lost business, late identification of
manifesting risk and inefficient employment of capital / funding
• Legacy systems from mergers / acquisitions
In order to achieve competitive advantage, financial institutions need
unified, effective frameworks that balance the varying functions carried
out and enhance the bottom-line through intelligent, codified
interaction
Why Use an Advanced Framework?
• Integrated and optimised origination process
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Data requirements optimised
Approval process optimised
Fast approval where warranted, appropriate extra scrutiny when needed.
Bottle necks eliminated
Capital employed effectively
• Proactive origination support from credit
– Identifies origination opportunities versus peers
– Existing customer base continuously profiled & prioritised for up-selling &
cross-selling opportunities
• Proactive origination support from ERM
– Identifies opportunities from in-house diversification, risk mitigation, funding
profile
– Capital profile identifies opportunities for increased volume
Pre Credit Crisis
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Capital / Funding plentiful and inexpensive
Benign lending (& credit) environment
Strong economic growth
Disparate origination & risk management
Origination
Credit Dept.
Portfolio Risk
E.R.M
Focus: origination process optimisation
independently of wider risk management
Immediately After the Crisis
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Capital / Funding scarce and expensive
Harsh lending (& credit) environment
Low / Zero economic growth
Constrained origination
E.R.M
Portfolio Risk
Credit Dept.
Origination
Focus: expand business with existing
“good” obligors, reduce other business
Now (and the Future)
Credit led opportunities
to assist origination:
• Countries
• Sectors
• Groups
• Obligors
• Products
Origination
Credit Dept.
Enterprise-wide risk profile
informs origination strategy …
dynamically – updates lending
rates, identifies target risk
areas for origination
E.R.M.
Identifies scope
for differentiation
Streamlined, effective credit process supports swift origination and strong ERM
Keys for success:
• Integrated framework
• Origination, credit & ERM operate together
• Effective processes
• Robust credit assessment
Now (and the Future)
Why Credit is Important
Notional loan interest rate needs to reflect:
– Economic Allocated Capital = PD* x LGD* x EAD*
(i.e. stressed to the lenders desired risk tolerance)
– Compensation for placing that capital at risk
– Adjustment for diversification / concentration
– Costs of origination, surveillance, administration
– Margin for profit
– Opportunity cost
Optimisation of these components is essential during period
of low (zero) growth and scarce (expensive) capital & funding.
Credit Approval – Best Practice
• Align time / resources spent on credit assessment
with extent of risk to lender (process optimisation)
• Automate where appropriate to do so
• Employ “checks & balances” to identify cases
requiring greater scrutiny
• Verify applicability of credit model used for obligor
being assessed
Credit Approval – Best Practice
Step 1: Check Loan Profile against Model Scope
• This involves contrasting the borrower profile with the inherent model scope
• Effectively to ensure how well the model may perform on this borrower
• Critical for models trained mainly on past data
• Still important for expert developed models
• Checks include:
• Product type: consistent with data used for model development?
• Geography / Sector: consistent with data used for model development?
• Borrower profile: Size (revenue, assets, employees); Duration in business;
Nature of activity
• Financials – are all exhibited financial ratios within ranges consistent with
those used for model development?
Strong Fit = less additional scrutiny and vice-versa
Credit Approval – Best Practice
Step 2: Check Exposure Profile
• This involves considering (potential) exposure at various levels:
• The facility itself – maximum EAD, expected EAD
• The obligor
• The economic group of the obligor
• The sector
• The country
• The region
Cost versus Benefit
The cost (expected additional loss) of being slightly wrong in
the credit assessment process (e.g. bb+ in lieu of bb-) needs
to be contrasted with the additional cost (extra credit
expenses) of achieving a more accurate credit outcome
Low exposure implies simple assessment approach
Credit Approval – Best Practice
Step 3: Checks & Balances
• While the model / methodology outcome should be the CORE opinion automated
scrutiny of checks & balances dictate when additional scrutiny is warranted:
• Checks & balances:
• Outcome versus third-party FICO score, Agency Rating
• Outcome versus other in-house models
• Outcome versus other indicators (equity spreads, bond spreads, CDS)
• Outcome versus median / anchor for sector / product
• Presence of outlier values in individual financial ratios
• Outlier non financial values in data
Presence of conflicting opinions does NOT mean the core
model / methodology is wrong but does mean further scrutiny
is warranted before finalising.
Credit Approval – Best Practice
Model /
Methodology
Exposure
Loan Profile
Checks &
Balances
Automation
Portfolio
Simple
Low
Normal
Pass All
Fully
75-85%
Intermediate
Moderate
Fringes
Pass Most /
Close on
Others
Fully
10-25%
Advanced
High
Different
Significant
fails
Mostly
1-10%
Specialised
High
Unique
Critical Fails
Limited
0-5%
Focus on optimising time spent – Risk Filter Approach
Credit Process – Best Practice
Example: Simple Model – Financial Only Scorecard
Financial Scoring Factors
Sales (SR)
Assets (SR)
Net Worth (SR)
Coverage Ratio (x)
ROA (Return on Assets (%))
ROS (Return on Sales (%))
Leverage (%)
Sub Total
Value
Score
Sector Weight
1,108,357
961,151
327,392
1.0015
0.1693%
0.1468%
65.94%
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13
16
15
15
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11
22%
1%
10%
27%
17%
11%
12%
15.7
100%
Credit Process – Best Practice
Advanced Methodology
Financials
Industry
Liquidity
Management
Data Quality
Credit
Score
Shareholder
or Group
Support
Checks &
Balances
Outliers
Behavioural
Minimum
Lending Rules
Credit Process – Best Practice
Best practice also involves setting the foundation
for surveillance:
• Triggers / tolerances before score changes
– Facilitating reduced review process
– Facilitating prioritisation of review
• Risk susceptibility clustering to prioritise
review following significant adverse events
Automation - Difficulties
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Availability of relevant data
Consistency of data
Quality of data
Interpretation of data
Being prospective
Objectivity of non financial data
Data volume to collect
Group / shareholder support
Paul Waterhouse
Head at The Analytical Cooperative
[email protected]
+44 7905 089 896