The document outlines a framework for consumer credit risk analytics, emphasizing the importance of customer credit scoring, approval rate determination, and effective credit limit setting. It critiques traditional methods, suggesting a shift towards using Vintage-Age Customer Value (VAV) for better insights into credit risk and overall customer valuation. The proposed approach aims to enhance decision-making processes in credit approval and limit setting by basing they on a more comprehensive analysis of customer value rather than simplistic good/bad indicators.

1. Framework for Consumer
Credit Risk Analytics
Senthil Ramanath
Head of Analytics, ACE Cash Express
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2. Credit Process
Score customer credit • Application data
worthiness • External 3rd party credit data
• Identity check denials
Approve/ Deny the • Business rule denials
customer • Score cutoffs
• Individual credit and loan
attributes such as
Calculate maximum loan • disposable income
amount • debt service amount
• collateral value/ equity
• Credit score (often not used)
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3. Common Credit Scoring Process
• Pick variables that would indicate high or low credit risk
Variables
• Typically follow the account charge-off criteria
Define a
• Or arbitrarily pick 30 or 60 days past due loans as bad loans
bad loan (usually more stringent than accounting definition)
• Perform logistic regression on the good-bad indicator
Create a • Based on this regression create a internal credit score
model
• Obtain traditional or alternative credit bureau data on the customer
Enhance and add it to the mix to get better score
the model
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4. Approval Rate determination process
Set approval score
based on desired/
historical approval
rate
Update/ Rebuild
the model
periodically as new
information is
available
Monitor the
approval rate
and tweak the
Monitor the score cutoff
impact in P&L
Add additional Based on and adjust the
strategic risk cuts economic approval score
based on conditions adjust
management intuition the score
or ad-hoc univariate
analysis
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5. Credit limit setting
Customer-Loan Ratios Customer Credit Attributes Loan Attributes Product Attributes
• Loan to Income Ratio • Income • Term of the loan • Market offered
• Loan to Collateral value Ratio • Total debt servicing burden • Pricing • Customer segment
• Total Debt to Asset Ratio • Tenure with the lending
company
• Credit score built for approve/
decline decision
Typical approach
 Typically one ratio (left box above) and one or more attributes (3 boxes on the right) will be used to
set the credit limit
 For example, a credit card company may use customer’s tenure (attribute) with them to decide on a
credit limit to income ratio. A mortgage company may use customer’s credit score to come up with
Loan-to-Value ratio.
 This framework is reasonable, but most companies use management estimates (without proper
statistical underpinning) to decide on the relationship between the variables such as customer
tenure and credit limit to income ratio.
Often neglected, because of complexity
 Often credit limit setting is considered as an art and rarely is it a statistical exercise
 These rules are often set at the inception of a product and rarely optimized later
 Credit limit setting is the foster-child in the credit risk management and does not get the attention as
the loan approve/decline scoring model
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6. Problems with traditional credit scoring and
limit setting approaches
 Credit Scoring Issues
 Wrong target
 Good-Bad indicator is not good enough
 Many credit scoring bureaus use 60 DPD as bad indicator.
 What is a better target ?
 Are all the goods same? Are the bads equally bad?
 How about partially collected loans? Where do you draw the line?
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7. Excerpt from bankinganalyticsblog.fico.com-
- explaining performance of a model
 The model was precise in identifying strategic defaulters among a 30-to-180-
day delinquent population (not yet written off).
 It found 76% of the strategic defaulters in the 30% of the population
receiving the most risky scores.
 The analytic separation is sharp—the most risky decile of borrowers are
200-times more likely to strategically default than the least risky decile of
borrowers.
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8. Start with Customer Life-time value (LTV)
 Customer life-time value estimation is not just
marketing holy grail, it is also an essential part of
credit risk management
 For instance in credit card industry, identify the first 1
year, 2 year, 4 year and 8 year values obtained per
customer in a segment.
 Based on these values customer LTV may be
extrapolated
 For analytical purposes select a vintage age customer
value (VAV) as a proxy for customer life-time value (LTV)
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9. Vintage-age customer value (VAV)
 Vintage-age customer value (VAV) can be defined as the cumulative
remaining marginal transaction value for the next x years for a given
customer, including the current transaction
 Selecting a vintage age (x number of years) for the VAV computation
has the following tradeoffs:
 Longer the age, it is better proxy for customer LTV
 Shorter the age the more recent origination can be used for analytics (this
factor usually dominates the tradeoff decision)
 Choose a vintage age and then for each transaction with a customer
calculate the VAV
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10. Framework to discount for collection efforts
 One of the major issues with calculating loan or customer value is
 Should collections expense be allocated to loans for marginal value computation?
 If so, how can one allocate collections expense to a specific loan?
 It makes sense to consider collection expenses as marginal given the number and
homogeneity of the job
 The following equation packs collection expenses into a factor called collections daily
discount rate
h = number of days in collection before the debt is sold off
Cli = actual collection amount on the ith day after an account hits collection
CDD = Collections daily discount rate
CE = Collections expense (actual)
 Iterate the equation above with varying CDD values until CDD converges to a stable value
 Solve for separate CDD values for different product types
 This enables calculating the Loan Valuation using the following in the next slide
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11. Marginal Loan Valuation
MV = marginal loan value
L = loan amount
O = marginal origination expense
m = total number of payments
d = number of days in a payment period
an = probability of nth payment
Pn = nth payment amount (includes pre-payments)
MCR = marginal capital rate per installment period
PP = marginal processing expense per payment
h = number of days in collection before the debt is sold off
Ci = expected collection amount --‘i’ days after the account hits collection
CDD = Collections daily discount rate
T = net terminal value from debt/collateral sale
k = number of payments made before the account goes to collections
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12. A better framework
 Score customers based on the vintage age customer value (VAV), instead of
simply using good/bad indicator
 This customer value based measure provides much better insights and
transparent impact to the bottom-line
 Instead of saying:
 The model found 76% of the strategic defaulters in the 30% of the population receiving
the most risky scores.
 Now, we could say:
 The model catches $43 MM of the strategic defaulters, while adversely getting rid of
$125 MM of good originations ($13 MM in projected customer value). Net benefit is $30
MM/year.
 In addition this framework can be extended for optimizing the credit limit
as well.
 This framework eliminates the need for numerous extra steps needed to
quantify the impact of an risk-cut or expansion.
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