Consumer Credit Risk

Credit Risk Scoring
Fahad ZafarMBA (Bradford) / BSc (Middlesex)
Mayfair Business Consultants

Alan Greenspan, May 1996
“… We should not forget that the basic economic function of these regulated entities (banks) is to take risk.
If we minimise risk taking in order to reduce failure rates to zero, we will, by definition, have eliminated the purpose of the banking system.”

Why Credit…?
Buying a house is the single biggest purchase most consumers will ever make.

You’ve found your dream house.

The neighborhood is perfect.
The schools are great.
The kitchen has everything you want.

BUT, You don’t have Hard Cash…

Journey to a Credit Economy
In 1999, American consumers charged about $1.2 trillion on their general-purpose credit cards.
By 2003, that number had grown by about a third—to more than $1.5 trillion.

Some Facts!
Most people pay their bills on time.
Fewer than 40% of consumers have ever been reported as 30 or more days late on a payment,
And only 20% have ever been 60 or more days past due.
(Source: FICO)

Pakistan
Pakistan's GDP has grown every year since a 1951 recession.
The economy proved to be unexpectedly resilient in the face of multiple adverse events concentrated into a four-year period:
The Asian Financial Crisis.
Economic Sanctions.
Global Recessions.
Severe rioting in the port city of Karachi.
A severe Drought.
Military tensions with India (Approx 1 million troops on the border).
The 2005 Pakistan Earthquake.
Post 9/11 Military Action in neighboring Afghanistan.
Despite these adverse events, economy kept growing, and economic growth.
Economy is expected to be back on track after the resolution of Judges and the FATA Terrorism issues by the new PPP government.

Business Cycle: Pakistan
Output (Real GDP)
We are now at this point in the cycle
Peak
Recovery
Expansion
Recession
Recession
Trough
Recovery
Trough
Time

Growth of Consumer Financing
Jan 2008 Rs 371.2 Billion
Jan 2007 Rs 328.8 Billion
Growth: 12.9 %
(Source: SBP).

Consumer Finance in Pakistan
Following the aggressive credit growth during the past few years, the credit quality of the banking system has started showing some concerns.

Increase in warrants of NPLs drew attention of the risk managers of the banks since it can affect the future profitability of the banking system, the review says and adds that future trends in NPLs would largely shape the profitability of the banking system.
Consumer Finance in Pakistan

Room for Growth
Analysts believe there is still room for consumer financing to grow.
“Inherent potential for consumer financing still remains strong, and with more players in the consumer financing business, the 2nd phase of consumer financing growth is likely to be faster than the initial boom observed from 2004 to 2006”.(Analyst at AKD Securities).

How Pakistan differs from West?

Customers do not have long credit history.
Low level of average income.
Repayment behavior differs from region to region.

Quote
Banks’ vigilant stance can curtail non-performing loans and lead to higher quality.

Les Holladay’s 5% rule
5 % take precautionary steps
95 % do not

Pointer
Interest rates are not the same for everyone, but instead can be based on risk-based pricing, a form of price discrimination based on the different expected risks of different borrowers, as set out in their credit rating.

Quote
Never make predictions, especially about the future.
(Casey Stengel)

Key Success Factors in Managing Retail Risks

What is Credit Scoring?

A statistical means of providing a quantifiable risk factor for a given customer or applicant.
Credit scoring is a process whereby information provided is converted into numbers that are added together to arrive at a score. (“Scorecard”)
The objective is to forecast future performance from past behaviour.

History of Credit Scoring

Credit scoring developed by Fair & Isaac in early 60s.
Widespread acceptance in US in early 80s & UK early 90s.
FICO scores make 75% of US Mortgage loan decisions.
Behavioural scoring accepted as more predictive than application scoring.

Uses in different area’s

Decision Models are used in many areas of industries:

Banking and Finance.
Insurance.
Retail.
Telecommunications.

Challenges in setting out Credit Policies
What is the target rate?

Multiple management goals.
Simultaneous focus on:

Market Share.
Portfolio Growth.
Earnings.
Quality of New Loans.
Quality of Collections.

Scoring Concept
Uses statistical techniques to identify and rank-order the desirability of potential customers.
Desirability is defined beforehand e.g.

Probability.
Risk.
Probability of response.
Willingness to renew.
Willingness to repay if delinquent.

Scoring Concept (cont)
Some systems identify:

Customers with high potential for bankruptcy.
Accounts in Collection for either no action or for special follow-up.
Customers for line increases.
Customers for line reductions.
Customer for early cancellation.

Understanding Customer Scores
Understanding customers' credit scores is essential for banks, and there is much to be gained by conducting credit scoring activities in-house, including:

Consistency,
Accuracy,
Faster integration of new models,
Privacy.

Types of Scoring
Application Scoring
Behavioural Scoring

Application Scoring
Credit
Decision

A statistical means of assessing risk at the point of application for credit.
The application is scored once.
Application scoring is used for:
Credit risk determination.
Loan amount approval.
Limit setting.

Usage of Application Scoring

Objective risk evaluation.
Cost effective processing.
Statistical control of the portfolios.
Controlled experimentation.

Behavioural Scoring
Debit $1344. 12
Debit $234. 01
Debit $987.56
Debit $6543.22
Debit $32423.11
Total $2556.00
Debit $1344. 12
Debit $234. 01
Debit $987.56
Debit $6543.22
Debit $32423.11
Total $2556.00
Debit $1344. 12
Debit $234. 01
Debit $987.56
Debit $6543.22
Debit $32423.11
Total $2556.00
Risk
Grading

A statistical means of assessing risk for existing customers through internal behavioural data.
Customers/accounts scored repeatedly
Behaviour scoring is used for:
Authorisations
Limit increase/overdraft applications
Renewals/reviews
Collection strategies

Usage of Behavioral Scoring

Control over account usage.
Better authorization.

Other types of Scoring

Attrition
Authorisations
Recovery
Response
Profitability
Customer

Scorecard Modeling
Create Business Plan.
Create Project Plan
Composition of Credit Score
Good or Bad
Segmentation

Create Business Plan
Scorecard development requires proper planning before any analytical work can start. It includes:

Identifying the reason or objective for the project.
Identifying the key participants in the development and implementation of the scorecards.
Assigning tasks to these individuals so that everyone is aware of what is required from them.

Organizational objectives & score card role
The first step is to identify and prioritize organizational objectives for the project.
E.g.

Reduction in bad debt/ bankruptcy/ claims/ fraud.
Increased profitability.
Increased operational efficiency.
Cost saving or faster turnaround through automation of adjudication using scorecards.

Internal vs External Scorecard Development
External
Internal
What do you think?

Role of Credit Bureaus
Company that collects information from various sources and provides consumer credit information on individual consumers for a variety of uses.

Western & Pakistani Bureau’s
Western
Pakistani

eCIB
Data Check
Credit Chex
NADRA (VeriSys)

Experian.
Equifax.
Trans Union.

Example of Western Report

Example of Pakistani Report

Create Project Plan
Identify Project Risks
Identify Project Team

Identify Project Risks
There are several risks associated with scorecard development, such as:

Non-availability of data or insufficient data.
Poor quality of data.
Delays/ difficulties in accessing data.
Changes in organizational directives.
Other legal or operational issues.

Identify Project Team
A list of project team members should identify:

Roles and responsibilities,
Executive sponsors and
Members whose signoffs are required

for successful completion of various development stages.

Composition of Credit Score
Evaluating the Credit Applicant

Application Scorecard Construction Flow Chart
Outsourcing

External Data Source
Scorecard Vendor

Data Integrity

Product Identification
File Data Availability
Sampling
Data Extraction/Cost

Generic Scorecard
Validation
Statistical Analysis

Characteristic Analysis
Multivariate model build
Reject Inference

Set cut-off Score
Implementation
Customised Scorecard
Scorecard Monitoring

Interrelationships

Scorecard Format
Predictive models are also developed in

SAS
C Language.

BUT, Scorecard is preferred because of following reasons:

Easy to interpret.
Easy to incorporate any regulatory requirement.
Easy to diagnose and monitor.

Good or Bad

A scoring system classifies an applicant in a particular “Good/Bad odds” Group.
If the applicant belongs to a
200 to 1 group,

Safe and Profitable.

4 to 1 risk group,

Unacceptable Risk.

There is a “cut-off” point where it is not profitable for the bank to accept a certain Good to Bad ratio
There will be some “bads” above the cut-off level set, and some “goods” below the cut-off level set.

Good or Bad (cont)

The score will be a measure of the probability of being a Good or Bad.
If the scorecard is performing well then the average scores of ‘Bad’ are lower than the average scores of the ‘Goods’.

Segmentation
Experience Based Segmentation.
Statistically Based Segmentation.

Experience Based Segmentation
It includes idea’s generated from:

Business knowledge and experience,
Operational considerations
Industry practices.

E.g.

Marketing/ Risk Management departments detecting different applicant profiles in a specific segment.
A portfolio scored on the same scorecard with the same cutoff but with segments displaying significantly different behavior.

Experience Based Segmentation (contd)
Typical Segmentation area’s include:

Demographics.
Product Type.
Sources of Business.
Data Available.
Applicant Type.
Product Owned.

Statistically Based Segmentation
Clustering.
Decision Trees.

Clustering
It is used to identify groups that are similar to each other with respect to the input variables.

Two methods used to form clusters are:

K-means clustering.
Self Organizing Maps (SOM).

Decision Trees
Decision trees isolate segments based on performance criteria i.e. differentiate between good and bad.

Innovations in Finance
New frontiers in Lending:

Mentally Retarded
Students
Student Loan Xpress + Univ. Financial Aid Office = Loan Pushers
Housing ATMs (cash-out equity)
Sub prime Loans
Affordability Products:
No Down,
No Docs,
Teaser interest rate,
40-50 Yr terms,
Interest only,
Liar Loans,
NINJA loans.

Securitized and sold to funds

Implications
Mortgage Security Market = $6.5T, bigger than Treasury Market
In 2001 Sub primes = 5% of Market; 13% in 2003; 2006 = 35%
increased from $120B in 2001 to $600B in 2006;
In 2000 Average Sub prime Loan = 48% of property value;
2006 =80%
In 2001 Liar Loans =25% of Sub primes; 2006 =40%
More than half of sub prime borrowers took ARMs
In 2005 the majority of mortgages to African Americans,
and 40% to Hispanics were sub prime.

Innovation
Increased Availability of Credit
Increased Price of Assets
Can/ Must take on more debt.
RESULT

LTCM & Dot.Com Bust
Bailout, Low Interest Rate
Implicit Twin Promise

No surprises,
Big Govt protection.

RESULT

Sub Prime Lending
Is offered at a rate higher than A-paper loans due to the perceived increased risk.
Encompasses a variety of credit instruments, including sub prime mortgages, sub prime car loans and sub prime credit cards.
Is risky for both lenders & borrowers due to the combination of:

High interest rates,
Frequently poor credit histories,
Potentially adverse financial situations that are sometimes associated with sub prime applicants.

US Credit Crunch
Who is to blame for the current mess?
Answer: Everyone
Consumers have been borrowing irresponsibly for years.
Lenders have been doling out money recklessly.
-- NINJA mortgages and other loans
Easy money fired up demand for homes from qualified, unqualified buyers
as well as U.S. and foreign speculators.
Investors (hedge funds and investment banks), frustrated by low yields, were
demanding securities with higher returns.

US Credit Crunch (contd)
Brokerage firms and banks responded by crafting a panoply of arcane and complicated securities backed by sub prime mortgages that “promised” higher returns.
Rating agencies stamp AAA approval on bonds they didn’t understand.
Regulators ignored warnings of abuses in the mortgage lending business.
Federal Reserve kept short term rates low for too long.
Easy money not only heated up real estate activity--- but swelled household debt.
Homebuilders were constructing houses 50%more than underlying demand.

United States “Delinquencies”
% of total borrowers
1995
1997
1999
2001
2003
2005
2007
Sources: Federal Reserve Board (FRB),
American Bankers Association (ABA):
30 days overdue on credit cards, HE and auto loans:

Fannie Mae & Freddie Mac
Hit hard by the mortgage foreclosure crisis.
Investors worry that the companies will suffer losses far larger than the $11 Billion.
As housing prices decline further and foreclosures grow, the markets are worried that Fannie and Freddie themselves may default on their debt.
(CNBC Report)

Think!
Are sub prime auto lenders insulated from the
hard times being experienced by the sub prime
mortgage market--or should they fasten their
seatbelts?

Future SBP Regulation

State Bank of Pakistan is in the process of issuing a circular requiring banks to provide Credit Score’s for all customer’s by Jan 2009.

Score Card Management Report
Once final scorecard is selected, a full suite of management reports are produced.

Gains Table.
Characteristic Report.

Gains Table
Includes a distribution of
Total.
Good.
Bad.
cases by individual scores or score ranges.

Key Findings in Gains Table
Interval or Marginal Bad Rate:

The expected bad rates for each score or score range.

Cumulative Bad Rate:

The expected bad rates for all applicants above a certain score.

Expected approval rates at each score.

Implementation
Pre-implementation Validation.
Strategy Development.

Pre-Implementation Validation
System Stability Report.
Characteristic Analysis Report.

System Stability Report
Also known as

Population Stability.
Scorecard Stability Report.

Index

< 0.10

No Significant Change

0.10 – 0.25

Small Change, needs to be investigated

> 0.25

Significant Change to Applicant population

Characteristic Analysis Report

Provides information on the shifts in the distribution of scorecard characteristics.
And the impact on the scores due to that shift.
Following figure shows a shift towards younger applicant, resulting in applicants scoring about 2.63 points less than at development for age.

Characteristic Analysis Report (cont)
Similar analyses could be performed for other characteristics, including:
Characteristics that are not in the scorecard, but might have an impact on applicant quality.

E.g. If shifts in the scorecard characteristics point to a deteriorating quality of applicants, reviewing these strong non secured characteristics will help to confirm that movement.

Characteristics that are similar to the ones in the scorecard.

E.g. If “age” is in the scorecard, tracking other time related characteristics such as

Employment or address
Age of oldest trade.

Characteristic Analysis Report (cont)
Where ratios are used in the scorecard, distributions of the denominator and numerator should be tracked to explain changes in ratio itself.

E.g. If utilization has decreased, it may be due to
Either balances.

or

Credit limits.

What if Score Card Does Not Work?
If analysis show that the population has shifted significantly, the user is left with a few options:

Adjust applicant population expectations, based on new distributions.
Adjust Cut-Offs.
Other Strategies.
Based on qualitative and quantitative analyses, bade rate expectations can be changed.
E.g. if it is deemed that the population is of lower quality than expected,

Conservative measures can be taken to increase “bad rate” expectations.
Credit line amounts can be lowered at margins.

E.g. if all analyses point to a younger applicant with higher levels of delinquency and less stability, the expected performance is obvious.

What if Score Card Does Not Work? (cont)

E.g. Cases where Credit Line Utilization has gone down denote lowered risk. This could be due to:

Decline in balances, Or
Increase in available credit limits due to competitive pressure among banks.

E.g. Where industry competition results in overall credit line increases, which artificially decreases utilization, fewer points can be assigned to people with lower utilization, to reflect a higher risk than what is suggested by the scorecard.
E.g. Bank can split the portfolio into two segments, one for established debtors and one for new debtors. Then develop separate score cards for the two segments.

Scorecard Validity Established
Pre-Implementation is validation is complete once it is established that the scorecard is valid for the current population.
Now its time for Strategy Development…

Strategy Development
Scoring Strategy
Cut-Offs
Strategy Development Communication
Risk Adjusted Actions
Overrides

Scoring Strategy
Following are important when making scoring strategies:

Effects on Key Segments.
“What-if” Analysis.
Policy Rules.

Single Scoring
Matrix Scoring
Matrix – Sequential Hybrid

Sequential Scoring
The applicant is scored on each scorecard sequentially, with separate cutoffs.
The following figure shows that an applicant has to pass through three different scorecards to get approved.
Approved
Bureau Score
Bankruptcy Score
Fraud Score
Pass
Pass
Pass
Fail
Fail
Fail
Declined

Matrix Scoring
Multiple scorecards are used concurrently with decision making based on a combination of the cut-offs for the various scorecards.
A good score from one scorecard may balance a bad score from another.

E.g.

Do you want to approve a low delinquency risk applicant who also has a high probability of attrition, or
Would you approve someone who is likely to be delinquent but has low probability of rolling forward to write-off?

Matrix Scoring (cont)
The figure shows:

Applicants with high delinquency score and high churn score being approved.
Applicants with low scores for both delinquency and churn being declined.
Applicants who are in the gray zone being referred for further scrutiny.
Applicants with low delinquency score and high churn score being declined outright.

Matrix – Sequential Hybrid
Applicants are pre-qualified using a sequential approach and then put through a matrix strategy.

E.g. Applicants can be put through a bankruptcy model first and upon passing the cut-off, be moved to a matrix strategy consisting of delinquency/ profit/ churn scores.

Benefits:

Simpler than multidimensional matrix.
More versatile than sequential scoring.

Cut-Offs
The minimum score at which an organization is willing to accept applicants.
It represents:
A threshold risk.
Profit.
Others, depending on the organizations objectives in using the scorecard.
The following figure shows a simple example of a cutoff strategy for new account acquisition.

Cut-Offs (cont)
Sophisticated strategies can be developed for complex applications in account acquisitions.

E.g. Based on the level of due diligence or additional information needed to give final approval to an application i.e. pending appraisals for mortgage loans or pending confirmation of income.
E.g. cut-offs are set above which income confirmation is not required.

This reduces the workload for:

Low risk customers, or
Low value loans.
In addition:

Higher risk applicants may be required to provide a copy of their pay stub to a branch.
Low risk applicants may be asked to simply fax theirs in.

Cut-Offs (cont)

E.g. A bank may have a final cutoff of 200 points for approval and may set a “hard low-side cutoff” of 180. Overrides of declined applications that score between 180 and 200, only.
Typical starting point for selecting cut-off, is the analyses of relationship between
Expected approval.
Bad rates for different scores.

Cut-Offs (cont)

A good approach in balancing the trade off between bad rate and approval rate is to identify two key points in the score range i.e.
What will be my bad rate if I keep my approval rate the same?
What will be my new approval rate if I keep my bad rate the same?

Strategy Development Communication
All stake holders should be involved in this process.

Marketing
IT
Collections
Finance
Customer Service
Legal
Others

Risk Adjusted Actions
Depending on score and other criteria, strategies can be developed, such as:

Risk adjusted pricing for loans and other credit products.
Insurance premiums for new accounts and also for re-pricing loans coming up for renewals.
Offering product upgrades to better customers.
Setting the level of down payment/ deposit for financed products such as auto loans & mortgages or setting renewal terms.
Cross selling of other products to better customers through pre-approvals.

Risk Adjusted Actions (contd)

Giving higher line of credit to better customers, both at application and as an existing customer.
Setting overall corporate exposure per customer based on risk profile, to manage concentration risks.
Using milder collection methods (i.e. sending letters) for low-risk customers & harsher actions (i.e. collection department) for high risk ones.
Allowing low-risk customers to make purchases on their credit cards above their limits, or when they are in early stages of delinquency.

Assigning Credit Line
Determine current line assignment practice based on the two measures and then use consensus based judgment to assign higher lines to better customers and lower to the high risk customers.

E.g. Top customers may get 30% increase and the worse ones a 30% decrease. Once best and worst boxes are filled, the rest can be filled in.

Determine the maximum and minimum credit lines the creditor is willing to give and assign these to the best and worst customers. Fill in the rest based on incremental increases or decreases.

Assigning Credit Line (contd)
One can work backwards to assign optimal “credit limits”, based on total expected loss for a cohort,

Expected Probabilities of Default (PD),
Loss Given Default (LGD),
Exposure at Default (EAD)

E.g.

Loss per account is Rs 500. If LGD is 97% of the limit. Then maximum credit limit with PDs 4%, 5%, 6% and 7 %, are following:

Overrides
It refers to manual or automated decisions that reverse the one taken on the basis of Score Cut-offs.
There are two kinds of overrides:

Low-side Overrides.
High-side Overrides.
Simple Rule
All overrides should be based on significant information available independent of the scorecard.

Overrides (contd)
For Example:

Local Knowledge about applicants:

Family income,
Recent job history,
Local economy etc.

Not being able to furnish satisfactory papers:

Mortgage/ Loan papers.
Income Confirmation.

A person was unemployed and missed payments, but now has a well paying job.

Issues for Successful Implementation

Cultural Change.
Requires top management support.
Operational process:
Redesign to minimise manual intervention and maximise cost savings.
Data Integrity:
Quality of the overall decisions is dependant upon the accuracy of the data input.
Setting the Cut-off score correctly

Main Challenges in Building & Implementing Score Cards

Non availability of quality data to build good scorecards.
Outdated front office systems.
Slow communication channels.
Credit Bureaus yet to develop their databases.
Credit & collection policies or even products may change often.
Lots of trust is put into the personal judgments.
Influence for deviations.

Post Implementation
Scorecard Management Reports

System Stability Report.
Scorecard Characteristic Analysis Report.
Final Scorecard Report.
Override Report.

Portfolio Performance Reports

Performance Report.
Vintage Analysis.
Delinquency Migration Report.
Roll Rate across Time.

Review
Once scorecard is built and implemented, a post implementation review is a good way to identify gaps or shortcomings in the overall scorecard development and implementation process.

Recapitulation
Consumer Financing.
Scorecard Development.
Implementation.
Post Implementation.

Questions & Answers
Thank You !
Fahad Zafar
Mayfair Business Consultants