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Charles WendelWorld Bank Workshop on Credit Scoring

New Delhi, IndiaMarch 3, 2006

Financial Institutions Consulting

Credit Scoring: Key Initiatives, Issues, andOpportunities

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2www.ficinc.com Copyright 2006 by Financial Institutions Consulting, Inc.

Todays Focus

What is credit scoring?

Requirements for creating a reliable model

The benefits of credit scoring

How do banks use credit scoring? Developed World Case Example: Union Bank of California

Developing World Case Example: Barclays Bank - Kenya

Constraints and limitations to SME credit scoring

Issues for discussion

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Credit scoring is a statistical tool that assists bankers in making sound creditdecisions

For SMEs, scoring models blend information from both the businessand the business owner to derive a score

Most bankers use the resulting score as a guide in the credit-decision

process

Credit Scoring is NOT Auto-Decisioning

What is SME Credit Scoring?

Definition: A statistical technique that combines several financialcharacteristics to form a single score to represent a borrowerscredit worthiness*

*The FreeDictionary.com

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Credit scoring activities need to be viewed as part of a continuum

Credit Screening Credit Scoring Auto-Decisioning

Credit Scoring Development

Description An initial test to ensure that the applicantmeets the lenders basic credit

requirements

Performed either by the banker duringthe initial applicant interview or throughan online questionnaire

A statistical technique that combinesseveral financial characteristics to form a

single score to represent a borrowerscredit worthiness

Derived from data provided on theapplication for credit as well as fromoutside sources (e.g., credit bureaus)

An automated application decision based on theapplicants credit score

Typically only very sophisticated banks and cardcompanies use auto-decisioning, and then onlyfor relatively small loan amounts, usually under$100,000

Elements Requires a checklist of basic eligibilitycriteria (e.g., number of years inbusiness, income, etc.)

Requires customer information providedon the credit application as well asprovided by credit bureaus

Requires a valid scoring model, eitherproprietary or provided by a third-party

Requires the lender to specify a credit scoreabove which the applicant will be approved,below which the applicant will be declined

Benefits Gives unqualified applicants animmediate decision

Reduces the number of unqualifiedapplications entering the underwritingsystem

Provides a statistically accurate probabilityof repayment based on the pastperformance of similar applicants

Reduces the cost of underwriting and thetime required to decision each application

Reduces the time and cost associated withprocessing and underwriting credit applications

Often used to provide applicants with an instantdecision

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Within credit scoring, there are three types of models. The pooled datamodel is the most accurate and the one most used in developed markets

Types of Credit Scoring Models

Expert Model Developed for a specific bank Judgment-based; not based on empirical data

Used in the following situations:

Inadequate historical data exists

There is a need for an economical alternative to a customscoring model

Insufficient internal bank loan volume exists to supportdeveloping a custom model

Custom Model A statistical model based solely on the banks internalcustomer data

However, potential limitations exist due to lack of diversityin the banks customer base

Pooled Data Model A statistical model based on the pooled customer data ofmultiple lenders

The larger data pool enhances accuracy

Example: Fair Isaacs Small Business Scoring Service

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The scoring model evaluates a number of criteria and produces a scorebased on the lenders weighting of each criterions attribute

Sample SME Credit Scoring Model

Possible Criteria Attributes with Hypothetical Weights*

Credit History of Principal(s) Data:Consumer Credit Report

Major Derogatory(bankruptcy, collections)

- 60pts

Minor Derogatory (minordelinquencies)

- 10pts

Satisfactory

+15 pts

No Record:

0 pts

Unused Credit Data: ConsumerCredit Report

75% of Available+40 pts

74%-33% of Available

+30 pts

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Source: Fair Isaac and Co., Inc.

The resulting score provides a statistically accurate probability ofrepayment unmatched by judgment-based underwriting methods

Example: SME Applicant Evaluation

Criteria Judgment-BaseUnderwriting

Credit Scoring

Credit history of principal(s) + 15

Unused credit 20

Credit history of business + 15

Industry type 20

Available liquid assets of business + 45

Net worth of principals + 40

Overall Decision +Accept

155Accept

Probability of repayment (based on the pastperformance of the pooled borrowers)

? 18:1(18 out of 19 applicants with this

score will repay)

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1. The model should be based on data derived from a comparison of

sample groups of credit-worthy and non-credit-worthy applicants whorecently applied for credit (both approved and declined)

2. The model should be developed using accepted statistical principlesand methodology

3. The model should be periodically revalidated and adjusted asnecessary

Scoring models result from an analysis of characteristics that arestatistically associated with repayment. Models are periodically

reassessed to ensure continued reliability

Elements of a Credit Scoring Model

Source: Farleigh, Wade & Witt PC

Requirements for Creating a Reliable Model

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For SMEs, information about the company as well as its owner(s)can provide a statistically reliable indication of repayment

Predictive Factors Included in Scoring ModelsBusiness Owner

Years in Business Age

Industry Marital Status

Years of Management Experience Residential Status

Total Assets Number of Years at Address

Company Credit Bureau File Personal Credit Bureau File

Multi-year history of payment performance Multi-year history of payment performance

Public record information such asbankruptcy and tax liens

Public record information, such as bankruptcyand tax liens

Scoring models are most reliable when sufficient data exists to depict a borrowersperformance over a multi-year periodAND when sufficient data exists to depict other

similar borrowers performance over a multi-year period

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To ensure their validity and ongoing reliability, scoring models must be initially validated and then revalidatedon an ongoing basis

Validation Testing Demonstrates that the credit factors used inthe model are predictive of the applicantscredit-worthiness

Revalidation Testing Demonstrates that the credit factors used inthe model continue to be predictive of theapplicants credit-worthiness

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The use of SME credit scoring appears to benefit multiple stakeholders,including lenders, borrowers, and the overall economy

The Benefits of SME Credit Scoring

ReducedTransaction Costs

Reduced CreditLosses

IncreasedNII Revenue

Results in

Increased Availability of SME Credit

Benefits Include:

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SME credit scoring can significantly impact a banks costs and revenue

Total Application and Underwriting Costs

Consumer Loan SME Loan

$30 50 Underwriting $300 500

$2 10 Administrative $25 100

$10 20 Corporate Overhead $100 200

$1 5 Data Cost $25 100

$5 10 Miscellaneous $50 100

$48 95 TOTAL COST $500 1,000

Today

(Ass

umesth

atconsumerloans

are

typically

scoredandSME

loansarenot)

Impact of Credit Scoring:

Reduce Costs:

Reduce losses by 10-30% while holding approval ratesconstant

Reduce time and manual steps in processing eachapplication

Reduce number of applications requiring manual review

Reduce training time for new credit staff

Note: Approximately 80% of costs are people costs.

Source: Fair Isaac and Co.; FIC analysis

Increase Revenues:

Increase approval rate 10-30% while holding theloss rate constant

Increase NII through risk-based pricing (A study by theFederal Reserve Bank of Atlanta suggests that banks usingSME Credit Scoring enjoy a 7 percent higher loan premium thanbanks that do not use scoring)

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Overall increase in the quantity of SME credit extended the study reporteda 37 percent increase in the ratio of SME loans to total commercial loansamong banks using SME credit scoring

Increased access to credit for SMEs lacking sufficient financial information

Due to the use of the business owners information in the decisioning process

Increased lending in low-income areas Believed to result from greater objectivity in the underwriting process

Increased lending outside the banks footprint

Resulting from the elimination of the need for face-to-face meeting withborrowers

However, we caution lenders in this area

A recent study by the U.S. Federal Reserve Bank of Atlanta shows that theimplementation of SME credit scoring increased the availability of credit alongmultiple dimensions

Source: Federal Reserve Bank of Atlanta

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Banks use of scoring varies based on the amount of information available and itsreliability as well as their level of comfort with the technology

In the U.S., banks typically score smaller loans. Most do not automatedecisions based on credit scores

Banks typically use scoring to supplement more traditional underwritingmethods

In countries lacking the infrastructure required to build pooled data scoringmodels, banks may create either an expert model or a custom scoring model

to aid their loan decisioning process

How Banks Use SME Credit Scoring

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U.S. banks typically rely on scoring only for relatively small loans

Source: FIC analysis. Based on responses from 35 U.S. banks

20%

31%

8%

8%

0%

31%

Less than

$100,000

$100,000

$250,000

$500,000

$1,000,000

Greater than

$1M

Maximum Scored SME Loan Amount

Percentage of Banks

51% scoreloans of$100,000 or

less

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In addition, U.S. banks auto-decision relatively few SME loans

Source: FIC analysis. Based on responses from 32 U.S. banks

25%

3%

6%

0%

65%Do Not Auto

Decision

Fewer than 10%

11-25%

26-50%

Over 50%

Percentage of SME Loans that are Auto-DecisionedPercentage of Banks

%

of

SMELoansAu

to-De

According to Benchmark International, the most sophisticated SME banks auto-decision up to 40 percent of their small business applications

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However, banks are more likely to score and auto-decision small-ticket*leasing transactions

* Transaction sizes less than US$250,000

Source: Equipment Leasing Associations 2005 Survey of Industry Activity

50%

22%

29%Completely

Manual Review

Credit Scored

with Manual

Review

Credit Scored

and Auto-

Decisioned

Bank Lessors Decisioning Method for Small Ticket TransactionsAs a % of Total Volume

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Banks apply SME credit scoring within the context of their sales and risk management processes

Barclays Bank (UK)

Focus: Supports the banks focus onstart-ups

Examples

Barclays has a niche focus on start-up smallbusinesses

The bank bases its credit decision for the businesson the credit score of the business owner

Business owner is personally responsible forrepayment

BB&T

Focus: One of several inputs into localdecision making

SME banker uses credit scoring as just one of severaldecision-making tools

Local banker has decision-making responsibility

However, the bank has established guidelines and

parameters to assist in making decisions

National City Bank

Focus: Precedes manual centralizedreview

Each potential borrower is credit scored

Every application is manually, if briefly,reviewed by a centralized underwriter

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Case Example: Union Bank of California

Union Bank of California, a US$50 billion west coast bank, credit scoresloan requests up to US$100,000. However, an underwriter must

manually review the application prior to approvalBackground:

Assets: US$50.5 billion

Branches: 340 branches in California, Oregon,and Washington

Small Business Definition: Companies with revenues less thanUS$5 million

Ownership: Bank of Tokyo-Mitsubishi UFJ

Use of SME Credit Scoring:

Union Bank scores loan requests up to US$100,000

If the score exceeds the banks minimum, an underwriter reviews theapplication

Manual review consists of verifying application information against theborrowers tax return submitted with the loan application

Underwriters typically decision and price loans based on the credit score;over-riding the recommended decision requires credit manager approval

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In countries lacking well-developed credit bureaus, banks have developed a number of methods to help standardize their SME decisioning process

Examples

Banco Solidario(Ecuador)

Although primarily a micro-lender, Banco Solidario has leveragedseven years of internal customer data to create a robust,segmentable scoring model

Data mined included: industry segment, years in business,deposit balances, and repayment history

While still in the validation phase, the bank expects the scoringmodel to reduce its lending cost and reduce its NPLs, which arealready lower than the national average (6.2% of assets versusthe national average of 7.3% as of Q1 2005)

Citibank In nearly 20 countries worldwide, Citibank has implementedCitiBusiness, a vertical market approach to SME lending

CitiBusiness is a marketing, sales, and credit processtargeting specific industries

By developing an expertise in a selected industry, Citibankcan effectively evaluate loan applicants based on industrytrends and standards

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Case Example: Barclays Bank of Kenya

Barclays Bank of Kenya has developed a decision tree for unsecuredloans up to about $10,000

Background:

Customers: ~ 45,000 many in the informal sector

Borrowers: ~ 20,000

Small Business Definition: Customers that do NOT require arelationship manager

Decision Tree Elements Include:

Company revenues

Past payment performance

Number of employees

Number of years in business

Loans greater than $10,000 are secured by land or other property

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Limitations to SME credit scoring primarily relate to the availability andreliability of data

Many developing countries lack centralized credit bureaus Even in highly developed markets, internal bank information may be

incomplete, inaccessible, or non-existent

In some cases, credit bureau data may be inaccurate or incomplete

The cost to implement scoring may also limit its use by smaller banks

Constraints and Limitations to SME Credit Scoring

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A number of factors often impede development of credit bureaus:

Data Privacy Regulations: Privacy laws in many countries restrict or prohibit data sharing. Forexample, because of data privacy laws, Brazilian banks were unable to participate in a recentWorld Bank study related to SME credit scoring

Competitive Considerations: Many banks that have invested in the resources required tocapture and store historical customer data believe it gives them a competitive advantage,therefore, they are often unwilling to share it

Ownership Issues: Non-participating banks may be suspicious of credit bureaus owned by aconsortium of banks. Similarly, government ownership of credit bureaus may also raise

suspicion or skepticism

The lack of centralized credit bureaus may be the most significant constraint to the development of creditscoring in developing countries

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Maybe Maybe

Retrieval of customer data is often difficult

Even in top U.S. banks, customer data required to create scoring models is often housed in separate, unlinked systems if it is captured at all

BusinessFirmagraphic

Data

SME CRMSystem

BusinessLoan

Information

SME LoanSystem

BusinessOwners

DemographicInformation

RetailCRM System

BusinessOwners Loan

Information

Retail LoanSystem

X

In many cases, data capture is inconsistent within banks, depending upon customer type

Banks typically capture the most information on loan customers, the least on deposit-only customers They dont borrow so they are not a real customer

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In 2002, Metris*, the tenth largest credit card issuer in the U.S., experienced a 30% increase in charge-offs, despite its heavy reliance on credit scoring

In its evaluation of the cause of Metriss sharp increase in charge-offs, officials from the Federal Deposit Insurance Corporation (FDIC) noted a number of credit scorings short-comings:

As much as 30% of credit bureau information may be inaccurate

Most scoring models use only two years of data which may not be enough data to predict behavior across economic cycles (At the time, two years of data did not include an economic downturn)

Some borrowers may be able to polish their credit score by planning ahead and rearranging finances

Even in countries with sophisticated credit bureaus, the reliability of the information occasionally comes into question

* HSBC purchased Metris in 2005

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Banks such as Wells Fargo and Citibank have invested tens of millions of dollars in their proprietary scoring models prohibitive even for most largebanks

CGAP* estimates the cost of implementing a scoring model as follows:

The cost of developing a scoring model may also be a significant factor limiting implementation

Outsourced Project Management $5,000 - $50,000

Scorecard Development 10,000 - 60,000

Software for Reporting and Analysis 5,000 - 65,000

Total $20,000 - $175,000

In addition, costs for training and updating the scorecard continueannually

* Consultative Group to Assist the Poor, a World Bank affiliated group working to improve access to financial services in developing countries

Issues for Discussion

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Issues for Discussion

Among the issues related to SME credit scoring that banks mustconsider:

Given the internal information currently available and the current state of creditbureaus in your country, what is the most appropriate scoring model for yourbank?

What steps must your bank take to standardize internal data collection

procedures? Does your bank currently collect the type of information requiredto create a strong scoring model? If not, what is required to begin collectingthat data? What are the constraints to collection (regulatory, cultural, etc.)?

Given your banks data quality and culture, how should it use SME creditscoring? Up to what loan size should it consider scoring? Is auto-decisioningan option for your bank?

Over what time period will the benefits afforded by SME credit scoringoutweigh its cost?

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What are some of the constraints involved in sharing the type of customer datarequired to build a robust scoring model? Are they primarily legal/regulatory or are

they competitive?

From a country-wide perspective, what is the most appropriate use for creditscoring? What processes and practices must change in order to maximize the fullpotential of scoring?

What role does the informal sector play in the SME economy? How can theiractivities be captured in a credit scoring model?

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