Information Infrastructure for SME Lending World Bank Conference Raffi Kassarjian, Vice President,...

Information Infrastructure for SME Information Infrastructure for SME LendingLendingWorld Bank ConferenceWorld Bank Conference

Raffi Kassarjian, Vice President, Global Origination SolutionsRaffi Kassarjian, Vice President, Global Origination Solutions

Copyright © 2002 Fair, Isaac and Company, Inc. All rights reserved.Job # goes here

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The “S” in SME are uniqueThe “S” in SME are uniqueThe “S” in SME are uniqueThe “S” in SME are unique

Traditional commercial lending infrastructure doesn’t lend itself to “lots of little decisions”

Financial spreadsheeting used in commercial underwriting less relevant

Business bureau data (when available) is thin

Consumer credit bureau report/score (when available) doesn’t reflect profile of small business owner

Industry still coming up the learning curve

Combination of data required to understand the risk


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“80% of measurable risk is decided upon at the time of origination.”MarketingMarketing SecuritizationSecuritization ServicingServicing

OriginationOrigination

Application Workflow And

Business Policy ++ Credit Risk Decisions and

Analytics

++Loan Fulfillment

And Documentation

Importance of the right lending decisionImportance of the right lending decisionImportance of the right lending decisionImportance of the right lending decision


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The big pictureThe big pictureThe big pictureThe big picture

StrategyStrategy

BusinessBusinessObjectivesObjectives

MarketingMarketingProcessProcess TechnologyTechnology

Credit PolicyCredit Policy

Legal/Legal/ComplianceCompliance

RiskRiskCustomerCustomerServiceService


5

Business goals of small business credit Business goals of small business credit grantorsgrantorsBusiness goals of small business credit Business goals of small business credit grantorsgrantors

Increase number of profitable relationships

Manage overall portfolio risk

Realize operational efficiencies

Leverage IT resources and enabling technologies

Maintain regulatory compliance


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HighHighRiskRisk

Segment Applicant PopulationSegment Applicant Population

LowLowRiskRisk

Score and attributes

MediumMediumRiskRisk

AutomatedDecline

AutomatedAccept

Reducing the ‘gray area’Reducing the ‘gray area’Reducing the ‘gray area’Reducing the ‘gray area’


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Information RequirementsInformation RequirementsInformation RequirementsInformation Requirements

Lender’s existing data/relationship with applicant

Existing relationship with business applicant

Existing relationship with principal owners of business

Additional information provided by applicant

Business credit bureau information

Consumer credit bureau information (on the principal owners)


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Technology RequirementsTechnology RequirementsTechnology RequirementsTechnology Requirements

Application Processing System

Application Input

Internal and external data access

Scoring capabilities

Decision capabilities

Workflow/queuing for credit officer review

Exception and performance tracking


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Example: model-assisted decisionExample: model-assisted decisionExample: model-assisted decisionExample: model-assisted decision

Export Decline DataExport Decline Data

AutomatedDecline

AutomatedDecline

Export Accept DataExport Accept Data

AutomatedAccept

AutomatedAccept

JudgmentalDecline

JudgmentalDecline

GenerateScore

GenerateScore

Obtain Business/ Consumer

Report

Obtain Business/ Consumer

Report

Import or Enter

Data

Import or Enter

Data

ApplyDecisionStrategy

Review

ArchiveArchiveAppsApps

ArchiveArchiveAppsApps

ProduceProduceReportsReports

JudgmentalAccept

JudgmentalAccept

Document Document //

BookBook

Document Document //

BookBook


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Score

Business DebtService Ratio

Offer

HighHighMedMedLowLow

CCB

Offer

LowLow

MediumMedium

High

A

B

C

D

E

Line amount

HighMediumMedium

LowLow

Interest Rate

Level of automationLevel of automationLevel of automationLevel of automation

Lo Med

CAA EDC

Hi Lo Med Hi Lo Med Hi

Approved applicants


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Example: complex term loan strategyExample: complex term loan strategyExample: complex term loan strategyExample: complex term loan strategy

0 - 39%

12 -36 mos

Auto-Approve

> 45%

Strategy#1

Auto-Approve

Strategy#1

Auto-Approve

40% - 45%

SBSS Score=> 170

Strategy#2

> 36 mos 12- 36 mos > 36 mos

No Yes No Yes

Strategy#2

ManualReview

Strategy#1

ManualReview

Strategy#1

ManualReview

Auto-Decline

Application

1. Debt Ratio

2. Months inBusiness

3. HighBalanceDeposits

CreditDecision

AmountApproved

Decision Logic for Term LoanRequest

Evaluation Steps


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Scoring Models can produce significant Scoring Models can produce significant results results Scoring Models can produce significant Scoring Models can produce significant results results

Validation of SBSS 5.0 Models

Original SBSS SBSS 5.0 RANDOM

BAD CUMULATIVE PERCENT

00.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

GOOD CUMULATIVE PERCENT00.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0


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Original SBSS SBSS 5.0Applications/year 50,000 50,000

Approval rate 60% 60%Booked at approval rate 30,000 30,000

Bad rate 7.2% 5.6%# bads 2160 1680

Bads saved 480Avg. line $30,000

Saved exposure $14,400,000Estimated loss rate 50%

LOSS AVOIDANCE $7,200,000

The potential impact of empirical modelingThe potential impact of empirical modelingThe potential impact of empirical modelingThe potential impact of empirical modeling

Example based on development sample. Individual results will Example based on development sample. Individual results will vary.vary.


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Implement the RIGHT overridesImplement the RIGHT overridesImplement the RIGHT overridesImplement the RIGHT overrides

As of March 2002 for accounts opened Oct. 00 – Sept. 01

140 - 149140 - 149150 – 159150 – 159SubtotalSubtotal

160 - 169160 - 169170 - 179170 - 179180 - 189180 - 189190 & up190 & up

Total ScoredTotal Scored

Prev.Good ExpPrev.Good ExpHi Checking BalHi Checking Bal

Long Time as OwnerLong Time as OwnerTotal OverridesTotal Overrides

SCORESCORESCORESCORE

181181409409590590

614614705705791791

2,3142,3145,0145,014

204204238238148148590590

# ACTIVE# ACTIVEACCTSACCTS

# ACTIVE# ACTIVEACCTSACCTS

7714142121

19191515131323239191

11115555

2121

# EVER# EVER60+ DAYS60+ DAYS# EVER# EVER

60+ DAYS60+ DAYS

3.9%3.9%3.4%3.4%

3.1%3.1%

2.1%2.1%1.6%1.6%1.0%1.0%1.8%1.8%

5.4%5.4%2.1%2.1%3.4%3.4%3.6%3.6%

% EVER% EVER60+ DAYS60+ DAYS% EVER% EVER

60+ DAYS60+ DAYS

BEL

OW

C

UTO

FF


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Tracking performanceTracking performanceTracking performanceTracking performance

Reporting on applicant populations Population Stability Reports

Characteristic Analysis Reports

Reporting on decisions Override Tracking Reports

Approve/Decline Reports

Automated/Manual Decisions

Reporting on performance Strategy performance

Override performance

Model performance

Policy performance

Process performance

Scorecard management


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Fair, Isaac and Small BusinessFair, Isaac and Small BusinessFair, Isaac and Small BusinessFair, Isaac and Small Business

Pioneered use of empirically-derived analytics

Recently released fifth generation of pooled origination models

Serve approximately 400 clients

22 of top 25 US lenders

Banking, leasing, captive finance, trade credit

Global presence – US, Canada, Chile, Hong Kong, Japan, Malaysia, Mexico, Singapore, South Korea, United Kingdom

Questions & AnswersQuestions & Answers

Information Infrastructure for SME Lending World Bank Conference Raffi Kassarjian, Vice President,...

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