wccrc 2008 fair isaac scoring tom quinn - sinacofi · pdf filefair isaac corporation credit...

October 2008

Thomas J. Quinn, VP - Global Scoring SolutionsFair Isaac Corporation

Credit Scoring

Confidential. The material in this presentation is the property of Fair Isaac Corporation, is provided for the recipient only, and shall not be used, reproduced, or disclosed without Fair Isaac Corporation's express consent. © 2008 Fair Isaac Corporation.

October 2008

Dynamic times for financial services

• U.S. mortgage lending situation has blossomed into an unprecedented world-wide credit crisis – Lack of liquidity– In many markets -- less consumer demand for credit and lenders

have tightened credit criteria

• Lenders are questioning validity of scoring tools given increasing losses and credit quality deterioration

• Lender currently focused on risk mitigation, but …• Also want to “be ready” for when the pendulum swings

back to more robust economic conditions

Opportunities exist to help ourclient base through this cycle!

© 2008 Fair Isaac Corporation. All Rights Reserved.

October 2008

Discussion topics

• Revisiting the basics • Credit scoring usage opportunities • Credit scoring innovation


October 2008

620

640660

680

Scores are designed to rank order


October 2008

Credit scoring basics

• Credit score distributions are not static, rather they are fluid and it is expected that they will change over time

• It is natural that score alignment will move and change over time – Changes in data reported, consumer credit behaviors, lender

practices, changing economic conditions, score updates, etc.

• For these reasons, it is important that each lender monitor and track their portfolio dynamics bycredit score on a frequent basis and makeadjustments to strategies as needed


October 2008

FICO® score odds alignmentU.S. example

All Existing & New AccountsTrade line PerformanceBad Definition: 90+ days & Charge-offs

1

10

100

1,000

500 520 540 560 580 600 620 640 660 680 700 720 740 760 780 800

FICO® Score

Odd

s (B

ads

Vs N

on-B

ads)

2000 - 2002 2003 - 2005 2005 - 2007


October 2008

Industry odds by “Lender”

1.0

10.0

100.0

1000.0

<500

500-

519

520-

539

540-

559

560-

579

580-

599

600-

619

620-

639

640-

659

660-

679

680-

699

700-

719

720-

739

740-

759

760-

779

780-

799

800+

FICO® Score

Odd

s (9

0+ d

pd)

Industry Lender A in all regionsLender B in region 3 Lender B in region 1Lender C in region 1 Lender C in region 2Lender D in region 2 Lender E in all regions


October 2008

Opportunities• Education & training

– Create educational programs that educate your clients, regulatory bodies, etc., on how your value-added tools are developed, what they are designed to do, and the end-user responsibilities when using credit scores

• Tracking & reporting services – Create quarterly or monthly credit scoring tracking report that

provides trending on score distribution, key credit variables, and score performance for broad-based population, as well as by industries of interest

• Client-specific validation services – Create service to customized tracking and trending

reports for client on their portfolios

• More frequent update/redevelopments


October 2008

Discussion topics

• Revisiting the basics• Credit scoring usage opportunities • Credit scoring innovation


October 2008

Early 2000s: Focus on growth


October 2008

Today: Focus on controlling risk


October 2008

Opportunities

• Increasingly, lenders are considering use of multiple scores to reduce risk exposure – Multiple scores predicting different types of risk (general risk vs.

bankruptcy risk, for example)– Pulling of credit scores from “secondary” credit bureaus in

markets where multiple bureaus exist

• Frequent credit score “refreshes/updates” on existing customers is more prevalent

• Benefits– Lender – better decisions/more profitable portfolios– Bureau – sell more information/services &

deepen relationship


October 2008

CB BANKRUPTCY RISK

High

LowHigh Low

OVER

ALL

RISK

Previously Approve – now Decline

New Bankruptcy

Cutoff

Previous Risk Score

Cutoff

Approvals

Opportunity: dual score approach• Lender uses multiple scores to identify “swap set

segments” where alternative decision/treatment is employed


October 2008

Case studyDual score use - new account bookings

Risk compared to Risk & Bankruptcy ScoreTools

Explore retrospective results to design initial challenger tests to evaluate new strategies incorporating different tools

Methodology

Identify more appropriate approvals to modify applicant screening segmentationTactic

New account approvalsPopulation

Reduce loss reserves & improverating positionObjective


October 2008

Benefit analysis

Reduced bankruptcy losses by 10% with minimal reduction in revenue

-3.8%-0.11%2.75%2.86%Bad Rate

-8.1%-$250$2,850$3,100Total Losses(000)

-10.0%-$170$1,530$1,700Bankruptcy Losses (000)

-2.5%-$270$10,420$10,690Net Revenue(000)

-1.9%-1.4%71.1%72.5%Percent of Approvals

DifferenceChallenger 1Champion


October 2008

Conceptual value of “secondary” score

• In many countries, there are multiple credit bureaus in operation

• Often, certain data elements captured on the consumer can be different across bureaus—driving differences in scores

• Accessing scores from multiple bureaus provides the lender with additional information for credit granting decisions – Swap In segments – Swap Out segments

• Question: understanding & quantifying thecost/benefit trade-off


October 2008

Case study results • Large U.S. lender (credit card portfolio)• Champion: Custom score and FICO® score from one bureau• Challenger: Custom score and FICO® score from primary &

secondary bureaus – Identify swap sets where use of secondary bureau FICO® score would

change approve/decline decision. Assess volume impacts as well as resulting loss performance impacts

• Test results– ~5% increase in approvals with no increase in risk– Devised strategy to obtain secondary score on targeted segments – Exceeded client’s internal ROI threshold (value provided exceeded

incremental bureau costs and operational efforts to implement)


October 2008

Score refresh – how often is enough?

• Obtaining updated credit bureau scores on a portfolio facilitates a lender’s ability to more accurately understand the over-risk & opportunity value of a given customer. – Provides a broad view of their credit practices not

captured with internal behavioral scores and metrics• How often do credit scores migrate over time? How

often should lenders refresh or obtain updated scores?

• How valuable is a refreshed score inpredicting delinquent behavior?


October 2008

FICO® score migration3-, 6-, and 9-Month MigrationsJun 07 - Mar 08

* Positive score difference equates to score increase over time

6% 8%

75%

7% 4%9% 10%

65%

10% 7%10%

58%

12% 9%11%

0%10%20%30%40%50%60%70%80%

Low to -41 -40 to -21 -20 to +20 +21 to +40 +41 to HighScore difference*

% o

f acc

ount

s

3 months 6 months 9 months


October 2008

How much more predictive is a fresher score?

9-month 730

6-month 730

3-month 730

9-month 690

6-month 690

3-month 690

Score Cutoff

23.975.73.4%3.6%30.2%69.8%

26.964.92.9%2.8%29.9%70.1%

34.358.32.0%1.8%29.7%70.3%

11.853.73.1%3.1%18.8%81.2%

12.853.02.5%2.3%18.6%81.4%

14.946.71.8%1.5%18.6%81.4%

Migrated Below

Migrated Above

% Migrated Below

% Migrated Above% Below% Above

Swap Set odds(10-point buffer)

End of Migration(10-point buffer)

Beginning of Migration

Swap set table - 14-Month Good/Bad OddsFICO® Score Migration: Through March 07

• The older scores exhibit larger swap sets and greater differences between the actual risk of the swap sets. (The older scores are further away from providing the correct risk assessment.)

• Lenders using the older scores are at greater risk of making suboptimal decisions on the swap set consumers.


October 2008

Discussion Topics

• Revisiting the basics• Credit scoring usage opportunities • Credit scoring innovation


October 2008

Lender “requests” for help

• Cracking the over-indebtedness question• Mining alternative data for risk prediction

insights• Forecasting future odds• Modeling consumer responsibility


October 2008

Understanding debt sensitivity

• Risk– Most credit scores predict credit risk from bureau report snapshot

• Predicts future performance based on past behavior– Incremental debt impact not considered– Same score implies same risk– However, same score includes individuals within different credit profiles

• Capacity– Identifying consumers’ ability to safely manage incremental debt

• Not known at the time of scoring– Different credit profiles suggest different incremental debt sensitivity

• Low capacity = higher risk sensitivity to incremental debt• High capacity = lower risk sensitivity to incremental debt

– Must be considered relative to risk– Income not always a proxy for debt sensitivity

• Improved understanding of debt sensitivity withinrisk levels can drive higher profits


October 2008

0%

5%

10%

15%

20%

25%

30%

No Incre

ase

Low

MedLow Med

MedHigh

High

Revolving Balance Change

Bad

Rat

e

Low Med High

Strong risk separation as balance increases

37%

45%

18%

Pop%

Results on Pooled Bankcard sample

New Bankcards - Mid-Low FICO® ScoreFICO® 660-699 by CCI


October 2008

Mining alternative data

• Where alternative data exists – Develop scoring solutions that incorporate alternative data not

being captured via traditional infrastructure • Align to existing solutions in place• Create “seamless” interface for client access• Ensure regulatory compliant

• Where alternative data not readily available– In some markets, opportunity to build risk, income, asset

“indexes” based on key population segments (postal code, etc.)– Infer that “no hit/no score” population is likely to have similar risk

and income profiles to the segment– Use indexes (along with layering of other elements)

for credit granting and line assignment decisions


October 2008

Focus on the future

• Cracking the over-indebtedness question• Mining alternative data for risk prediction

insights• Forecasting future odds• Modeling consumer responsibility

In the research phase


October 2008

Thomas J. [email protected]

Thank You

Confidential. The material in this presentation is the property of Fair Isaac Corporation, is provided for the recipient only, and shall not be used, reproduced, or disclosed without Fair Isaac Corporation's express consent. © 2008 Fair Isaac Corporation.

wccrc 2008 fair isaac scoring tom quinn - sinacofi · pdf filefair isaac corporation credit...

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