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Maximizing predictive performance at origination and beyond!

John Krickus, Experian

Joel Pruis, Experian

Amanda Roth, Experian

© 2010 Experian Information Solutions, Inc. All rights reserved.Experian Public. 2

Agenda

Score performance in financial crisis

Model performance by model type

Impact of data

Custom model process

Decisioning options

Model performancein the financial crisis



Consumer marketplace

Financial Institutions experienced higher delinquency across all products

Increase actually began in 2004 / 2006 for real estate portfolio

Bankcard experienced 33% increase in the 2007 / 2009 period


Performance of consumer score

Although some change was experienced, scores remained relatively stable over the analyzed periodsPercentage of change experienced from development time:

► Overall – 9%► Auto – 4%► Bankcard – 2%► Real estate – 30%

Real estate experienced the greatest degradation primarily due to:

► Capacity verification► Home value depreciation► Job loss increases

Validation results using GINI statistic


Employee size trendsSix-month trend shows slight decline for smaller firms

For small business (0 - 20), the percent dollar delinquent and the percent 91+ increasing slightly

March 2010 Six-month change

Risk score

AverageDBT

Percent dollars

delinquent

Percent 91+

delinquent

Riskscore

AverageDBT

Percent dollars

delinquent

Percent 91+

delinquent

National average 58.60 6.28 11.8% 5.2% -0.9% 1% -1% 0%

Number of employees

Nonemployer 61.22 5.52 7.3% 4.7% -0.7% 0.2% -3.3% -1.5%

1 to 4 56.38 6.72 13.1% 7.6% -1.4% 1.0% 0.1% 0.9%

5 to 9 54.16 8.02 14.3% 7.2% -1.3% 0.9% 0.7% 2.4%

10 to 19 53.46 8.26 14.3% 5.9% -1.1% 0.9% -0.3% 1.5%

20 to 49 54.52 8.08 13.6% 4.8% -0.9% 0.7% 0.4% 3.0%

50 to 99 54.96 7.66 14.0% 3.5% -0.7% 0.7% -0.2% 2.0%

100 to 249 53.05 7.83 16.0% 2.8% -0.7% 0.5% -0.2% -3.3%

250 to 499 51.62 8.24 15.1% 2.2% -0.8% 0.2% 0.4% 1.3%

500 to 999 51.36 8.55 17.4% 1.8% -1.1% 0.4% -0.1% -2.1%

1,000 and over 38.04 7.53 18.1% 2.0% 1.3% 1.7% 0.3% 1.4%

Source: Experian Business Information Services


Performance of business score: Intelliscore PlusSM

Pre vs. post financial crisis, bottom 10% bad capture

Note: Large company segment and demographic only records less then 5% of total, not shown

Bad rate capture for bottom 10% remained strong across model segments


LendingAt the peak of the cycle

Eco

nom

ic g

row

th

Time

Expansion Recession

What are you doing with those clients that are just above your acceptable score threshold?


Unsubstantiated response to recession


Loan production changePre vs. post

Category Pre Post Change Impact

Application volume per financial institution 1,185 1,061 -124 - $8,624,860

Average request $112,186 $95,012 - $17,174 - $11,297,401

Approval percentage 62% 58% -4% - $4,032,309

$82,423,054 $58,468,484 - $23,954,570

a\$23,954,570 @ 3.00% Net Interest Margin = $718,637


Summary: financial crisis performanceConsumer / business / lending

Consumer scores experienced slight declines, major exception: real estate

Business scores, bottom 10% bad capture held up extremely well

Small business payment performance has finally improved

Combination of factors drove drop in lending

84% of drop related to number of applications, amount requested, 16% related to approval rate

Model performanceby model type



Evolution of model performance

Model performance is driven by several factors: Bad definition, model build population, depth/breadth and quality of data

Performance gains have been realized in each model type

Which is the best fit for your operation? Factors to consider:

► Volume / account size / score dependency

► Cost benefit analysis costs vs. operation savings and performance improvement

All industry Industry-specific Custom model


Performance of all industry models Bad capture

Significant performance gains for IP: 10% and 20% bad capture up around 40% vs. 113 model

Bad definition: 90 day payment delinquency or bankruptcy

Model built off of Experian’s all industry database, BizSourceSM, bad rate of 14.7%

All industry

Intelliscore PlusSM (October 2008) vs. previous two models

Intelliscore PlusSM

Commercial IntelliscoreSM 210

Commercial IntelliscoreSM 113


Performance of industry-specific vs. all industry

80% bad capture for industry model vs. 50% for all-industry model (bottom 20% bads)

Bad definition: Two 60-day late pays or one 90-day late card payment

Model built off of SBCS financial trade and general business database, bad rate: 7.81%

Industry-specific

Small Business Credit ShareSM: Bad capture bottom 20%, private card


Highest evolution of model performance Custom

Custom model provided continuing lift in bad capture, worse 10%, up to 53% improvement Performance gains measured against industry-specific Small Business Credit ShareSM

modelsBoth Small Business Credit ShareSM and BIS business data available; data selected for model is client specific

Custom model

46% lift41% lift

53% lift

3% liftAcross all client types, superior performance (bottom 20% bad capture)

Impact of data onmodel performance



Predictive information impact on score*All industry Intelliscore PlusSM

Model weighting* Business aggregates

5-10% Historical payment behavior, delinquency trends

50-60%Current payment status – number / trade balance / percent of accounts delinquent

10-15%Credit utilization, in commercial data using highest credit as proxy for credit line

5-10%Company profile – years in file, industry risk, employee size, number of inquiries

10-15% Derogatory items – collections, liens, judgments, bankruptcy

* Estimate based on inquiry patterns and score model segments utilized


Blended data improves performanceSignificant gains using blended vs. commercial only

Intelliscore PlusSM blended, meaning commercial data and consumer data on owner / guarantors vs. commercial29% improvement in screening out bad accounts at the 90% cutoff level

► 71% of bads excluded vs. 55%Almost screen out as many bads at higher approval level, 90%, as were screened out at 80%

► 71% vs. 74%

Percentage of bads eliminated


ALL001Total number

of trades

Commercialcredit database

FTC001Total number offinancial trades

ALL001Total number

of trades

FIL001Total number of

financial installment loans

FLS001Total number of

commercial lease trades

FCC001Total number of

commercial credit card trades

Small BusinessCredit ShareSM

Financial industry specific data provides lift – Small Business Credit ShareSM data expands total number of trades


Predictive data used in all industry vs. custom modelsData selected that has the most impact on your portfolio

Data aggregate for IntelliscoreSM

► RTB030 average balance, recently delinquent accounts

► RTO076 past presence of delinquency

► TTC series, number of accounts: current / delinquent / derogatory

► TTC090 percent of balance seriously delinquent

► TTP series, percent of accounts: active / delinquent / seriously delinquent

► TTP093 utilization: total balance to total high credit

► OTB leasing balance

Data aggregate for custom model

► RTB030 average balance, recently delinquent accounts

► RTO076 past presence of delinquency

► TTC series, number of delinquent accounts / current /derogatory

► TTC090 percent of balance seriously delinquent

► TTP series, percent of accounts: active / delinquent / seriously delinquent

► TTP093 utilization: total balance to total high credit

► OTB leasing balance

Custom model process



Custom model advantages The ultimate in performance

Custom models utilizes your...

Each custom model is based on your bad definition, delinquency, non-performing, etc.

Uses your specific account portfolio characteristics and performance

Model will utilize the business data elements that best fits your portfolio

Bad definition Best data fitPortfolio profile

Custom model


Bad definition is specific to your portfolio, not a generic or even industry model

Key advantage of custom model designUsing your bad definition

Model performance improves significantly when built directly on a specific “bad” event

► Early stage delinquency: 15, 30, 60 etc.

► Deposit requirement

► Write-off

Custom model


Key benefits of custom model outputCustom model

Customized odds charts

Results driven by portfolio characteristics

Odds chart tied directly to your portfolio’s performance

Options to build

Internal analytic resourcesExternal consultantsExperian analytics, combo of above

Improved performance

30%, 45% improved performanceHuge ROI on bad debt / slow pay elimination with same or increase account approval


Custom model lift: Major cable / satellite – Major lift in KS and bad capture compared with IntelliscoreSM

45% lift in bad dollar capture 75% lift in KS

Summary results by account type

KS

Bad

$ c

aptu

re w

orst

sco

ring

20%

Custom model


Custom model process – three stepsWeeks, not months

Initial data files from with performance based on agreed to “bad” definition

► Average of three weeks, however can vary widely

Decision science

► Two weeks to develop the model after the receipt of clean data: segmentation / model development / validate against holdout / performance statistics

Technology

► Three weeks for implementation and deployment: coding and audit / validate programming

Custom model

Result: In eight weeks, or less, a custom model is produced!

Decisioning



Decision treeEnhancing score prediction

Start at top with any standard score, Intelliscore PlusSM / Small Business Credit ShareSM

Identify, deploy key variables

► Combined trade count

► Combined trade balance

► DBT (days beyond terms)

► Years in file

► Recent high credit

► Derogatory legal count

Optimize bad identification with decision paths and resulting decision nodes


Decision tree exampleOverall 1.19% write-off rate

Start with portfolio and Intelliscore Plus (IP), bad rate of 1.19%

Score range of 17-37, resulting bad rate of 1.94%

Branch off of number of tradelines, 0-2, 2-7, 7+

IP score of 17-27 and 0-2 trades, bad rate now 3.32%

Further branch down to DBT, days beyond terms, of 16-36 (below score of 17-27, nbr trades 0-2)

DBT of 16-36, bad rate is 4.61%


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maximizing predictive performance at origination and beyond! › assets › decision-analytics ›...

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