How do economic changes impact consumer risk?

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how do economic changes impact consumer risk?Lenders require new analytics to gauge how dynamic market conditions affect riskBy Dr. Andrew Jennings, FICO Chief Research Officer

As the banking industry tries to grasp the lessons of the financial crisis, what stands out is a harsh

reminder of the dynamic nature of risk.

While lenders only saw moderate change in portfolio risk during years of relative economic stability,

in the past year, they have observed major shifts as economic conditions worsened. The industry

was caught unprepared and failed to take timely actions to correct for the impact of the economy

and changing lending practices.

Today’s economic realities call for a paradigm shift in risk management—one that includes new

analytics that go beyond the traditional assumption that past risk levels are indicative of future risk.

In this paper, I’ll discuss this new approach in detail and introduce analytic

capabilities, fresh from the research labs at FICO, that give lenders the ability to

anticipate and adjust more quickly to economic conditions and more closely

align risk strategies with future performance.

Number 26 — November 2009

www.fico.com Make every decision countTM

FICO® Economic Impact Service allows lenders to better predict the impact of economic factors on credit risk, so they can more quickly adjust to a dynamic market. To learn more, watch a Tech Talk video interview with Andrew Jennings.

http://www.youtube.com/watch?v=T3BifSjueDw

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Figure 2: More accurately predict default rates with analytics tuned to economic impactBad rate comparison: overall portfolio

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How do economic changes impact consumer risk?

insights » insights »

To determine where the industry needs to go, it is necessary to understand what current risk

management tools provide.

Lenders rely on scoring models, which assess risk by rank-ordering accounts, to help differenti-

ate their “best” and “worst” customers. These models are applied in decisions across the account

lifecycle, and in provisioning and portfolio valuation, among other areas.

To translate this relative risk measure into a clearer picture of default rates, lenders often look at

the historical risk observed at different score ranges. Lenders use this historical yardstick to make

decisions on how to treat today’s accounts according to their risk/reward preferences.

The assumption that recent default rates will be representative of future defaults works reason-

ably well if the lending environment remains relatively stable. But in situations where external

factors are changing rapidly, such as with the recent credit crisis, it can be dangerous to assume

the risk levels associated with scores will remain stable over time.

To illustrate this point, Figure 1 shows the observed default rate at each FICO® score range for

real estate loans. Each line represents a large random sample of existing accounts evaluated over

different time periods and the default rates one year later.

As the graph shows, the risk levels associated with accounts in 2005 (blue curve) versus 2006 (red

curve) were already diverging. One year later, we see much greater risk levels associated with all

but the very highest scores (green curve shifts more dramatically upward).

Why a New Approach »Is Needed

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Figure 1: Risk levels can shift dramatically over timeDefault rate distributions over time Real estate loans—existing accounts

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In 2005 and 2006, for instance, a default rate of 2% was associated

with a score of approximately 650–670. By 2007, this default rate

was associated with a score of about 710—a 40 to 60 point shift.

This risk shift was most likely due to economic decline after a

period of more lenient lending practices, such as adjustable rate

mortgages and no-documentation (“no-doc”) loans. Lenders in

2007 who made decisions assuming that 670 still represented

2% default saw increases in their default levels, when the actual

default was about 4.5%.

Many lenders do attempt to anticipate changes in economic

conditions and adjust strategies accordingly—for example, tight-

ening origination policies and reducing credit lines in a recession.

But lenders often make these changes judgmentally, and as a

result, there is a tendency to over-correct and miss key revenue

opportunities, or under-correct and retain more portfolio risk

than desired.

Lenders need an objective and empirical approach to measure changing conditions and

translate these into more effective strategies. The next generation of predictive analytics must

go beyond the assumption that only past risk levels are representative of future risk. Instead, risk

estimates should also account for the impact of:

The economy. • Consumers’ ability to repay changes as the economy shifts. Some lower-risk

consumers may refinance in downturns, leaving behind a portfolio of riskier consumers.

Others may reach their breaking points through job loss or increased payment requirements.

Higher-risk consumers get stretched further, resulting in more frequent and severe delin-

quencies and defaults.

Lender strategies. • Changes in lender policies across the account lifecycle can influence

the risk profile of newly booked and existing accounts.

Competition. • As consumers are presented with more (or less) attractive offers by the

competition, attrition will change the population.

More specifically, lenders need analytics that predict the impact of the above factors on future

risk levels for each account. In other words, the analytics must consider the macroeconomic

view of market conditions within the micro-analysis of individual consumer risk. The analytics

must be flexible enough to take into account what is known—the historical data available to

derive past patterns and current economic conditions—as well as what is expected—forecasted

views of the future.

“The global recession has made it an imperative for lenders to rethink their tried-and-true credit risk management practices. Only looking at historical indicators of performance is no longer sufficient. Those who best utilize additional data—for example, to anticipate macroeconomic impact on individual consumer lending decisions—will have considerable competitive advantage.”

—Bobbie Britting, Research Director, Consumer Lending, towergroup

Next Evolution of »Predictive Analytics

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Given the ready availability of economic data, lenders can first take into account the impact of

economic factors on future risk estimates. In addition, lenders should begin to track changes in

their strategy and competitive factors, which can be incorporated into future risk estimates as

the data becomes robust enough.

This is the approach behind FICO® Economic Impact Service, currently available as an analytic

offering from FICO. It provides lenders with an understanding of how the future risk level associ-

ated with scores will change, based on current and projected economic conditions. The service

examines up to 150 different economic indicators, such as unemployment rate, interest rate and

gross domestic product (GDP).

Based on past dynamics, FICO can derive the empirical relationship between the default rates

observed at different score ranges (e.g., the risk score’s odds-to-score relationship) as seen on the

lender’s portfolio, and historical changes in economic conditions. Using this derived relationship,

lenders can then input current and anticipated economic conditions to project the expected

odds-to-score outcome under those conditions.

With such a relationship, it is possible to relate the impact of economic factors on odds, default

rates and scores. This relationship can be derived at an overall portfolio level or more finely for

key customer segments that are expected to behave differently under varying economic

conditions.

This patent-pending methodology builds upon existing risk tools used by lenders, enabling

quick and seamless implementation. It can be applied to a variety of scores, such as origination

scores, behavior scores, broad-based bureau scores like the industry-standard FICO® score, and

Basel II risk metrics.

When applied to these scores, lenders gain an additional dimension to their risk predictions so

they can better:

Limit losses.• Lenders would have greater insight on how to tighten up credit policies

sooner and for the right populations during a downturn.

Grow portfolios competitively.• Lenders could more quickly determine when and how to

proactively loosen up credit policies as markets recover.

Prepare for the future.• Lenders could simulate the impact of future macroeconomic

conditions on scores, to better adjust longer-term strategies and stress-test portfolios.

Meet regulatory compliance.• Lenders could better set capital reserves by creating more

accurate, forward looking, long-run/downturn estimates required by Basel II.

Analytics Tuned to » Future Performance

http://www.fico.com/en/Products/Analytics/Pages/FICO-EIS.aspx

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In practice, FICO® Economic Impact Service generates a more accurate risk prediction that’s

better tuned to economic conditions. For instance, FICO recently applied this methodology to a

behavior score used by a leading US credit card issuer. Figure 2 compares three metrics:

The actual bad rate observed on the portfolio over time (blue line).•

The bad rates predicted by the behavior score aligned to • historical odds performance

(orange line)—traditional approach.1

The bad rates predicted by the behavior score aligned to • anticipated odds performance

(green line)—FICO Economic Impact Service approach. The economic conditions used were

limited to what was known at the time of scoring.

Over a three-year time span, predictions from the FICO Economic Impact Service were on aver-

age 25% closer to the actual bad rate, compared to the traditional prediction.

Next, I’ll highlight some early adopter case studies and validations to illustrate how FICO

Economic Impact Service can be applied across a lender’s business to bring bottom-line impact.

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1Some scores are periodically “aligned” to maintain a consistent odds-to-score relationship over time—for example, to ensure a behavior score of 675 equals a target odds of 30 to 1. Traditionally, behavior scores have been aligned to the odds observed in the last six months.

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Within originations, the lender primarily determines whether or not to accept the loan as well

as the initial loan amount. Using FICO® Economic Impact Service, a lender can set a cutoff score

based on the anticipated future default rate, as opposed to the historical default rate. The lender

can maintain the desired portfolio risk levels by adjusting cutoff scores as the economy changes.

Figure 3 shows how a lender could view the current default rate by score range (orange line)

and predict how the default curve will shift under different economic conditions.

During a recession, the curve may shift to the dark blue line. The lender can update its cutoff

score from the “Current Cutoff” to the “Cutoff–Recession” based on empirical guidance from FICO

Economic Impact Service. This allows the lender to limit losses by proactively tightening credit in

anticipation of the downturn.

Conversely, during a time of economic growth, the curve may shift to the light blue line. The

lender can update its cutoff score to the “Cutoff–Growth.” This allows the lender to loosen credit

policies in anticipation of an economic upturn, and bring in more profitable customers ahead of

competitors.

A similar approach can be taken for initial loan amount and pricing strategies.

Lenders use behavior scores to help manage accounts already on their books for credit line

management, authorizations, loan re-pricing and cross-sell decisions. With the FICO Economic

Impact Service, an “economically impacted” behavior score could be used in place of or along

with the traditional behavior score across the full range of account management actions.

Improve Account » Management Decisions

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Figure 3: Flexibly adjust score cutoffs under different economic conditionsDefault rate under different economic conditions

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Grow Portfolios » Responsibly

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As an example, for a US credit card issuer, FICO retroactively used a FICO Economic Impact

behavior score in place of the traditional FICO® TRIAD® Customer Manager pooled behavior

score for a credit line decrease strategy and for collection actions. The key question was: In April

2008 (a period of relative economic calm), could FICO Economic Impact Service help the lender

anticipate the economic turmoil six months later and minimize its financial impact?

We analyzed performance in October 2008 and compared the different decisions made by the

two scores. Figure 4 shows the line decrease results.

The columns of interest are the “Swap In” and “Swap Out,” since they illustrate where different

decisions would be made. The second column identifies accounts that would have received

decreases using the Economic Impact score but did not receive decreases by the traditional

behavior score (the accounts would be “swapped in” if the lender had used the Economic Impact

score). The third column identifies accounts that the Economic Impact score would not have

decreased and the traditional behavior score did decrease.

In the highlighted cells, we see that the behavior score for these two populations are almost the

same (swap-in: 643 vs. swap-out: 646). In other words, the behavior score identified both popula-

tions as at relatively the same risk level.

However, the FICO Economic Impact score was better able to distinguish risk among these

populations. Using this score, the lender would have decreased more accounts that would be

negatively affected by the downturn (average score of 625). The lender also would have not

decreased accounts less sensitive to the downturn, reflected by slightly higher scores.

The actual bad rates seen six months later reinforces that the Economic Impact score identified

riskier accounts (swap-in: 10.5% vs. swap-out: 7.9%). If the lender had decreased credit lines on

the appropriate accounts, it could have realized a yearly loss savings of roughly $2.4 million and

a net savings of $1.7 million, shown in Figures 5–6.

Figure 4: Economic Impact score better identifies higher-risk accounts for line decreases

Volume of Accounts

Actual Bad Rate

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In times of economic turmoil, it’s even more critical for lenders to proactively manage collection

efforts. FICO Economic Impact Service can be applied on existing behavior or collection scores, such

as FICO® Collection Scores, to help lenders identify which accounts will become riskier and should

receive increased collection priority.

For the same US card issuer, FICO retroactively used an economically impacted behavior score in

place of the traditional behavior score to treat early-stage (cycle 1) delinquent accounts. Prioritizing

accounts by risk, the strategy using the Economic Impact behavior score would have targeted 41%

of the population for more aggressive treatment in April 2008. FICO then examined the resulting bad

rates six months later (October 2008), and saw that these accounts resulted in higher default rates.

In other words, the Economic Impact score better identified accounts that should receive more

aggressive treatment in anticipation of the downturn six months later. Using this strategy, the lender

would have been ahead of its competition in collecting on the same limited dollars.

Figure 5: Yearly loss savings using FICO® Economic Impact Service

Swap In Swap Out

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% of Accounts

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Change in Balance for Bad Accounts(Performance—Observation)

Initial Savings

Average Monthly Savings

Yearly Loss Savings

Figure 6: Yearly net savings using FICO® Economic Impact Service

Observation Balance–Good Accounts

Initial Reduction in Revenue

Assumption: Attrition of 5% of Good Balance

Revenue Yield of 10%

Average Monthly Reduction

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Yearly Net Savings

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Limit Collections Losses »

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Figure 7 illustrates how the lender could have saved close to $4 million by taking aggressive action

earlier. FICO calculated this using the number of actual bads that would have received accelerated

treatment, average account balance and industry roll rates.

Combining this roughly $4 million in collections savings with the $1.7 million savings from the

credit line decrease strategy, the lender would have saved $5.6 million using FICO Economic Impact

Service. This illustrates the aggregate benefits of the service when used across two areas in a

customer lifecycle. Clearly the benefits would be scalable for larger portfolios.

When setting provisions and capital reserves, it is important to understand the risk in the portfolio

under stressed economic conditions. Having forward-looking risk predictions is explicitly mandated

by Basel II regulations, and should be part of any lender’s best practice risk management.

FICO is working with an Eastern European lender to apply FICO Economic Impact Service to its

Basel II Probability of Default (PD) models. Using the derived odds-to-score relationship between its

PD score and economic conditions, the lender can simulate the expected PD at a risk grade level

under various economic scenarios. Thus, the lender can more accurately calculate forward-looking,

long-run PD estimates to better meet regulatory requirements and calculate capital reserves.

Figure 7: Yearly loss reduction using FICO® Economic Impact Service

Yearly # of Cycle 1 Accounts (30 Days Delinquent)

# Bads Receiving More Agressive Treatment

Avg Bad Balance (April 08 Observation Balance for all Bad Accounts)

Assumption: % of Bad Accounts that Charge-Off

Assumption: 3% Additional Bads Cured Due to More Aggressive Treatment

Yearly Loss Reduction $3,958,532

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792

Combining this roughly $4 million in collections savings with the $1.7 million savings from the credit line decrease strategy, the lender would have saved $5.6 million using FICO Economic Impact Service.

Set More Accurate » Provisioning and Capital Reserves

The Insights white paper series provides briefings on research findings and product development directions from FICO. To subscribe, go to www.fico.com/insights.


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FICO , TRIAD and “Make every decision count” are trademarks or registered trademarks of Fair Isaac Corporation in the United States and in other countries. Other product and company names herein may be trademarks of their respective owners. © 2009 Fair Isaac Corporation. All rights reserved.2612WP 11/09 PDF

Redefining Risk »Management Best Practices

With delinquencies reaching unprecedented highs across the industry, there’s no better time for

lenders to reevaluate risk management practices.

Forward-looking analytic tools like FICO Economic Impact Service will become the risk management

best practices of tomorrow. With improved risk predictions better aligned to current and future

expected economic conditions, lenders can more quickly adjust to a dynamic market and steer

their portfolios for the challenges ahead, whether the economy heads towards further constriction,

prolonged retraction or turns the corner toward growth.

Dr. Andrew Jennings is a Senior Vice President and Chief Research Officer at FICO. During his

15-year tenure at FICO, Jennings has worked with many leading banks worldwide, and previously

has held senior risk positions at Barclays and Abbey. Watch his Tech Talk video interview to learn

more about how FICO Economic Impact Service can add value to your business.

http://www.youtube.com/watch?v=T3BifSjueDw

How do economic changes impact consumer risk?

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