INSIGHTS » Why Real-Time Risk Decisions Require Transaction Analytics Reduce losses with sharper, faster risk prediction and action in customer management and collections The double shock of recession-spurred delinquencies and new regulatory inroads on profit is making creditors acutely aware of the need to make sharper risk distinctions among customers. In both account management and collections, they need precise insights to guide more targeted, timely actions. Every bank with transacting accounts has the potential to achieve this higher level of risk precision. The necessary deep insights into customer behavior can be drawn from credit card and debit card account transaction data. It’s the most consistent and frequently updated source of customer data. By using transaction analytics to analyze this data—and combining the results with the FICO® Score and behavior scores—banks more cleanly separate customers by risk level, especially in moderate risk bands and early lifecycle accounts. Sharper risk separation and real-time or frequent scoring enables banks to quickly and precisely target line changes and timing, and other treatments, to reduce losses and improve profitability. Better insight into risk also enables banks to focus collections resources in ways that reduce charge-offs. This white paper: Demonstrates how combining transaction scores with traditional behavior scores and FICO® • Scores (credit bureau risk scores) increases accuracy of risk predictions Discusses the advantages of deploying transaction analytics in real-time mode to accelerate • awareness of developing risk and enable early intervention to mitigate losses Presents results of three studies showing the increased predictive accuracy, segmentation • granularity and monetary value gained by adding transaction scoring to current methods Number 29 —January 2010 www.fico.com Make every decision count TM FICO® Transaction Scores, generated with every transaction, can be used instantly by FICO® TRIAD® Customer Manager to make real-time decisions
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Why Real-time Risk Decisions Require transaction AnalyticsReduce losses with sharper, faster risk prediction and action in customer management and collections
The double shock of recession-spurred delinquencies and new regulatory inroads on profit is
making creditors acutely aware of the need to make sharper risk distinctions among customers.
In both account management and collections, they need precise insights to guide more targeted,
timely actions.
Every bank with transacting accounts has the potential to achieve this higher level of risk precision.
The necessary deep insights into customer behavior can be drawn from credit card and debit card
account transaction data. It’s the most consistent and frequently updated source of customer data.
By using transaction analytics to analyze this data—and combining the results
with the FICO® Score and behavior scores—banks more cleanly separate
customers by risk level, especially in moderate risk bands and early lifecycle
accounts. Sharper risk separation and real-time or frequent scoring enables
banks to quickly and precisely target line changes and timing, and other
treatments, to reduce losses and improve profitability. Better insight into risk
also enables banks to focus collections resources in ways that reduce charge-offs.
This white paper:
Demonstrates how combining transaction scores with traditional behavior scores and FICO® •
Scores (credit bureau risk scores) increases accuracy of risk predictions
Discusses the advantages of deploying transaction analytics in real-time mode to accelerate •
awareness of developing risk and enable early intervention to mitigate losses
Presents results of three studies showing the increased predictive accuracy, segmentation •
granularity and monetary value gained by adding transaction scoring to current methods
Number 29 —January 2010
www.fico.com Make every decision countTM
FICO® Transaction Scores, generated with every transaction, can be used instantly by FICO® TRIAD® Customer Manager to make real-time decisions
“Transactions will become more important as demand for reduced latency in business intelligence and monitoring require more granular knowledge and timely tracking at the transaction level. Real-time intelligence will be needed in every FSI vertical and across every transaction type from retail deposits and savings to loans, investments, and payments.”
TowerGroup, July ‘09
While top banks have recently increased their use of transaction data, even industry leaders have
yet to realize its full potential for real-time detection of changing risk.
Transaction analytics detect changes in risk as they occur, and generate fresh scores with each
transaction. Yet most banks are processing the scores in batch mode, usually once a month. This
batch processing prevents promotion or intervention with clients at point-of-sale when early
signs of improving or deteriorating performance occur. In addition, many banks are using models
that capture only a portion of the insights that could be extracted from their transaction data.
The means to make sharper risk distinctions in real time
are available today. Banks can take advantage of better
modeling techniques as well as software that enables
deployed models to deliver accurate scores in the
milliseconds it takes for authorization of a credit card
purchase or an ATM withdrawal.
Transaction scoring is the key to making accurate real-
time risk decisions. As TowerGroup recently pointed out,
demand for reduced latency in business intelligence and
monitoring will require creditors to pull more insight from
their transactions.1
The combined use of traditional behavior scores and the FICO® Score to achieve a more accurate
assessment of customer risk has been standard industry practice for many years. The predictive
models that generate these scores analyze different data sources and, consequently, provide
different risk perspectives. Using both perspectives to make customer decisions lifts results above
what can be achieved using either score in isolation.
Transaction analytics lift performance even further because they model an additional rich source
of data and provide an additional risk perspective. Authorizations of credit and debit account
transactions contain abundant detail—the what, when and how much of customer spending—
from which patterns indicative of risk can be drawn.
Some of these patterns are evident in Figure 1, which shows percentage changes in credit card
authorization amounts from a six-month period in 2007–2008 to the same period a year later as
the recession set in.
Sharper Risk »Distinctions in Real Time
1 “Global IT Spending Forecast for Risk Management: A Growth Opportunity for Business and Technology,” TowerGroup, July 20, 2009, # V60:02BE
In this chart, we see changing spending patterns for all consumers (blue bars), perhaps as a result
of economic stress. But accounts that were seriously delinquent by the 2008–2009 period showed
much more pronounced changes (orange bars) during this period. These customers have changed
their spending as the economic cycle has changed, including higher levels of cash advances, and
reduced spending in travel and home improvement.
It’s the combination of spending behaviors that creates a pattern indicative of rising or falling risk,
and sometimes it’s the absence of activity that is most indicative. For example, as consumers become
more risky, their spending shifts away from categories such as home improvement and retail. Single-
event transactional triggers (e.g., the third cash advance in a month) miss these clues to changing risk.
Transaction analytics—models that capture the most indicative characteristics and relationships in
transaction data—detect such spending patterns. As a result, they’re able to separate accounts that
appear the same in cycle-end activity summaries but actually have different levels of risk.
The best models are very sophisticated and very specific. They analyze characteristics that go
beyond whether the consumer bought groceries or gasoline to, for example, where the consumer
bought groceries and what time of day or night the consumer bought gasoline. They capture
additional dimensions of time and space, such as velocity of cash advances and changes in elapsed time for various types of purchases.
–30%
–50%
–10%
10%
30%
70%
50%
90%
Figure 1: Revealing risk in customer spending patterns Percent change in credit card authorization amounts per accountOctober 2008–March 2009 compared to October 2007–March 2008
Because, as shown in Figure 2, transaction patterns are customer-specific, analytic models
must also be able to differentiate between patterns that are unusual for one customer but not for another. Making an ATM withdrawal at 11:30 pm may be a departure from one customer’s
normal spending pattern, and it may indicate a changing risk profile, while it is completely
ordinary for another customer, who works on a night shift.
The most important point to understand about today’s advanced transaction analytics is that
the kinds of risk patterns shown in Figures 1 and 2 are detected as they develop. Indeed, the
best models pick up patterns indicative of changing risk within a handful of transactions.
Because a fresh risk score can be generated with every transaction, it reflects spending patterns
drawn from behavior taking place a day before or even minutes before the current transaction.
Overall Risk Level
Date Time Merchant Degree Of Risk
May 01 4:00 pm
May 09 6:30 pm
May 09 7:15 pm
May 19 5:30 pm
May 27 11:30 am
May 31 12:00 pm
June 01 9:00 am
June 19 6:00 pm
June 19 7:00 pm
June 21 4:30 pm
July 03 5:00 pm
July 08 6:00 pm
July 27 3:00 pm
July 31 12:00 pm
Specialty Retail
Specialty Electronics
High-End Retail
Specialty Grocery
Discount Retail
Petrol
High-End Hotel
Children’s Toys
Specialty Retail
Specialty Retail
Specialty Retail
Petrol
High-End Retail
Fast Food
Date Time Merchant Degree Of Risk
May 01 4:00 pm
May 09 11:30 pm
May 09 7:15 pm
May 19 10:30 am
May 27 11:30 am
May 31 12:00 pm
June 01 9:00 pm
June 19 9:00 pm
June 19 7:00 pm
June 21 4:30 pm
July 03 5:00 am
July 08 6:00 pm
July 27 3:00 pm
July 31 12:00 pm
Specialty Retail
ATM / Cash
Liquor Store
Casino
Discount Retail
Petrol
Casino
Discount Retail
Specialty Retail
Fast Food
Petrol
ATM / Cash
High-End Retail
Fast Food
Overall Risk Level
Figure 2: Customer spending patterns may reveal risk
Better segmentation of accounts for credit limit decreases and other loss-mitigation treatments can
have a significant financial impact, as shown in Figure 6.
Sharper collections treatment decisions
FICO also performed a study on the impact of transaction scoring on the segmentation of delinquent
accounts, using the same 10 million North American credit card accounts as the previous study.
For simplicity and brevity, the charts below show the impact of combined scoring on just one slice
of the accounts: one-cycle delinquent accounts in the middle-scoring 10% (decile 5) of behavior
scores. The study demonstrated particular benefit in moderate score ranges such as this—those
traditional “gray areas” of risk, where portfolio managers may have the least confidence in applying
treatment to accounts.
Figure 6: Estimating the benefit of sharper credit line decrease decisions
Portfolio size 1 million accounts
Lift scenarios (% of accounts swapped-in for line decrease)
Monthly prevented bad $
Annual prevented bad $
Annual benefit per total account
Average bad rate of swap-in population
Average decrease amount per account
Utilization of incremental credit line
Monthly accounts subject to credit line decreases
Conservative: 10% Average: 15% Optimistic: 20%
$39,000
$468,000
$0.47
80%
13%
$750
5,000
$58,500
$702,000
$0.70
$78,000
$936,000
$0.94
Current accounts 900,000 (90%)
Sharper segmentation enables you to prevent losses by avoiding balance build on riskier accounts. The chart below shows representative results in three estimated lift scenarios. The actual benefit you gain will depend on how effective your current segmentation scheme is, as well as your specific policies around credit limit decreases.
It is important to note that these estimates show only one side of the benefit to be gained from more accurate credit limit decrease decisions. In addition to helping with loss mitigation, sharper segmentation also improves profitability. By identifying less risky accounts that can be removed from credit line decrease populations, it prevents unnecessary constraints on account usage and avoids causing dissatisfaction that can lead to the attrition of valuable customers.
Figure 9: Estimating the benefit of sharper collections decisions
Portfolio size 1 million accounts
Delinquency % Balances affected Amount
With transaction scoring (75 basis point improvement in 1-to-3-cycle roll rate)
Baseline performance
1 cycle
1-cycle to 3-cycle roll
3-cycle-to-charge-off
1-cycle to 3-cycle roll
3-cycle-to-charge-off
1 cycle
15% of 1-cycle balances
85% of 3-cycle balances
7.5% of receivables
$19,687,500
$16,734,375
$131,250,000
7.5% of receivables
14.89% of 1-cycle balances
85% of 3-cycle balances
$131,250,000
$131,250,000
$16,608,867
Total receivables $1.75 billion
Sharper segmentation enables better collection prioritization, which results in lower roll rates for early-stage delinquencies. The chart below shows representative results from transaction scoring for a standard portfolio of 1 million accounts. By reducing the 1-to-3-cycle roll rate from 15% to 14.89% (compare the rows highlighted in green), transaction scoring reduces annual charge-offs by $1.5 million. The actual benefit you gain will depend on how effective your current collections segmentation scheme is, as well as your specific policies around collections treatments.
The Insights white paper series provides briefings on best practices, research findings and product innovations from FICO. To subscribe, go to www.fico.com/insights.
For banks with transacting accounts, data from transactions is the most consistent and frequently
updated source of information on their customers. Under today’s economic and regulatory
pressures, it is essential for uncovering the additional insights that enable more accurate risk
predictions and sharper decisions on how to treat customers, from account management through
collections.
Most banks, of course, are currently interested in transaction analytics primarily for improving
control of risk. As the economy recovers, however, the opportunities that can be gained through
fine-grained separation of customers by other behavioral dimensions will garner more attention.
Precise decisions—driven by real-time detection of changes in not only risk, but customer needs
and lifestyle—will be essential for extending the right credit at the right time to maximize profit in
the recovering economy.
Learn more: Visit • www.fico.com/cards to learn about FICO’s overall program for Reengineering Card
Profitability, including using transaction analytics to reduce credit losses
Watch a • Tech Talk video interview on real-time risk decisions using transaction analytics
See related white papers at • www.fico.com/insights
Contact u• s to discuss how you can use transaction analytics to reduce credit losses
Better Results on »Both Sides of the Balance Sheet
