one-to-one customer risk management

One-to-One Customer Risk ManagementMinimize card portfolio losses. Maximize returns. And build long-term growth in the midst of a recession.

A First Data White Paper

© 2009 First Data Corporation. All trademarks, service marks and trade names referenced in this material are the property of their respective owners.

By Krista TedderProduct Owner, Risk Management and Fraud Solutions

A First Data White PaperOne-to-One Customer Risk Management A First Data White Paper

page 2© 2009 First Data Corporation. All rights reserved. firstdata.com page 2firstdata.com

Executive Summary As financial institutions large and small grapple with the challenges of the

most severe economic crisis since the Great Depression, the approaches these

institutions take toward retail credit card customer risk management can have a

profound effect on their success in weathering the current storm—and in fostering

profitable customer relationships for the future. By employing the best practices

and advanced analytical tools of One-to-One Customer Risk Management across

the credit life cycle, institutions can minimize defaults on current portfolios and

maximize retention of profitable customers for long-term growth.

The credit card debt crisis that BusinessWeek (Oct. 9, 2008) characterized as “the

next meltdown” did not self-generate overnight, nor will it disappear quietly or

quickly. Rather, it is one symptom of much broader economic problems created

in large part by the “more credit for everyone” philosophy that dominated the

previous decade.

Today, consumers and financial institutions alike are paying the price for

these mistakes.

Mortgage foreclosures, which topped 2 million in 2008, could near 3 J

million in 2009 (U.S. Census and the National Association of Realtors®)

Credit card charge-offs are likely headed for historic highs. Moody’s J

Investors Service has predicted charge-offs as high as 8.5 percent by the

end of 2009, compared to recent historical averages of 4 to 5 percent

Each of these disturbing trends also affects, and is affected by, the wide variety

of factors that have contributed to the national and global recession, such as

increasing unemployment and the bursting of the housing bubble.

In the face of these economic threats, many financial institutions are tempted to

turn 180 degrees from “more credit for everyone” to what now seems like “no

credit for anyone.” It’s a classic example of overcorrection, driven by a fear of

record charge-offs and decreasing interchange revenue and aimed at minimizing

short-term losses.

A First Data White PaperOne-to-One Customer Risk Management

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And as with most cases of overcorrection, this one is not likely to achieve the

desired results. Instead, First Data recommends a more thoughtful, forward-

looking approach. We call our approach One-to-One Customer Risk Management,

a “back to basics—but better” methodology that combines proven credit-risk

policies with advanced analytics and targeted communications, applied at each

phase of the credit life cycle: acquisition, maintenance and collection avoidance,

and collection. This new approach can make it possible to not only weather the

storm of the current crisis, but also be prepared to hit the ground running when

the economy turns around.

The Pendulum Swings to Extremes “More credit for everyone” became the unspoken theme of financial markets as residential real estate values inflated through 2007. As these values rose, so did the number and creativity of new consumer and financial institution debt products: zero-down-payment, interest-only mortgages; low-interest home equity loans; secondary and tertiary markets for debt (the subprime shell game collapse would eventually send the economy into its current tailspin).

The results: The ratio of U.S. credit market debt vs. gross domestic product (GDP), which had averaged about J150 percent from 1936 through 1980, rose to 374 percent in 2008, a total debt of almost $50 trillion vs. about $14 trillion GDP (Data: Innovest)

Home ownership increased from 64 percent in 1994 to over 70 percent in 2006 as subprime Jmortgages increased from $35 billion to $600 billion, rising from 5 percent of total loans to 20 percent in the same period (U.S. Census Bureau and the National Association of Realtors®)

When the housing bubble burst, it took large segments of the economy with it, beginning with the Jcollapse of hedge funds and proceeding through widespread foreclosures, stock market losses, business failures ... the list goes on and on—and now includes BusinessWeek’s “meltdown” in credit card debt

Figure 1: Ratio of U.S. Credit Market Debt vs. Gross Domestic Product (GDP)

Total Credit Market Debt as a Percent of GDP

375%

350%

325%

300%

275%

250%

225%

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

http://www.economagic.com/ Jan 2 2009

Total Credit Market Debt as a Percent of GDP

Source: High-Yield Blog, January 18, 2009, www.highyieldblog.com


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In response, there are already signs that the pendulum is swinging to the opposite extreme. First Data is seeing credit card issuers take such draconian steps as reducing credit lines across the board, closing all inactive accounts, ceasing all new-customer acquisition activities, quickly cutting off non-paying customers, raising fees and generally focusing on cost cutting rather than revenue generation.

Such overcorrections are widespread. The market research organization Mintel Comperemedia reported in August 2008 that the number of credit card offers mailed to Americans in the third quarter of 2008 dropped to its lowest point in more than three years—and 28 percent lower than the same quarter in 2007. A prominent Oppenheimer & Co. analyst has said, “We expect ... credit card lines to decline by 45 percent” by mid-2010.

Many financial institutions have attempted to deal with higher delinquency rates by initiating sweeping changes across large portions of their portfolios. Perhaps the most damaging activity has been the wholesale reduction of credit lines in both active and inactive accounts. Reducing credit lines reduces credit scores, increases interest rates, increases minimum payment amounts and decreases the cardholders’ ability to pay.

Figure 2: The Vicious Circle of Overcorrection

The Vicious Circle of

Overcorrection

STEP 3

A decrease in the FICO score causes other accounts to move into default pricing, increasing the second account’s minimum payment amount.

STEP 2

The available balance and “open to buy” (the amount of credit available on the loan) is re-evaluated by the credit bureaus. The FICO score decreases due to the change.

STEP 4

The cardholder is unable to continue to make the higher minimum payment and defaults on the second account.

STEP 1

The financial institution closes an inactive account and reduces the credit line on a second account that is revolving a balance.

Such “no credit for anyone” measures ignore basic risk-management and business-management principles that are more important than ever in times of crisis.

These principles include:Managing an acquisition process that attracts profitable customers. Such an effort can allow an Jinstitution’s portfolio mix to cleanse itself of toxic accounts without completely drying up in the process

Driving customer trust and retention through multiple banking relationships. Faith in the financial Jsystem, which is at a historic low, is further eroded by cutting off good customers from lines of credit or raising their interest rates. These actions serve to drive away profitable accounts along with the unprofitable ones

Adapting to the shifting behavior of non-paying customers. As more debt goes out for collections, Jless and less is actually collectible. Improved analytics and different customer-handling strategies are required in order to be one of the bills that over-strapped consumers opt to pay



One-to-One Customer Risk Management—A Better WayCan such attempts to quickly curtail losses and reduce risk actually be proven to be a better course of action than attempting to ride out the crisis with status-quo tactics and reliance upon the market to self-correct over time? Or is there a third, more effective choice?

First Data believes there is a better choice, and we have dubbed our recommended approach One-to-One Customer Risk Management (1:1 CRM). This methodology is modeled on the proven, powerful concept of 1:1 marketing, which tailors and promotes a marketer’s products to individuals, based on a thorough understanding of each individual’s capabilities, needs and preferences. The 1:1 CRM approach uses similar principles to create individualized, rules-based decisioning that enables the financial institution to make better-informed choices about the credit it issues to individual customers—and to market those choices more effectively—at each phase of the credit life cycle.

Community bankers have long employed this valuable tool on a smaller scale, tailoring individual credit arrangements for people and businesses whose capabilities, needs and preferences the bankers personally understand.

One result: The Nov. 20, 2008, issue of Bank Systems & Technology reported on research that found consumers trusted community banks 300 to 400 percent more than the nation’s five largest banks. In fact, smaller banks have been much less impacted by the current crisis because, in large part, they did not participate in the subprime lending frenzy. A Nov. 7, 2008, report in the New York Times on one small Vermont bank is representative of what many smaller financial institutions are now experiencing—a veritable boom in loans and deposits as consumers flee to what they perceive as safer ground:

First National Bank of Orwell, Vermont’s smallest bank, founded in 1832, is having its best year in Jrecent memory. Loans are up 22.6 percent from a year ago, and deposits are up 7 percent in the same period. The bank has $36.5 million in assets

Referring to community banks, James F. Gatti, a finance professor at the University of Vermont, Jstates, “If the banks maintained the lending standards that they had established in the past, and had not and did not buy a lot of securities from Fannie Mae and Freddie Mac, they’ve got no problems and they’re sound”

As larger card issuers look to emerge from the current crisis, they could take a few lessons from the little guys. The giant lenders have come to base their credit decisions more on a limited number of specific threshold metrics, such as credit score, and less on a broader understanding of an individual’s circumstances.

Today, newer and more sophisticated analytics make 1:1 CRM a reality—and an indispensable tool that can enable financial institutions to avoid the dangers of blindly following the pendulum when it swings too far in either direction. To demonstrate the benefits of 1:1 CRM, and the negative impacts of the status-quo or overcorrection approaches to riding out the crisis, First Data created a predictive model that highlights the possibilities of each approach for strengthening a hypothetical portfolio in a deep recession.

The model demonstrates that card issuers need to take a measured approach to portfolio cleansing that includes individualized customer risk management across the life cycle:

Continued acquisition activity targeted at profitable customer segments1.

Avoidance of dramatic account maintenance actions that drive away good customers along with the bad2.

Employment of new receivables management models that reflect the changed debt load of the 3. average consumer



While 8.5 to 9.5 percent charge-offs are historically high, they still reflect only a small minority of customers in a card portfolio. The model illustrates just how damaging “throwing out the baby with the bath water” can be to the long-term health of an issuer’s business.

The model is based on a number of key assumptions, each rooted in well-established financial and marketing principles, as well as First Data’s industry-leading risk management experience and insight. Some key assumptions are summarized in Figure 3.

Figure 3: First Data Credit Risk Management Model Assumptions

Risk Management Approach Assumptions Status-Quo Overcorrection1:1 Customer Risk

Management

New Account Acquisition

1:1 CRM gains a substantial growth advantage by actively

seeking new, profitable accounts while other models cut

marketing spending and limit or eliminate acquisition activity.

1% 0.3% 2.3%

Churn

While status-quo watches churn increase due to the

recession, overcorrection drives added churn by enacting

widespread credit line limitations, rate increases and

cancellations. 1:1 CRM attempts to limit churn to the most

undesirable customers via targeted portfolio culling and

retention activities.

-3% -8% -0.5%

Inactive to Active

1:1 CRM seeks to identify and reactivate potentially

profitable inactive accounts. Other models are focused on

removing inactive accounts from their books.

0% 0% 2%

Defaults/Charge-Offs

Charge-offs grew throughout 2008 and can easily reach 6%

in 2009 if nothing is done. Overcorrection drives elevated

default rates by failing to adapt collections practices to the

current situation. 1:1 CRM works to limit charge-offs to 5%

with improved collections targeting and customer-centric

payment arrangements.

6% 8.75% 5%

New Base at the End of the Month

1:1 CRM dramatically reduces shrinkage while it works to

improve the quality of the customers who are retained and

acquired.

-8% -16.45% -1.25%

With these assumptions in mind, the First Data model clearly demonstrates the value, both immediate and longer term, of 1:1 CRM over both the status-quo and overcorrection approaches to risk management. The results are outlined in Figure 4. For a more detailed view of the model’s results, refer to Appendix A.



Figure 4: First Data Credit Risk Management Model

Status-Quo ScenarioMonth 1 Month 2 Month 3 Month 4 Month 5 Month 6

Starting Base 1,000,000 920,000 846,400 778,688 716,393 659,082

New Accounts 1.0% 1.0% 1.0% 1.0% 1.0% 1.0%

Churn 3.0% 3.0% 3.0% 3.0% 3.0% 3.0%

Default 6.00% 6.00% 6.00% 6.00% 6.00% 6.00%

Ending Base 920,000 846,400 778,688 716,393 659,082 606,355

Total Revenue $29,009,000 $26,688,280 $24,553,218 $22,588,960 $20,781,843 $19,119,296

Overcorrection ScenarioMonth 1 Month 2 Month 3 Month 4 Month 5 Month 6

Starting Base 1,000,000 815,000 664,225 541,343 441,195 359,574

New Accounts 0.3% 0.3% 0.3% 0.3% 0.3% 0.3%

Churn 10.0% 10.0% 10.0% 10.0% 10.0% 10.0%

Default 8.75% 8.75% 8.75% 8.75% 8.75% 8.75%

Ending Base 815,000 664,225 541,343 441,195 359,574 293,053

Total Revenue $32,918,668 $27,353,715 $22,776,277 $19,007,026 $15,899,538 $13,334,229

1:1 Customer Risk Management ScenarioMonth 1 Month 2 Month 3 Month 4 Month 5 Month 6

Starting Base 1,000,000 987,500 975,156 962,967 950,930 939,043

New Accounts 2.3% 2.3% 2.3% 2.3% 2.3% 2.3%

Churn 0.5% 0.5% 0.5% 0.5% 0.5% 0.5%

Inactive to Active 2.0% 2.0% 2.0% 2.0% 2.0% 2.0%

Default 5.00% 5.00% 5.00% 5.00% 5.00% 5.00%

Ending Base 987,500 975,156 962,967 950,930 939,043 927,305

Total Revenue $34,537,901 $33,867,227 $33,224,053 $32,606,505 32,012,856 $31,441,514

The Ills of Overcorrection

The model demonstrates that even with the status-quo approach to risk management, the financial institution is substantially weaker after just six months. The lack of proactive acquisition and retention activity in an era where customers are fleeing faltering financial institutions en masse results in significant portfolio degradation. The apparent inaction is actually a negative action that has long-term impact on the business.

The six-month results for the overcorrection approach are even worse, revealing problems such drastic reactions to the credit crisis can create.

Initially, overcorrection seems to be better than taking no action at all, but after only three months the instant gratification of quick profit and wholesale risk reduction begin to take their toll. Brand integrity, consumer confidence and strategic risk management that yield growth are nonexistent, and the model reveals a whole new set of problems that can extend the depth and length of the institution’s own “recession.”

These additional problems start with the many customers who are already nervous about their finances, short on trust in financial institutions and consequently at risk of loss to a competitor. Cutting off such customers’



credit, slapping them with increased interest rates or imposing larger fees will almost certainly jeopardize current—and future—relationships with those customers. Not reflected in this model, but an equally clear risk, is the loss of other lines of business (DDA—demand deposit accounts, mortgage, etc.) from customers dissatisfied with their treatment as credit cardholders.

Each of these factors leads to what may seem at first a counterintuitive conclusion. Raising rates and fees in the name of risk management is likely to actually reduce revenue from interest, fees and interchange because such actions will drive away good customers who can obtain credit elsewhere, while marginal customers are forced to hold onto the accounts they have, in effect increasing the portfolio’s risk level.

In its 2008 report, “10 Trends That Will Shape Financial Services in 2009,” Javelin Strategy & Research asserts, “The war for deposits is on.... The ability of banks and credit unions to buttress their deposit base will go a long way toward determining which ones thrive, survive or fail as the banking crisis deepens.”

Failing to take advantage of opportunities to acquire new customers—especially profitable customers who have been alienated from their banks—simply compounds the problem. As the model illustrates, eliminating unprofitable customers without acquiring profitable ones cuts into revenues more and more as the recession drags on.

Compared to the pitfalls of overcorrection described above, the 1:1 CRM approach substantially reduces the risk of alienating good customers with short-term risk-reduction strategies that should be focused only on poor risks. It enables selective portfolio cleansing and supports improved targeting of new, high-quality customers. And 1:1 CRM helps institutions maintain valuable customer relationships during the current crisis as a solid foundation for future up-selling and growth opportunities when the economic conditions improve.

Implementing 1:1 CRM PrinciplesSo, how can a financial institution put 1:1 CRM to work? Three common principles guide successful 1:1 CRM implementation and address key challenges in marketing analytics across financial institutions. First, banks need to adjust their existing marketing models to better reflect current risk tolerance. They also need to adopt new analytics tools to help identify behavioral triggers to predict when existing customers become too risky, which customers are likely to become inactive and which might need an incentive to restart or increase spending. Success in the first step requires better leverage of behavioral information, transaction data and payment data.

Figure 5: Three Steps to Improved 1:1 CRM

Behavior- Based Predictive Modeling1

STEP

2STEP Improved

Campaign Development Processes 3

STEPOffer and Channel Refinement

The next step to successful 1:1 CRM focuses on the campaign management process. According to a November 2008 Aite Group survey (“Marketing Analytics Trends in Retail Financial Services”) of 24 retail financial services firms, database marketing efforts are hindered by slow cycle times and inflexibility:

63 percent of survey respondents believe the process for building and implementing new models Jis broken or challenged

62 percent of respondents claim their model development cycle is always or usually too long J

59 percent of respondents report they are never or infrequently able to conduct quick and accurate Jcampaign scenarios



Tools and techniques exist to facilitate faster data aggregation, campaign design, model creation and campaign execution. Process improvement must also leverage cross-channel information and bridge the gap between marketing and risk management.

The third key step to implementing successful 1:1 CRM focuses on driving campaign effectiveness by improved offer relevancy. This scenario is based on a better understanding of individual cardholder habits and maximization of efficient communications channels.

1:1 CRM for Cardholder AcquisitionAs reflected in the model, it is critical to keep the first step of the credit life cycle in place during times of crisis. Continuous “good customer” acquisition efforts are needed in order to maintain profitable customer accounts and begin the process of portfolio cleansing. That being said, financial institution marketers are increasingly frustrated with falling direct-mail response rates, which have dropped from approximately 1.4 percent in 1995 to 0.6 percent in 2008, driven by penetration saturation and ever-increasing mail volumes (Mintel Comperemedia, 2008).

Figure 6: Declining Credit Card Growth

7,000

6,000

5,000

4,000

3,000

2,000

1,000

1995 1996 1997 1998

Mail Volume (millions)

1999 2001 2002 2003 2004 2005 2006 2007 2008Projected

2000

0

1.6%

1.4%

1.2%

1.0%

0.8%

0.6%

0.2%

0.4%

0.0%

Average Response Rate (%)

Source: “Declining Credit Card Growth 2008: A (Leaking) Glass Half Full,” Mercator, November 2008

To implement the 1:1 CRM across the acquisition process, banks can start by better incorporating current risk-profile principles into their target marketing—in other words, don’t spend money soliciting card applications they wouldn’t want to approve. Existing acquisition models should be modified to focus less on simple response rates and more on good customer response.

A back-to-basics approach must influence this shift in targeting strategy. Rules-based campaign tools can assess prospective cardholders’ ability, stability and willingness to pay. Such tools need to look well beyond credit bureau scores. These basic scores are useful to identify extremely qualified or unqualified applicants, but a significant portion of potentially profitable customers fall in a “gray zone” just above or just below the average cutoff score. Card issuers can maximize the efficiency of their marketing investment by using tools that better assess those gray zone customers by analyzing more detailed data such as geography, demographics, mortgage status, medical payments, job changes, address changes and check-writing history, plus other key information that can make a customer appear more or less risky.

For example, individual reviews of applicants with previous bankruptcies must recognize that all bankruptcies are not the same. Approximately 1 million Americans file bankruptcy each year due to medical expenses



(“Short on Money, Will Your Customers Pay Your Bills?”, Online Resources Corporation, December 2008). They have the ability, and the desire, to repay standard debts, but not hundreds of thousands of dollars of medical bills. Treating the bankruptcy market sector differently in an acquisition rules-based model can enable card issuers to accept profitable, long-term customers.

Improved targeting is the first step to successful 1:1 CRM implementation for acquisition; but as all marketers know, campaign success is about delivering the right offer to the right customer at the right time. In a November 2008 Aite Group survey of several top 100 U.S. credit card issuers (“Overcoming Challenges of Credit Card Issuing”), respondents rated Reward Strategy as the single most important variable in acquiring loyal cardholders, with product customization, distribution channels and fees/rates close behind. The growth of merchant-funded rewards programs affords banks a great opportunity to differentiate their programs and target rewards based on individual cardholder habits while reducing expenses associated with reward redemption or increasing the value of customer rewards.

Figure 7: Overcoming Challenges of Credit Card Issuing

Q: On a scale of 1 (not at all important) to 5 (extremely important), how important will the following strategies be to your institution in acquiring loyal cardholders in the next 24 months? (n=12)

Reward strategy 12

Product customization 8

Distribution channel strategy 8

Fees/rates 8

Segmentation and targeted marketing 8

Customer service/problem resolution 7

Features other than rewards 5

Transparency and clarity of billing processes and policies 4

Online/mobile banking capabilities 3

Co-Brand strategy 3

Billing and payment processing 2

Important to Not at All Important Extremely Important to Neutral

Source: “Overcoming Challenges of Credit Card Issuing,” Aite Group, November 2008

Mastering an extended channel strategy is trickier for card issuers whose acquisition models have long been overly dependent on direct-mail solicitation. With customers fleeing larger banks and all financial institutions scrambling to react, those who can best leverage cross-channel/cross-product customer communications

4

4

4

4

7

8

9

9

10

5



will win out. For acquisition strategy, this means investing more effort into offering cross-platform rewards, targeting DDA customers with appealing card offers and using branch locations, Internet banking, ATM and Interactive Voice Response (IVR) platforms to deliver cross-sell messages.

1:1 CRM for Account MaintenanceIn phase two of the credit cycle, account maintenance and collection avoidance, 1:1 CRM reassesses the health of an institution’s credit card portfolio and proactively manages a two-front battle. On one front, systems must be in place to closely monitor credit risk and address customers who are either showing signs of trouble or already at risk. On the other front, in order to maximize portfolio value, issuers need to actively convert inactive accounts, retain customers at risk of churn and drive increased activity among loyal customers.

Behavioral modeling, accessing traditional data sources such as demographics and credit history, as well as less traditional data like spending patterns and payment history, can drive big improvements in risk assessment and the ability to predict churn. It can also be invaluable in targeting offers that appeal directly to individual customers based on purchase history. Aite Group’s survey on marketing analytics declared that only a minority of financial institutions are currently using behavioral data in their account-maintenance and collection-avoidance processes, although many expect to increase spending for this type of information over the next three years.

Figure 8: Rating of Card Issuers’ Activation Strategies

Q: On a scale of 1 (not at all important) to 5 (extremely important), how successful do you expect the following activation strategies to be in the next 24 months? (n=12)

Offer attractive rewards 9

Make special double or triple rewards offers 8

Offers from merchants (directly or through vendors or card networks) 5

Balance consolidation 5

Live agent or automated voice-mail reminder 4

Promote use of card for recurring billing 1

Mail reminder 1

E-mail reminder 1

Offer a product that can only be billed through your institution’s credit card

Successful to Extremely Successful Don’t Use Not at All Successful to Neutral

Source: “Marketing Analytics Trends in Retail Financial Services,” Aite Group, November 2008

3

12

7

3 1

6 1

6 2

10 1

10 1

10 1



The example below demonstrates how transactional data can help card issuers identify customers most likely to turn inactive and target them with incentives to stay. The same techniques can be applied to predetermine those customers most likely to become credit risks or to be strong candidates for debt consolidation activity, enabling the bank to take proactive steps.

Facing pressure to reduce escalating customer attrition while conserving scarce marketing resources, a regional bank engaged First Data to execute a customer tempo analysis to identify which customers to target and customized marketing offers that would be relevant and effective.

First Data worked with the bank to construct a predictive model. Analyzing historical transaction data over a two-year period for 2 million accounts enabled an attrition score to be assigned to each current cardholder—a percentage expressing the probability that a particular customer would cancel or inactivate his or her account during the next 60 days.

This analysis produced attrition scores for each cardholder and yielded valuable insights that could be used to design effective promotional offers to retain customers with the highest risk of attrition. For instance, the results showed that customers with low usage of their cards at gas stations were more likely to cancel their accounts than customers with moderate card usage at gas stations. More interestingly, however, the analysis revealed that cardholders with high usage at gas stations were also comparatively more likely to cancel their accounts—a counterintuitive finding that prompted the bank to target these high-value cardholders with gasoline promotional offers.

The bank applied aggressive, highly-customized promotional efforts to those accounts most likely to be closed during the following 60 days. It placed the strongest focus on the 10 percent of customers with the greatest attrition likelihood, segmenting them into categories based on demographics and spending habits. Targeting its most at-risk customers permitted the bank to focus its resources where they mattered most, and segmenting these customers allowed the bank to develop effective marketing campaigns to increase card usage and reduce the likelihood of attrition.

Preliminary results indicate this approach was a success. The bank expects to increase card usage by 8 percent, alongside a 6 percent drop in attrition.

Finally, automated communications tools are available to cost-effectively help manage credit risk. Phone calls, personalized statement messages, e-mail and SMS (short message service) alerts are useful to drive account activation, notify customers approaching credit limits and remind customers with late-payment habits. Loan officers can be assigned to review existing accounts (both active and inactive) and initiate contact with customers to update demographic information and determine overall loan health. Some accounts will require risk-management action—lower interest rate programs, fee waivers or even settlements if cardholders make statements on bankruptcy or financial hardship. When risk-management techniques are coupled with customer-service attributes, delinquency rates are as much as 20 percent lower in high-risk portfolios, and customers are able to repay their debt and subsequently remain in good standing. The combination of automation and personal loan review exemplifies the power of 1:1 CRM in strengthening a financial institution’s portfolio.

1:1 CRM for CollectionsCollections, the third step of the credit life cycle, presents yet another counterintuitive condition in this economy. In theory, today’s greater levels of consumer debt should result in a windfall for collection operations whether it is the creditor or collection agencies and debt buyers. But in practice, lower liquidity rates result in lower collection success, driving up the overall cost per dollar collected. This puts increased pressure on collectors to better target their efforts. Increasingly, collection operations are looking to predictive analytics and automation tools, such as “decision engines” and auto dialers and virtual collections via the Web or through IVR technology, to improve collection success rates and prioritize activity. The problems are even greater in certain segments of the industry such as for card issuers; consumers consistently report credit card bills as their lowest priority when settling monthly debts.



Figure 9: Credit Cards Lead the Pack—in Not Getting Paid

Bill Mortgage Insurance Loans Utilities Healthcare PhoneCredit Card

% of

Households

30 Days +

Past Due

5.9% 2.1% 8.3% 8.7% 8.2% 7.7% 11.8%

In the past year, one in three households was contacted by a collections agency and those that have been contacted are paying at much lower rates. But 20 percent of households with delinquent bills have still not been contacted at all. Maximizing right-party contacts for the right cost at the right time is critical for collection agencies struggling to keep down cost per dollar collected. While many tools exist to trace debtors, the tools are not all created equal. Agents cannot afford to spend valuable time researching various data sources for accurate contact information. Rather, agents need tools that can cost-effectively access a wide variety of information sources with a single request.

But tracing debtors is just the tip of the iceberg. Collection operations need to funnel accounts to the right channels including their live agents. Earlier stage receivables can be managed very cost-effectively via automated contact channels such as automated voice messaging, SMS and e-mail. E-mail, with its convenience and relative anonymity, is the preferred communication and payment method of over 40 percent of delinquent consumers, yet only 1 percent of consumers report having been contacted by e-mail to resolve a bill (Online Resources Corporation, December 2008). In fact, few of the collection operations surveyed by First Data actively drive customers to the Web to make collection payments. This situation represents a great opportunity for collectors to reduce cost and improve collection rates.

No matter what tools are being used, effectively leveraging analytics to prioritize collections is the real key to dealing with the current liquidity environment. Operations not currently employing predictive models need to seek vendors with experience to get proven recovery models in place immediately. Off-the-shelf marketplace tools can enable organizations to determine which accounts to target and even establish which payment offers or settlement offers to make because these accounts are otherwise unlikely to pay and/or destined to roll to charge-off. In an Intelligent Results case study, one large financial institution was able to improve annual returns by $2.1 million with this solution, enabling the institution to become much more aggressive in making settlement offers as early as 90 days delinquent.

These off-the-shelf tools not only can help target accounts, but they also can help facilitate the execution of the targeting by augmenting the “rules engines” systems of an organization. All automated collection systems have some sort of rules engine that use “if-then” logic to distribute accounts through the workflow process. The better the rules engine, the more efficient and effective the process. The number one barrier to successful use of rules-engine augmentation tools, though, is a lack of internal IT resources to make changes in a timely fashion. Attempts have been made to reduce the complexity of these rules engines and make it easier for the institutions’ internal operations teams to work on the systems.

One approach has been to leverage predictive analytics to refine and improve upon the human’s experiential and intuitive insight that helps them to make the decision on what action to take. These traditional analytics solutions have continuously proven to be more accurate than a human at predicting behavior. However, they have not been effectively integrated with other tools for cost-benefit evaluations of the potential actions (including the opportunity cost of spending time on one account vs. another) so the resulting “scores” still need to go through the rules engine to complete the decision. As a result, predictive analytics have been successfully leveraged by only a small percentage of operations.



What has been missing, at least until just recently, is a “decision engine.” This is a software tool that the typical operations manager or business analyst can use that combines the most advanced predictive analytics with a business analysis tool and a rules engine. A human is still needed to set the strategy and establish the overall business objectives, but the system does the evaluation of each individual account. Additionally, decision engines can leverage certain benefits of automation:

Increased accuracy of predictive analytics by minimizing human error J

Increased productivity by evaluating a larger number of accounts J

The goal of a decision engine is to automate much of the decisioning on an account in a way that dramatically simplifies the rules engines in the downstream systems, thus minimizing the internal IT resources needed. Decision engines are now available off-the-shelf and can be used by an organization’s internal operations team to effectively leverage predictive analytics, dramatically improving the ability to target accounts, which leads to improved collections performance.

No Time to Be TimidAs Javelin noted in its “10 Trends That Will Shape Financial Services in 2009” report, “Historic times call for historic actions.” This is not a time for status-quo inactivity, nor will it be sufficient to simply “cut, cut and cut some more.”

First Data’s predictive model demonstrates the value of 1:1 CRM and its basic principles: behavior-based predictive modeling; improved campaign development processes; and offer and channel refinement. When these principles are applied across the credit life cycle, financial institutions can minimize defaults and maximize acquisition and retention of profitable customers for long-term growth. Such a commitment to prudent, decisive action can drive an institution’s success for years to come.



Appendix A: First Data Credit Risk Management Model

Status-Quo Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Total

Assumptions Starting Base 1,000,000 920,000 846,400 778,688 716,393 659,082

New Accounts 1.0% 1.0% 1.0% 1.0% 1.0% 1.0%

Churn 3.0% 3.0% 3.0% 3.0% 3.0% 3.0%

Default 6.00% 6.00% 6.00% 6.00% 6.00% 6.00%

Ending Base 920,000 846,400 778,688 716,393 659,082 606,355

% Revolving 55% 55% 55% 55% 55% 55%

Average Balance $3,300 $3,300 $3,300 $3,300 $3,300 $3,300

Average APR 14% 14% 14% 14% 14% 14%

Revenue Fees $6,140,000 $5,648,800 $5,196,896 $4,781,144 $4,398,653 $4,046,761 $30,212,254

Interchange $3,388,000 $3,116,960 $2,867,603 $2,638,195 $2,427,139 $2,232,968 $16,670,866

APR $19,481,000 $17,922,520 $16,488,718 $15,169,621 $13,956,051 $12,839,567 $95,857,478

Total $29,009,000 $26,688,280 $24,553,218 $22,588,960 $20,781,843 $19,119,296 $142,740,597

Overcorrection Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Total


New Accounts 0.3% 0.3% 0.3% 0.3% 0.3% 0.3%

Churn 8.0% 8.0% 8.0% 8.0% 8.0% 8.0%

Default 8.75% 8.75% 8.75% 8.75% 8.75% 8.75%

Ending Base 835,000 697,225 582,183 486,123 405,912 338,937

% Revolving 53% 53% 53% 53% 53% 53%

Average Balance $3,800 $3,800 $3,800 $3,800 $3,800 $3,800

Average APR 17% 17% 17% 17% 17% 17%

Revenue Fees $7,380,000 $6,587,300 $5,891,396 $5,279,035 $4,738,937 $4,261,479 $34,138,147

Interchange $2,285,360 $1,908,276 $1,593,410 $1,330,497 $1,110,965 $927,656 $9,156,165

APR $23,823,942 $19,892,991 $16,610,648 $13,869,891 $11,581,359 $9,670,435 $95,449,265

Total $33,489,302 $28,388,567 $24,095,453 $20,479,424 $17,431,261 $14,859,570 $138,743,577

1:1 Customer Risk Management Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Total


New Accounts 2.3% 2.3% 2.3% 2.3% 2.3% 2.3%

Churn 0.5% 0.5% 0.5% 0.5% 0.5% 0.5%

Inactive to Active 2.0% 2.0% 2.0% 2.0% 2.0% 2.0%

Default 5.00% 5.00% 5.00% 5.00% 5.00% 5.00%

Ending Base 987,500 975,156 962,967 950,930 939,043 927,305

% Revolving 57% 57% 57% 57% 57% 57%

Average Balance $3,500 $3,500 $3,500 $3,500 $3,500 $3,500

Average APR 15% 15% 15% 15% 15% 15%

Revenue Fees $5,172,000 $4,868,400 $4,587,711 $4,328,117 $4,087,948 $3,865,667 $26,909,844

Interchange $4,740,120 $4,680,869 $4,622,358 $4,564,578 $4,507,521 $4,451,177 $27,566,622

APR $24,625,781 $24,317,959 $24,013,984 $23,713,810 $23,417,387 $23,124,670 $143,213,591

Total $34,537,901 $33,867,227 $33,224,053 $32,606,505 $32,012,856 $31,441,514 $197,690,057



Appendix A (Continued): First Data Credit Risk Management Model

Assumptions

1,000,000

Status-Quo Overcorrection 1:1 CRM

Base Accounts Monthly Growth Vol. Monthly Growth % Monthly Growth Vol. Monthly Growth % Monthly Growth Vol. Monthly Growth %

"+" New Accounts 10,000 1.00% 2,500 0.25% 22,500 2.25%

"-" Churn 30,000 3.00% 80,000 8.00% 5,000 0.50%

"+" Inactive to Active - 0.00% - 0.00% 20,000 2.00%

"-" Default 60,000 6.00% 87,500 8.75% 50,000 5.00%

New Base 920,000 835,000 987,500

% Revolving Credit 55% 53% 57%

Average Balance $3,300 $3,800 $3,500

Average APR 14% 17% 15%

Revenue From Fees $6,140,000 $7,380,000 $5,172,000

Revenue From Interchange $3,388,000 $2,285,360 $4,740,120

Revenue From APR $19,481,000 $23,823,942 $24,625,781

Total Annual Revenue $29,009,000 $33,489,302 $34,537,901

firstdata.com

The Global Leader in Electronic Commerce

First Data powers the global economy by making it easy, fast and secure for people and businesses around

the world to buy goods and services using virtually any form of payment. Serving millions of merchant

locations and thousands of card issuers, we have the expertise and insight to help you accelerate your

business. Put our intelligence to work for you.

About The AuthorAs product owner for Risk Management and Fraud

Solutions for First Data, Krista Tedder is responsible

for the development of risk management, fraud and

collection solutions that meet the market challenges

faced by financial institutions. During her tenure with

First Data, Krista has consulted with financial institutions

to mitigate risk exposure while improving cost control

and customer relationship management. Prior to joining

First Data, Krista spent five years with MBNA in various

positions focused on risk management and fraud.

For more information, contact your First Data Sales Representative or visit firstdata.com.

© 2009 First Data Corporation. All rights reserved. All trademarks, service marks and trade names referenced in this material are the property of their respective owners.

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First Data’s One-to-One Customer Risk Management Solutions

Improved Data Sources*

Use multiple data sources to help assess risk J

Gain insight to determine the likelihood of acquisition, J

upsell and payment

Access data to identify fraudulent applications and J

identity theft

Simplify and improve right party contact rates J

View the total value of the relationship J

Risk & Analytics

Create predictive models forecasting overall J

credit worthiness

Identify consumers “at risk” for non-payment, and J

develop appropriate strategies

Drive increased customer usage, cross-sell and J

retention based on transaction patterns and risk

Prioritize collection efforts using predictive modeling J

Communication Tools

Create customer alerts based on key J

behavioral triggers

Manage costs using automated voice messaging, J

e-mail, SMS and targeted messaging

Enable customer self-service via Web/IVR J

Payment Processing

Minimize the cost and complexity of accepting J

multiple payment types

Accept any form of payment J

Set up monthly deductions J

Customer Management Systems

Improve automated marketing and risk decisioning J

Identify trends, such as bust-outs, and J

modify your strategies

Link accounts to help make decisions J

Many organizations are feeling the impact of the current credit crunch, which continues to reverberate throughout the financial industry. As your partner, First Data is here to help.

Solutions Guide

For more information, contact your First Data Sales Representative or visit firstdata.com.

First Data offers a host of solutions to help you address the wide range of portfolio management issues covered in the accompanying white paper, “One-to-One Customer Risk Management,” including continued quality cardholder acquisition, existing account risk management and growth, and collections.

*Please refer to the charts on the following pages. All charts align with the solutions highlighted on this page.

First Data wants to talk with you about your specific challenges. Whether it’s a risk issue, how to better evaluate data, what to look for during the application process, or how you can address “at-risk” accounts, First Data has the tools and consultative expertise to work with you.

A Global Leader in Electronic Commerce


firstdata.com

Solutions to Assist with the Current Credit Environment

Cre

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Acq

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Customer Objectives by Life Cycle of an Account Data SourcesRisk &

AnalyticsCommunication

ToolsPayment

Processing

Customer Management

Systems

We need to do better risk assessment on prospect accounts. • • • •We need additional data sources, other than traditional bureaus, to evaluate consumer risk. • •Our goal is to determine what the “right” offer is for the consumer. • • • •We want tools that allow for automated decisioning. • • • •We need to identify fraudulent applications and prevent identity theft. • •We need to improve campaign response rates. • • •We need to be faster and more flexible in designing and executing acquisition campaigns. • • •We need to drive automated payment and e-statement usage at time of acquisition. • • •

1:1 CRM for Cardholder Acquisition

Cre

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Ris

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ToolsPayment

Processing

Customer Management

Systems

We need to proactively identify “at risk” consumers and develop targeted strategies. • • • •We don’t want to jeopardize valued customer relationships when making credit decisions. • • • •We need to avoid bust-outs. This is something we would consider outsourcing. • •We need to prevent check kiting. • •We want to focus on portfolio segmentation to help reduce the potential for charge-offs. • • • •

1:1 CRM for Account Maintenance

Solutions Guide

firstdata.com


Cu

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ete

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an

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ToolsPayment

Processing

Customer Management

Systems

We need to understand transaction patterns and attrition risk to drive increased customer usage, cross-sell and retention. • • • •We want to convert inactive accounts to active accounts. • •We need to market based on a total view of the customer relationship. • • • •

Cu

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s We need to improve the rate of collection by handling customers in a way that is appropriate to their situation. • • • •We want the ability to reach out to consumers through a variety of channels. •We need tools to drive down the cost of customer communications and minimize live calls. • •We need to integrate analytics and communications tools for end-to-end automation of customer event-driven communications. • • • • •

Car

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Pro

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/Fe

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We want consumer data at our fingertips in order to adjust the pricing on accounts where we understand the level of risk. • • •Our repayment programs have to take into account negative amortization. • •

1:1 CRM for Account Maintenance


Solutions Guide

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Pay

me

nt

Pro

cess

ing



ToolsPayment

Processing

Customer Management

Systems

We need to be able to accept multiple payment types and through automated solutions 24/7. •Our agents need to be able to assess payment risk and accept payments of all sorts over the phone. • •We want to streamline our payment processing to reduce operational expenses. •

Co

llect

ions

/Acc

ou

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Re

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Man

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We would be more effective if we could optimize our resources through more intelligent calling queues. • • •Better analytics tools would help us to reduce our net credit losses. •We need easier, faster tools for applying analytics that don’t require extensive IT support. • • •We need to maximize right party contacts. • •We need to funnel early-stage collections through automated communications tools. • • • •

Ban

kru

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y/C

har

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-off

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We want to minimize collections and streamline operations by managing various accounts in one system. •We have resource constraints and need to assess what accounts to send out to collection agencies. • •We need to identify high-risk consumers who have a high likelihood of filing for bankruptcy. • •

1:1 CRM for Collections


Solutions Guide

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