An industry snapshot finds leading US issuers exploring new ways to manage the bottom-line impacts of regulation

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1 How Are Issuers Changing Course Under the CARD Act? An industry snapshot finds leading US issuers exploring new ways to manage the bottom-line impacts of regulation Number 42 August 2010 Inside: Find out how issuers can meet the CARD Act ability to pay requirement while boosting profit by nearly 100 basis points In this time of uncertainty and continuing change, the first question on the minds of many credit card issuers is, What are other issuers doing? Issuers are at a pivotal moment, as they emerge from months of almost total absorption in the problems of reducing delinquencies and ensuring compliance with the CARD (Card Accountability, Responsibility and Disclosure) Act of As the impact of these regulations shows up on profit and loss statements, many issuers are starting to take measures to refocus on revenue and growth. A few are making very smart maneuvers for competitive advantage. This paper provides a snapshot of what FICO is hearing from clients, including: Results from a recent FICO survey of credit card issuers on their activity with credit line increases, overlimit authorizations and pricing. A case study of how a major US bank optimized its strategy for setting APR (annual percentage rate) and initial credit line to decrease losses, maintain revenues and improve profit. A look at what issuers are doing to meet the CARD Act ability to pay requirement and how to be both compliant and profitable. Directions industry leaders are moving to improve card industry profitability through greater analytic precision and new business models. Make every decision count TM

2 »» Focusing on Compliance For many issuers, concern over rising delinquencies and the rush to compliance with the CARD Act have been top-of-mind, sometimes at the expense of revenue-generating activity. We hear it in conversations with industry executives: A top risk officer at one of the nation s leading banks said his company had increased credit lines on less than 1% of its portfolio over the past year, and decreased credit lines on just over 1%. According to a vice president of bankcard services at a large regional US bank, the volume of card solicitations going out from his institution dropped by two-thirds. Most of the line and price promotions being mailed were due to contractual requirements with their private label partners. This is from an issuer with predominantly prime and super-prime portfolios! This contraction of revenue-generating activity was confirmed in a recent FICO survey of credit card issuers. In this survey, conducted in March 2010, we asked a range of issuers, both large and small, to tell us what they were doing in three decision areas. We received 40 responses from issuers, and here are some highlights from the results: Credit line increases 5% said they had continued to perform automated credit line increases after 2/22/10; everyone else said they had turned off this functionality to implement new systems that meet regulatory requirements for assessing the customer s ability to pay. 34% are manually processing line increase requests, doing bureau score pulls and requesting income when the customer calls in. More than half of these issuers regard this as a permanent measure. 45% are discussing or testing a bureau-based income estimator solution. Overlimit authorizations 42% stopped allowing customers to go overlimit after 2/22/10. 40% are still allowing some overlimit transactions, with the decision made by account management software, such as FICO TRIAD Customer Manager. 84% said it was not worth the effort to flag overlimit accounts that had not opted into fees. 13% are allowing opt-out accounts to go overlimit without charging a fee. 11% have turned off their authorizations strategy altogether. Pricing 16% turned off pricing functionality as of 2/22/10. 79% had adjusted penalty pricing to comply with CARD requirements. None of them had implemented prospective balance/protected balance pricing, although a handful of respondents had plans to do so. 19% said they intended to implement it; 29% said they did not. The statistics paint a very conservative picture and a grim one for profitability. For the sake of compliance and implementation speed, issuers are sacrificing considerable revenue. In authorizations, for example, one of the results is a spike in declines, that negatively impact revenue through lower utilization and cardholder attrition. page 2

3 »» These revenue losses are adding to those resulting directly from CARD restrictions on interest and fees. The good news, in the case of these secondary revenue impacts, is that issuers can do something about them. Addressing the Revenue Issue We re now seeing a transition from the tight focus on reducing delinquencies and ensuring compliance to a more balanced concern that includes restoring revenues. The right offer up front CARD restrictions on the issuer s ability to change APRs on existing credit cards make originations decisions especially critical. More profitable decisions will come from taking a holistic approach. As one speaker at our recent FICO World conference pointed out: Banks can t continue to put together offers by making sequential, unrelated decisions about APR, credit line, and other terms and conditions. To make profitable offers in the new environment, they need to take multiple factors into consideration simultaneously and make a unified, optimized decision. Also at FICO World was one of the first issuers to implement this kind of holistic approach in originations. This large, multiregional, US commercial bank has been working with FICO to simultaneously optimize its APR and initial credit line (ICL) decisions for its consumer credit card portfolio. The new CARD-compliant originations strategy balances the conflicting goals of minimizing losses and maintaining revenue. FICO consultants created an action-effect decision model that maps the relationships between the key drivers in the decision, including a range of actions that could be taken by the issuer and the likely reactions to them by the consumer. Figure 1 is a simplified representation of the model. Figure 1: Simplified model for APR/ICL decision Example Decision Model Variables Custom Origination Risk Score Credit Bureau Risk Score Activation Revenue # Inquiries L6 Months ICL & APR Profit Highest Utilization Bankcard Months on Credit Bureau File Risk Losses Decision models map the mathematical relationships between all factors that need to be considered in making a decision. Multiple underlying equations define the connections, in order to simulate the results of different actions the issuer could take, and likely customer reactions. page 3

4 »» The decision model was then used with an optimization algorithm to find the most profitable strategies given exposure and price constraints. As shown in Figure 2, the bank found that it could significantly increase profits without increasing exposure. The blue line in the graph shows the potential trade-offs between exposure and profit. It represents an efficient frontier of optimal strategies that will deliver maximum profit at different levels of exposure. Figure 2: Efficient frontier of optimized ICL strategies $25 AVERAGE INCREMENTAL PROFIT PER ACCOUNT $20 $15 $10 $5 Baseline (Champion) $0 $6,000 $6,500 $7,000 $7,500 $8,000 $8,500 $9,000 $9,500 ICL Figure 3: Efficient frontier of optimized APR strategies $25 AVERAGE INCREMENTAL PROFIT PER ACCOUNT $20 $15 $10 $5 Baseline (Champion) $0 3.99% 4.49% 4.99% 5.49% 5.99% 6.49% 6.99% 7.49% APR Optimization also revealed that the bank could increase its APRs and profit. Figure 3 shows the efficient frontier of optimal strategies that will deliver maximum profit at different price levels. page 4

5 The final combined strategy makes the most of limited exposure by shifting it from low-risk/lowrevenue consumers to low-risk/moderate-revenue consumers. At the same time it gives higher APRs to riskier accounts with less revenue potential (profiles indicating lower balances and utilization). Expected results, as shown in Figure 4, include a 17% decrease in loss while maintaining revenue, resulting in a $20 increase in profit per account. Once you ve identified optimal offers, you want to also maximize the chances that they ll be accepted by recipients. By using similar techniques of action-effect modeling and optimization, you can predict a consumer s sensitivity to the specific attributes of an offer, such as APR, line amount, introductory period, and even channel, packaging and timing. You can then automatically generate population segments with similar sensitivity. Figure 4: Expected results from APR/ICL decision strategy optimization Key Metrics per Eligible Accout Revenue Loss Loss/Revenue Activation Rate Bad Rate Average ICL Average APR Final Strategy Compared to Baseline -2% -17% -16% -4% -8% -1% 36% Ability to pay and profitable line increases As issuers revive activity in decision areas such as automated credit line increases (CLI), some will implement methods that are compliant, and others will implement methods that are compliant and profitable. The predominant response to the CARD requirement that issuers assess the consumer s ability to pay is to implement an income estimator tool or income estimation combined with some sort of debt-to-income ratio. With these in place to ensure compliance, many issuers will continue to make line management decisions based on the historical bias toward setting low limits on consumers with low incomes and high limits on consumers with high incomes. The problem is, of course, that traditional methods no longer work when the very profile of a profitable customer is changing. In a recent interview, a top risk officer told us that his company was still doing convenience-check mailings to individuals with FICO Scores of 780 or more. But he also said that the company was losing more money on high-income customers with FICO Scores of 700 or more than on customers with scores in the mid-600s and modest incomes. We re seeing a different recession in the first page 5

6 »» quarter of 2010 than in preceding months. Default rates are now rising among consumers who previously had little experience with economic hardship and so are less skilled at belt-tightening. Income is a poor indicator of ability to pay. We can see this clearly in Figure 5, which compares bad rates across a range of income levels to bad rates across a range of FICO 8 Scores. The X axis distributes the sample population into deciles. Depending on which line you re looking at, the deciles are either 10% distributions by risk score or 10% distributions by income. Therefore, the green line shows the bad rate from the lowest incomes (decile 1) to highest incomes (decile 10). The blue line shows the bad rate from the lowest FICO 8 Scores (decile 1) to the highest scores (decile 10). The green income line is relatively flat: Bad rates for low income, medium income and high income consumers are about the same. The blue line, however, shows much higher bad rates for the lowest FICO Scores (most risky), and bad rates fall as we move toward higher scores (less risky). As a predictor, therefore, income provides far less separation than the standard risk score. Figure 5: Income is a poor indicator of ability to pay New and exsisting accounts, general performance (90+ days past due) 80% BAD RATE % (90+) 70% 60% 50% 40% 30% Legend Estimated Income FICO 8 Score 20% 10% 0% DECILE Estimated income provides very little differentiation, since from lowest to highest income, the bad rate is similar. The FICO 8 Score provides clear differentiation with bad rates, that are highest for the lowest scores at the left of the chart, dropping as you move toward the higher scores on the right. A recent FICO research study on pooled bankcard data shows that issuers can make even more precise CLI decisions by using an additional type of analytic to predict credit capacity. Like the standard credit risk score, the FICO Credit Capacity Index (CCI) is based on bureau data, but it analyzes this data to find out something entirely different: What is the ability of consumers to manage increments of new debt on top of what they already have? page 6

7 The first step of the study provided a baseline by combining the results of using an income estimator with just the FICO 8 Score. In Figure 6, the top table shows bad rates in a matrix of income levels (rows) and risk scores (columns). Looking within the score columns, there is a modest difference between the bad rates at various levels of income. Income, therefore, is providing some separation between goods and bads above and beyond that obtainable from the score alone. Figure 6: Assessing ability to pay with income and risk score Pooled bankcard data on approximately 200,000 accounts ANALYSIS BY BAD RATE FICO 8 SCORE Income Code <= >798 TOTAL Missing 1, 2 3, 4 5, 6 7 8, 9 A Total 21.05% 17.50% 17.65% 18.06% 17.32% 16.87% 16.12% 19.28% 8.90% 7.10% 7.32% 7.26% 7.17% 6.52% 6.22% 7.87% 3.58% 2.59% 2.77% 2.67% 2.57% 2.23% 1.93% 2.92% 0.96% 0.75% 0.72% 0.68% 0.50% 0.47% 0.36% 0.71% 0.24% 0.21% 0.16% 0.13% 0.10% 0.12% 0.07% 0.16% 8.97% 6.80% 6.44% 5.95% 5.10% 4.19% 3.24% 6.82% Total excluding missing 17.51% 7.04% 2.50% 0.58% 0.12% 5.39% ANALYSIS BY LOSS RATE FICO 8 SCORE Income Code <= >798 TOTAL Missing 1, 2 3, 4 5, 6 7 8, 9 A Total 10.75% 8.60% 8.69% 8.81% 8.63% 8.27% 7.55% 9.66% 4.30% 3.03% 3.08% 3.08% 3.19% 2.95% 2.71% 3.60% 1.61% 1.08% 1.13% 1.13% 1.16% 0.83% 0.83% 1.27% 0.43% 0.35% 0.29% 0.27% 0.19% 0.22% 0.15% 0.31% 0.12% 0.09% 0.08% 0.06% 0.05% 0.06% 0.03% 0.08% 4.47% 3.20% 3.00% 2.76% 2.43% 1.96% 1.47% 3.30% Total excluding missing 8.58% 3.04% 1.05% 0.24% 0.06% 2.52% Legend Income Code Income Range A < $15,000 $15,000- $19,999 $20,000- $29,999 $30,000- $39,999 $40,000- $49,999 $50,000- $74,999 $75,000- $99,999 $100,000- $124,999 $125,000- $149,999 >150,000 page 7

8 Incremental Limit Incremental Balances Loss Dollars How Are Issuers Changing Course Under the CARD Act? Loss Rate % Risk-Adjusted Revenue % FICO 8 Score $ 362,202,500 $ 153,434,019 $ 2,175, % 13.42% Credit Capacity Index $ 369,729,000 $ 153,409,817 $ 2,036, % 13.54% Difference $ 7,526,500 $ (24,202) $ (139,207) 6.8% 0.09% ss Rate % = (Incremental Losses/Incremental Balances) sk-adjusted Revenue % = (Incremental Risk-Adjusted Revenue/Incremental There s less separation, Balances) however, when we look at loss rates an even more important metric for issuers who are considering increasing customer credit lines. The bottom table shows loss rates in the same matrix. Looking within the score columns, we see little difference between loss rates at various levels of income. So income doesn t provide a lot of added value above and beyond the risk score when it comes to predicting how much of a potential for loss an issuer might be exposing itself to in a CLI decision. Our view is that this smaller differential in loss rates reflects variability in how consumers utilize available credit and manage their debt. For example, within income levels, and within risk score ranges, there are consumers who will borrow only a small percentage of the credit offered to them, consumers who will borrow it all and everything in-between. These and other variations in behavior patterns are better captured by the credit capacity analytic, which is tuned to consider how consumers handle new debt. In the next step of our research study, we used the FICO 8 Score to determine which accounts would receive a line increase, then used FICO CCI for assigning the increase amount. As shown in Figure 7, we were able to reduce losses by 6.8% while growing balances. The forecasted result was a risk-adjusted revenue (RAR) gain of $2 $3 per account, and a 100 basis-point improvement in profit. A further slight improvement was achieved, as shown in Figure 8, by adding income to the decision matrix. Figure 7: Increased precision from using risk score with credit capacity score Pooled bankcard data on approximately 300,000 accounts Including 100,000 accounts for which income estimator could find no income data Incremental Limit Incremental Balances Loss Dollars Loss Rate % Risk-Adjusted Revenue % FICO 8 Score $ 362,202,500 $ 153,434,019 $ 2,175, % 13.42% FICO 8 Score + FICO Credit Capacity Index TM $ 369,729,000 $ 153,409,817 $ 2,036, % 13.54% Difference $ 7,526,500 $ (24,202) $ (139,207) 6.8% 0.09% Loss Rate % = (Incremental Losses/Incremental Balances) Risk-Adjusted Revenue % = (Incremental Risk-Adjusted Revenue/Incremental Balances) Figure 8: Best results from combining income, risk score and credit capacity score Pooled bankcard data on approximately 200,000 accounts Excluding accounts for which income estimator could find no income data FICO 8 Score + FICO Credit Capacity Index TM With Income Incremental Limit Incremental Balances Loss Rate % Risk-Adjusted Revenue % $ 234,889,250 $ 92,597, % 12.76% Loss Rate % = (Incremental Losses/Incremental Balances) Risk-Adjusted Revenue % = (Incremental Risk-Adjusted Revenue/Incremental Balances) $ 235,170,050 $ 92,530, % 12.85% Difference $ 280,800 $ (66,746) 0.5% 0.7% page 8

9 What do these research results boil down to for card issuers who want to resume automated CLI activity? If your goal is just to comply with ability to pay requirements, then integrating an income estimation with a FICO Score may be an adequate solution. However, if your goal is to make more profitable and compliant CLI decisions, you need additional analytic insight into the variability of consumer behavior, especially as that behavior continues to change in the new environment. Risk-based pricing after CARD Most issuers have implemented CARD-allowed 60+ days penalty pricing. Some are also planning to implement 30+ days prospective balance/protected balance pricing. It s very difficult, however, to determine how much to increase APRs on late-paying customers. As one FICO World session panelist remarked, It doesn t make sense for us to charge a huge APR that will cause 80% of balances to go to write-off while penalizing those customers who find a way to pay us back. Finding out what does make sense is the current focus of FICO s work with a large North American bank. The company is a user of FICO TRIAD Customer Manager, but had not previously turned on the risk-based pricing module. Now activating this module, with its new functionality for partial-balance price adjustments, this bank is among the first to implement penalty pricing and prospective balance/protected balance pricing. Penalty pricing business rules automate decisions about whether and how much to increase the APR (after the required notification + 45 days) on accounts that are 60+ days late. Prospective balance/ protected balance pricing rules determine whether and how much to increase the APR on future purchases (after the required notification + 45 days) for accounts that are less than 60 days late. The implementation brings a wide range of historical and current data on customer financial behavior to bear on its pricing decisions. Through systematic champion/challenger testing and rigorous analysis of results, the company expects to get a head start on competitors in understanding the changing environment. Selective Overlimit Authorization Issuers are facing a very tricky situation indeed when it comes to authorizing overlimit transactions. Chronically overlimit customers used to be quite profitable due to penalty fees. Today it s much more complicated: Issuers need to figure out which opt-out accounts will remain profitable, even if allowed to go over their limit without a fee. There is urgency around this, since the rising decline rates and other negative impacts on revenue from current broad-brush policies are likely to intensify. In a recent survey of US banks 1 by the Professional Risk Managers International Association, half of the respondents expected more cuts to the average credit line on credit card accounts during the second quarter of Approximately the same number said they expected the average balance to increase. While more recent research indicates that usage may actually be trending downward, slow job growth in the recovery means that many consumers will continue to have card balances hovering around the top of their credit lines. The frequency of transactions referred for overlimit authorization is likely to remain high, and so is the impact of bank decisions on customer satisfaction and retention. Banks should replace broad-brush policies, such as disallowing all overlimit transactions, or allowing them but not charging for them, with automated authorization strategies that selectively allow 1 US Consumer Credit Risk: Trends and Expectations, 2nd Quarter 2010, Professional Risk Managers International Association, April page 9

10 profitable customers to go beyond their limit. That requires getting a handle on the variability of customer behavior in the new environment, including likely reactions to the changing policies of card issuers. The best way to do that is using additional analytics such as credit capacity scoring, along with action-effect decision modeling and optimization.»» Winning in the New Market Winning in the new market requires comprehensive optimization of the originations process, credit line management and collections. That s how Ray Duggins, Chief Credit Risk Officer at GE Capital, summed up the changed business environment. We agree with Ray. New pricing rules and other CARD regulations have given greater urgency to making the right decision upfront. In today s market, issuers must make more precise decisions. That requires a better understanding of consumer behavior and motivations, enabled by analytic techniques such as action-effect modeling and optimization. It won t be long before these methods, once applied only in limited areas by an elite handful of companies, are widely used across the customer lifecycle by most financial services companies. Winners will also perform this precise decisioning at the customer level assigning a single credit limit, for example, flexibly distributed across all accounts. Such holistic approaches will enable companies to understand and manage not only the relationships and trade-offs between the components (APR, ICL, introductory period, etc.) of a particular offer, but also how making that offer may impact other credit products the customer may be a candidate for or may have. The ability to do this speedily, consistently and in huge volumes is also fundamentally intwined with initiatives to find more profit by transforming the card industry s business model. Listen to these voices from industry leaders: We think we can get back to a 3% ROA over time by increasing our market penetration and share of wallet. It s going to take shifting to a payments-oriented business model where we re offering services that help customers manage their money and move their debt responsibly from one vehicle to another. Rick McNutt, Executive Vice President, PNC» Our industry s future involves combinations of fee-based bundled services, like subscriptions in the mobile telephone market. Sure we have had fee-based products for years, but we ve never dealt with bundled enhancements around the core product, and we need to. Vice President of Bankcard Services at a Large Regional US Bank» The days when households had no budget or payment plan in place are gone. More and more households are operating their finances as small businesses, carefully managing cashflow. There s plenty of opportunity to provide these financially savvy households and small businesses with valuable services like notifications when a check clears especially through mobile channels. Steve Mott, Principal, BetterBuyDesign page 10

11 Two panelists at the FICO World session Post-Crisis Analytics, Chris Sparks, head of financial strategy for financial institutions at Royal Bank of Scotland, and David Malcolm, General Manager, Risk Product Operations at Westpac, a top Australian bank, told the audience that their companies were already working on integrating and optimizing decisions at the customer level, beginning with collections and pre-delinquent treatments. The rationale for starting there is not only the need to control rising levels of delinquencies and write-offs, but the fact that back-end processes usually present the fewest data integration obstacles. Pre-delinquency is particularly fertile ground for such efforts, since making the right decisions at this stage can not only reduce bad debt losses but can also strengthen relationships with valuable customers. As Chris Sparks, of Royal Bank of Scotland, put it: Before accounts go into collections is when we have the opportunity for really productive conversations with customers. There are a lot of complicated reasons why otherwise good customers have problems, and if we can identify them early on, we have an opportunity to» do something to help them get out of trouble and back to a safe place. We re trying to craft» a lasting relationship with customers and their families, and we re willing to invest in making that happen. In the months to come, we ll continue reporting on the investments card issuers are making for successful futures in a changing market. Learn more: Learn about FICO s program for Reengineering Card Profitability, and read related white papers, at Contact us at info@fico.com to explore more ways to mitigate regulations impact on your card profitability. The Insights white paper series provides briefings on research findings and product development directions from FICO. To subscribe, go to For more information US toll-free International web (0) info@fico.com FICO, TRIAD, Credit Capacity Index 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 Fair Isaac Corporation. All rights reserved. 2700WP 08/10 PDF