Building Ability to Pay -Compliant Growth Strategies

Transcription

1 Building Ability to Pay -Compliant Growth Strategies The CARD Act s income provision poses a lasting challenge for issuers to comply without sacrificing profit goals Number 68 June 2013 The Credit Card Accountability Responsibility and Disclosure Act of 2009 (CARD Act) introduced sweeping changes to how US credit card issuers conduct business and price their financial services. Of these, one of the most difficult components to comply with has been the ability to pay (ATP) provision. The Federal Reserve s Final Rule implementing this provision requires that, prior to the extension of new credit, issuers consider an applicant s income and/or assets in relation to the applicant s debts. Since the CARD Act and the implementing regulation took effect in February 2010, capturing or estimating the income component of the ability to pay provision has emerged as a major hurdle for card issuers doing business in the US. Without a doubt, the ATP provision has had a material impact on their bottom lines. Estimates suggest, for instance, that lenders who cannot proactively grant line increases could be losing $50 per active account per year in foregone revenue. This white paper explores the ability to pay issue in depth, from current challenges issuers face to new approaches that balance compliance requirements with growth goals. It includes: Analysis of today s regulatory environment One issuer reported that not resolving the ability to pay challenge is costing millions in lost revenue because growth is severely limited. Examination of compliance costs and hurdles New research into the predictive value of income Best-practice strategies for compliance and growth Make every decision count TM

2 The High Cost of Compliance Over the past several decades, lenders have relied on a combination of application data, behavioral data and credit risk scoring analytics to determine which consumers qualify for an extension of credit, both in opening new lines and increasing existing lines. While broad-based credit risk scores like the FICO Score that are built upon credit bureau data include debt obligations, neither income nor assets is reported or stored at a credit bureau or other large-scale accessible repository. The new ATP provision has had a dramatic impact on US issuers, as described in the FICO Insights white paper #42: How Are Issuers Changing Course Under the CARD Act? To comply with this change, many lenders initially implemented commercially available income estimation models within their risk assessment. Since then, these income estimators have been called into question by regulatory examiners on the basis of questionable accuracy. As a result, financial institutions of varying sizes are still struggling to meet the CARD Act income requirement in a compliant way, and there is growing frustration at the lack of resolution to the industry-wide problem. Many report this as a top concern for card risk management. Ken Paterson, Vice President of Research at Mercator Advisory Group, agrees: Ability to pay has emerged as a much bigger hurdle than expected, and lenders are still struggling to find an effective solution. Card issuers across the industry are suffering significant revenue shortfalls due to the income restriction, which is preventing them from extending credit to consumers who otherwise pass their risk assessment. Brian Riley Senior Research Director CEB TowerGroup One issuer reported that not resolving the ATP challenge is costing it millions in lost revenue, because growth is severely limited. Brian Riley, Senior Research Director at CEB TowerGroup, summarizes it this way: Card issuers across the industry are suffering significant revenue shortfalls due to the income restriction, which is preventing them from extending credit to consumers who otherwise pass their risk assessment. Estimates suggest that lenders who cannot proactively grant line increases could be losing $50 per active account per year in foregone revenue. Foregone profits could be as much as $5 million from just one campaign on a portfolio of 1 million accounts. While impact will vary greatly, there is no doubt that the ATP provision has had a material impact to lenders bottom lines.»navigating» Today s Regulatory Environment History has shown that the regulatory landscape tends to swing like a pendulum between stricter regulations (risk aversion) and deregulation. In response to the recession of 2008 and the near meltdown of the US financial system, there was considerable pressure on legislators to strengthen consumer protections, and lawmakers responded by imposing additional regulations aimed at limiting the risky practices in which lenders could engage. The resulting cycle of tightened regulations includes regulations such as the CARD Act and the Dodd-Frank Wall Street Reform and Consumer Protection Act. page 2

3 »» The CARD Act, which amended the Truth in Lending Act of 1968 (TILA), introduced the broad requirement to consider the consumer s ability to repay debt before extending new credit (bold emphasis added): Sec Consideration of Ability to Repay. A card issuer may not open any credit card account for any consumer under an open end consumer credit plan, or increase any credit limit applicable to such account, unless the card issuer considers the ability of the consumer to make the required payments under the terms of such account. This provision puts the responsibility for safeguarding consumer interests onto the lenders, to ensure consumers don t take on more debt than they can handle. Regulation Z, the rule issued by the Board of Governors of the Federal Reserve System 1 that implements the changes made by the CARD Act s TILA amendment, interprets the income requirement in a prescriptive way, such that card lenders must have reasonable policies and procedures to consider income or assets as well as debt obligations Ability to pay. (a) General rule. (1)(i) Consideration of ability to pay. A card issuer must not open a credit card account for a consumer under an open-end (not home-secured) consumer credit plan, or increase any credit limit applicable to such account, unless the card issuer considers the consumer s ability to make the required minimum periodic payments under the terms of the account based on the consumer s income or assets and the consumer s current obligations. (ii) Reasonable policies and procedures. Card issuers must establish and maintain reasonable written policies and procedures to consider the consumer s ability to make the required minimum payments under the terms of the account based on a consumer s income or assets and a consumer s current obligations. Reasonable policies and procedures include treating any income and assets to which the consumer has a reasonable expectation of access as the consumer s income or assets, or limiting consideration of the consumer s income or assets to the consumer s independent income and assets. Reasonable policies and procedures also include consideration of at least one of the following: The ratio of debt obligations to income; the ratio of debt obligations to assets; or the income the consumer will have after paying debt obligations. 2 In its official staff interpretation of the regulation, the Federal Reserve Board allowed for the use of consumer-stated income and income estimators that are empirically derived, demonstrably and statistically sound (EDDSS). 1 From TILA s inception, the authority to implement the statute by issuing regulations was given to the Federal Reserve Board. Effective July 21, 2011, the Dodd-Frank Act transferred rulemaking authority over TILA to the Consumer Financial Protection Bureau. 2 As of April 30, 2013, the CFPB removed the independent ability to pay requirement for consumers over the age of 21, allowing issuers to consider income to which the consumer has a reasonable expectation of access. page 3

4 »» A card issuer may consider the consumer s income or assets based on information provided by the consumer... A card issuer may also consider information obtained through any empirically derived, demonstrably and statistically sound model that reasonably estimates a consumer s income or assets. The intent of the ATP provision and regulations is for lenders to accurately assess an individual s ability to repay. However, in practice, there have been unforeseen complications in complying with the regulation. In context of this regulatory environment, we ll turn now to the available alternatives for acquiring the appropriate information to comply with the ATP provision.»» The Challenges of Calculating Income In making an ATP determination, lenders must consider the consumer s income/assets in relation to debt and determine that the consumer can make the monthly payments. Typically, the income/ assets and debt are combined into a normalized ratio, such as a Debt-to-Income (DTI) ratio or calculation of net disposable income, expressed in monthly terms to facilitate comparison with minimum monthly payments. Lenders may consider three different types of income allowed by the regulation: verified, selfreported and estimated income, in addition to other assets. Each type has some benefits, but also faces inherent challenges. Debt obligations can be captured from the consumer s credit report, available from any of the three major consumer credit reporting agencies (CRAs). While the regulation requires debt obligations to be considered, a holistic assessment would also consider living expenses. Verified Income. Verified income is the most ideal due to its accuracy, however accessibility is limited, and its cost is generally prohibitive outside of mortgage loans. Income verification services are cost-prohibitive for smaller, unsecured or revolving lines, and too time-consuming for lenders to respond to consumer credit applications in real time. Furthermore, income databases tend to have low coverage of the population. Lenders can take advantage of recent paystubs submitted for other purposes or demand deposit account (DDA) information, if available. Procurement of verified income aside, freshness of the data is still a concern. Even income from a consumer s W-2 form from last year may quickly become out-of-date, depending on the individual s personal situation and life events, including employment, marriage and health status. Self-Reported Income. Self-reported income is easy to request at time of application and is significantly less costly than verified income. But self-reported income has its challenges, notably uncertain reliability. While some consumers may knowingly under- or over-report income, others may do so unintentionally. Calculating income is not always straightforward, and consumers may state their income in different ways for a variety of reasons. Should they state gross or net income? Should they provide annual, monthly or bi-weekly income? Many people have variable compensation, such as hourly or seasonal workers or those on commissions or bonuses. Unreported or untaxed income is another wild card, not to mention the question of individual and household income. Income Estimators. Income estimators offer numerous operational benefits such as scalability and widespread availability. Because estimators are widely available at the CRAs, an up-to-date estimate can be obtained at any time, without having to contact the consumer, who may perceive a request to reveal their income as an invasion of privacy. page 4

5 However, there have been serious challenges to the accuracy of income estimators in the wake of the ATP provision, spreading doubt about their validity. For instance, a number of lenders report being prohibited from using modeled income by bank examiners who are concerned that the currently available income models are not sufficiently predictive of the consumer s actual income, or do not meet the requirement that income estimators must be empirically derived, and demonstrably and statistically sound. The explanation for the low accuracy may lie in a lack of available development data, such as a verified income repository, upon which to build a predictive broad-based model. It may also be that the spending and payment behaviors captured at the CRA do not correlate with income, since individuals manage their finances in different ways. Another issue is that estimators commonly output a range rather than a specific dollar amount, introducing additional fuzziness into an ATP determination. Many estimators are capped at a certain value, and as a result, underestimate upper-income consumers. FICO is conducting follow-up research on the feasibility of building stronger income estimation models. Assets Besides Income. Considering consumer assets is another way to comply with the regulation, allowing lenders to consider other sources of income. The regulation explains: Income, assets, and employment. Any current or reasonably expected assets or income may be considered by the card issuer. For example, a card issuer may use information about current or expected salary, wages, bonus pay, tips, and commissions. Employment may be full-time, part-time, seasonal, irregular, military, or self-employment. Other sources of income could include interest or dividends, retirement benefits, public assistance, alimony, child support, or separate maintenance payments. A card issuer may also take into account assets such as savings accounts or investments that the consumer can or will be able to use. Lenders have a longstanding tradition of relying on assets as a good indication that a borrower can repay a loan, even if the borrower does not have a regular source of income. However, the regulation seems to require that the assets be reasonably liquid; if not, then calculating available (monthly) funds to repay a monthly obligation may become complicated. While there are third-party providers of verified asset information, comprehensive coverage of the population is a concern, especially for certain segments likely to be under-represented. Debts. On the obligations side, lenders have access to consumer debt and payment patterns reported to the CRAs, including both installment loans and revolving lines. However, lenders still have to make a number of decisions about how to translate these into a DTI ratio or monthly disposable amount. Based on our experience building predictive models using data from all three CRAs, FICO is uniquely positioned to offer guidance on calculating various debt metrics. For instance, the determination should calculate monthly obligations, even though some loans have different payment schedules. Other loans, like mortgages, are shared household debt as opposed to individual debt. The calculation should also account for deferred loans such as student loans. Furthermore, lenders should consider whether the consumer may have any obligations not reported to the CRA, such as personal debt or healthcare bills. Finally, it s important to remember that debt obligations do not page 5

6 account for monthly expenses not captured on the credit report. These include utilities, rent, food and similar costs, and should be adjusted based on reasonable geographic differences. With this background, let s consider a related question, critical in this context: Does considering income, as required by the ATP provision, provide any predictive lift within a consumer credit risk assessment, and if so, how much?»gaining» Predictive Value from Income There is a commonly held belief among risk managers that income provides little to no value to a risk assessment, especially for smaller, revolving lines of credit. 3 As it pertains to the ATP rule, Nessa Feddis, Vice President and Senior Counsel for the American Bankers Association, remarked that: [i]ncome isn t as good a predictor of whether someone will repay as their credit history is. As of now, banks are being required to verify income, although how they do that is a work in progress. 4 Figure 1: Income has some predictive value Income by Bad Rate, Bankcard Performance BAD RATE, 90+ DPD 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Income POPULATION PERCENTILE, LOW TO HIGH RISK To be sure, income, even when accurate, does not capture consumer behavior; it also does not take into account different lifestyle choices or variable costs associated with different geographic regions, number of dependents and the like. On the other hand, regulators, legislators and common sense would suggest that income and assets are relevant to consider. After all, without either of these, how could a consumer repay debt? What is not well understood is how income affects a consumer s risk and ability to repay debt. Towards this end, FICO undertook research on the value of income in the context of assessing ATP. A key goal was to move beyond opinion to empirical evidence that can drive well-informed decisions. Our research strove to understand the contribution of income as a factor in the assessment of how much additional debt the consumer can repay. To isolate the predictive value of income, we first examined how income rank-orders consumers by their probability of default. This study used a sample of consumer records for which we had verified income information based on mortgage applications, and then looked at the consumers credit card payment performance as they took on debt. In the first research phase, we considered whether income is related to risk. As shown in Figure 1, when we rank people from high to low income, we also see an increase in delinquency rates. While we don t see perfect rank-ordering of consumers, there is a clear relationship between income and risk. No lender would use income as sole criterion for a lending decision, so in the next research phase, we considered the relationship between DTI and risk. This study calculated DTI using obligations reported to the CRA, including mortgage, and the same verified income examined above. 3 FICO research has found that for larger installment loans, income does have more bearing, and mortgage lenders have long relied on various DTI calculations to understand the consumer s financial situation. 4 Chicago Tribune, December 17, page 6

7 Figure 2: DTI is a stronger risk predictor than income Income and DTI by Bad Rate, Bankcard Performance BAD RATE, 90+ DPD BAD RATE, 90+ DPD 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Debt to Income Income POPULATION PERCENTILE, LOW TO HIGH RISK Figure 3: FICO Score is the strongest risk predictor Income, DTI and FICO Score by Bad Rate, Bankcard Performance 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% FICO Score Debt to Income Income POPULATION PERCENTILE, LOW TO HIGH RISK Figure 4: Combined approach offers highest performance In Figure 2, we ranked the population from low to high DTI. The steeper line indicates that DTI is a stronger predictor of delinquency in rankordering risk than income, perhaps because DTI is a normalized ratio capturing both income and debt. For comparison, when we include the FICO Score for the same population, we see significantly more separation coming from the risk score (see Figure 3). This underscores that the FICO Score is the strongest predictor of future delinquency because it is designed for that purpose. Does this prove that income doesn t add to a risk assessment? Not necessarily. A more precise measurement is gained when we put these different variables into an analytic scorecard where we can measure the relative contribution of each to the model. Figure 4 shows that the FICO Score alone offers a material improvement in predictiveness over DTI alone. Using DTI together with the FICO Score provides an additional 2.55% lift. This suggests that income does indeed add a small but perceptible improvement over the FICO Score, capturing something not already represented by the risk score. However, the contribution is small because the FICO Score is already doing an effective job of separating good risks from bad risks. The results we ve seen so far offer a snapshot of performance without respect to different segments of the population or varying amounts of incremental debt. Since the ATP provision is meant to protect customers from getting credit that exceeds their ability to repay and thus pushes them into default, we then explored the role income plays in the relationship between incremental debt burden and default. Bankcard Performance, 90+ Days Delinquency Predictive Variables Relative improvement in predictiveness 5 Debt to Income alone -- FICO Score alone 22.3% improvement over DTI FICO Score + Debt to Income 2.55% improvement over FICO Score 5 In this study, predictiveness is measured in ROC Area, a measure of how well the model separates goods from bads in the rank-ordering. The maximum value is 1, representing the case where all consumers who default are accurately distinguished from those who do not. page 7

8 We tested this by looking at how bad rates on bankcards evolve for different population segments (defined by a combination of FICO Score and DTI) as they take on incremental monthly debt. Thus in Figure 5, we see the relationship between incremental debt, shown as ranges on the x-axis, and the subsequent change in risk level. Figure 5: Measuring risk as consumers take on more debt Bad Rate by Incremental Monthly Debt, Bankcard Performance BAD RATE, 90+ DPD 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% FICO Score FICO Score FICO Score INCREMENTAL MONTHLY DEBT OBLIGATION ($) DTI >45% DTI 25 45% DTI 10 25% DTI <10% The first thing to note is that as incremental monthly debt increases, the observed bad rate increases. As expected, we see a lot of separation between FICO Score groups, indicating that default rates are largely captured by the score. Within each score range, DTI provides some risk separation. Given the varying steepness of the lines associated with DTI levels, we conclude that DTI is capturing consumer sensitivity to additional debt not yet incurred. Steeper lines indicate groups that are more quickly affected by additional debt burden, whereas flatter lines indicate less impact. For example, in the score band (orange lines), the low DTI consumers are less sensitive to additional debt, while the high DTI consumers show greater tendency to default (about twice as likely) as they take on more debt. Let s consider a matrix showing DTI ratio in a crosstab with the FICO Score, as shown in Figure 6. We see an interesting pattern in the range, a key decision area of the risk spectrum. High DTI consumers are at greater risk of defaulting than their low DTI counterparts within the same risk band. Figure 6: High DTI consumers show greater default risk in key decisioning area Bankcard 90+ Bad Rates: DTI Ratio by FICO Score FICO Score Bands Average Bad Rate Debt to Income Ratio Bands 0 10% 94% 75% 54% 36% 33% 18% 12% 9% 12% 2% 5% 4% 1% 1% 0% 0% 0% 0% 4% 10% 25% 88% 79% 75% 63% 51% 32% 28% 14% 9% 7% 5% 2% 1% 1% 1% 0% 0% 0% 3% 25% 45% 94% 78% 67% 67% 52% 42% 26% 18% 11% 10% 7% 5% 3% 2% 1% 0% 0% 0% 7% 45%+ 90% 90% 85% 68% 58% 48% 34% 27% 22% 16% 14% 10% 7% 4% 3% 2% 1% 0% 15% Average Bad Rate 91% 84% 77% 66% 54% 42% 29% 21% 16% 12% 10% 6% 4% 2% 1% 1% 0% 0% 8% page 8

9 From these results, we conclude that DTI does differentiate consumers within some risk bands, namely the mid-risk spectrum right around/below the standard subprime boundary. DTI is less informative at the low and high ends of the risk spectrum. In summary, our research found evidence that income, especially within a DTI ratio, does offer some predictive value on top of the FICO Score. Let s now consider how best to incorporate income into an ATP determination. Best Practices for Compliance and Growth Many lenders have approached ATP compliance as a purely check-the-box task, hoping to comply with as little impact as possible to existing processes. However, given that broad-based estimators are failing to gain approval in the current regulatory environment, lenders are forced to rethink their compliance strategies. The key issue becomes how to comply and gain value in the process in other words, how to avoid added compliance costs that don t produce a return. The practice of simply applying a minimum income requirement before or after the risk assessment doesn t extract any of the analytic value discussed above from the income information. Thus, a lender would not be fully leveraging the investment into acquiring income information. With the right approach, lenders can seek to improve risk segmentation and assessment in a regulatory compliant way, with guidance from their legal and compliance team. 6 Defining a compliance strategy depends on each lender s unique business context and must strike the right balance of various constraints, such as cost, operational feasibility, and perhaps most importantly, available data. FICO can advise on some best practices for gaining the most value from your ATP compliance strategies. Tailor your strategy to credit lifecycle Since compliance requirements differ by lifecycle, strategies should be tailored to make investment when supported by a strong return. For prescreening, there is no requirement to check income or calculate ability to pay prior to making a prescreen offer. Thus, lenders can continue to solicit consumers without acquiring income information beforehand. If a consumer receives a prescreen offer and applies for credit, the Fair Credit Reporting Act allows lenders to implement a post-screening step where they consider additional information not available at time of prescreen, such as the consumer s income. In this fashion, lenders can make a prescreen offer of credit, but are still not required to make a firm offer of credit if the consumer does not meet the lender s minimum required DTI standards. This post-screening process is permissible as long as the DTI standards are established and documented prior to the lender s prescreen solicitations. 6 This paper is not intended as legal advice. Please consult with your own legal and compliance professionals to determine how these best practices may apply to your particular situation. page 9

10 At time of origination, most lenders find it easy to request consumer income directly, whether on an application, at retail point of sale or through a customer-requested line of credit. This is a cost-effective way to acquire income information and is fully aligned with the spirit of the regulation. Lenders may elect to clarify for consumers exactly the type of income they re requesting on the application. Account management decisions, such as how to facilitate automated credit line increases, present the largest challenge and require the most design. Due to this hurdle, it s doubtful lenders will return to the liberal line increases of several years ago. Many lenders have begun to solicit customers with invitations to apply, enabling them to interact with consumers, get a stated income and reward the consumer with a line increase. FICO can help you identify ways to strengthen your business and return to growth, while also supporting your regulatory compliance strategies. We ll discuss several examples in the sections below. Rely on sound business practices As a matter of good business practice, financial institutions should implement a comprehensive assessment of each consumer s financial situation, as well as responsible lending programs that rely on tried-and-true credit risk management practices, such as the FICO Score and a solid DTI ratio. With this framework in place, FICO can suggest approaches to generate additional analytic insights and improve segmentation within your ATP compliance strategy. Given regulatory concerns about consumers being granted too much credit, any estimation model must take care not to overestimate income. One way to mitigate the impact of model inaccuracies is to reduce the estimates across the board by a certain percentage so that no overestimation occurs. FICO can build analytic models to determine whether available monthly funds are sufficient to repay a specific amount of debt and how a consumer s risk level may change with access to incremental credit. Get closer to your customers In order to grant a line increase, lenders must acquire up-to-date information about consumer income. The easiest way to get this is by building a relationship with your customers, which can be done by phone or through internet banking channels, or when they visit a branch or call in for customer service. New customer engagement technologies, like FICO Adeptra Mobile Services Platform, facilitate communication through customer-preferred channels. For instance, younger consumers often prefer to interact with financial institutions on their mobile devices by text or , while older consumers may prefer an automated voice call. This opt-in customer engagement approach allows lenders to get consumer-stated income in an automated and cost-effective way that also aligns with regulatory intent. In addition, FICO can help clients select (or optimize) the best candidates for credit line increases and provide opt-in strategies even without having income at the time of the offer, instead of relying on stated income coming from the consumer s response. page 10

11 Ability to pay has emerged as a much bigger hurdle than expected, and lenders are still struggling to find an effective solution. Ken Paterson Vice President of Research Mercator Advisory Group Leverage all the data you can Third-party providers of asset or income information can be leveraged where applicable. While broad-based income estimators have come under intense scrutiny, custom models leveraging other sources of data have fared better. For example, an approach that leverages banking relationship data, such as from demand deposit accounts, can provide a more reliable estimate of income based on monthly available funds. Financial institutions that hold checking and savings accounts should consider leveraging that data to bolster their marketing of bankcards to customers likely to have credit cards with other institutions. 7 Although checking and savings account data may not provide the total customer asset picture, it may suffice to determine whether the consumer can repay a specific extension of credit. FICO offers innovative analytics that take advantage of recent additions to the CRAs, such as considering the consumer s actual payments as opposed to minimum scheduled payments. Improve your risk assessment Irrespective of how income information is acquired, it s critical to maximize its analytic value within your risk management so that the investment drives return. For instance, income can be incorporated into the risk assessment in a variety of ways, and it might be used to set the credit line appropriately. Given the predictive lift from income that we discussed earlier (e.g., DTI contributed 2.55% lift on top of the FICO Score), FICO can develop an income- or DTI-adjusted FICO Score. Focus on the cutoff areas Accurate income may be more critical for some decisions than others, so it s prudent to consider where income may alter a credit-granting decision. In Figure 6 (on page 8), for instance, income may be less likely to change the decision for consumers in the high-risk and low-risk groups, because other factors weigh more heavily. It s primarily at the edges of the score cutoff area where income could sway a lending decision or (dis)qualify a consumer for better terms. This is likely the key area of focus for acquiring accurate income. Therefore, an accurate measure of income, obligations and disposable income can give all parties greater confidence that the credit under consideration is appropriate and affordable for consumers in the cutoff area. 7 A PricewaterhouseCoopers survey of consumers found that 48% only have credit cards at institutions other than their primary financial institutions; another 16% have credit cards at both their primary and other institutions. page 11

12 »Conclusion» Complying with the income component of the ATP provision remains a challenge for the industry. This is especially true due to a more restrictive regulatory environment than when the provision first took effect, as commercial income estimators have by-and-large been rejected. Despite these restrictions, appropriate analytic forethought will ensure that your investment helps to achieve goals related to growth and profitability, as well as regulatory compliance. We ve seen that income and especially DTI can provide lift in an analytic risk assessment. Given the challenges of acquiring reliable income information, lenders would be well-served to extract full analytic value out of such valuable data. Those who manage to solve this issue are poised for growth and can position themselves ahead of competitors still struggling to define their approach. A comprehensive and compliant lending program communicates to the public that your institution takes its responsibilities seriously, and communicates to your shareholders that compliance does not mean sacrificing profitability. FICO specialists are available to discuss our unique approach to leveraging income and debt obligations within a regulatory compliance strategy, while improving the profitability of credit granting decisions. To discover more strategies that balance growth and compliance, visit the Banking Analytics Blog or download these Insights papers: Comply and Compete: Model Management Best Practices (#55) How Analytics Can Help Banks Navigate Financial Reform (#43) How Are Issuers Changing Course Under the CARD Act? (#42) The Insights white paper series provides briefings on research findings and product development directions from FICO. To subscribe, go to For more information North America toll-free International web (0) [email protected] FICO, Adeptra 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. 2978WP 06/13 PDF