Summary. January 2013»» white paper

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1 white paper A New Perspective on Small Business Growth with Scoring Understanding Scoring s Complementary Role and Value in Supporting Small Business Financing Decisions January 2013»» Summary In the ongoing pursuit of profit and shareholder value, the methods financial institutions select to make decisions on applicants and accounts matter greatly. But rather than pitting alternative methodologies against one another to select the best one, financial institutions should shift their thinking to look for the best methodology for particular decisions and applications. That s an approach that s truly in the best interest of the institution, providing alternatives that can be cost-effective and revenue producing. This paper describes the role scoring technologies can fulfill to help financial institutions increase profitability of their small- and medium-size enterprise (SME) portfolios. It explains how institutions can benefit by differentiating which decision areas are best served from the use of automation and scoring, and which require the added expertise of skilled underwriters. It also provides a general explanation of scoring, and how its numerous benefits enable institutions to safely stimulate growth, effectively manage risk, more easily comply with regulations and reduce operating costs. Make every decision count TM

2 »Rewarding» Innovation with New Pathways to Profit Unless a financial institution is willing to explore new ways to make decisions on applicants and current customers, can a volatile economy be completely blamed for difficulties in driving greater profitability? This is a particularly important question for institutions serving small and medium enterprises (SMEs). Typically, SME portfolios reside within the retail bank. However, unlike other retail bank portfolios that rely on advanced scoring and automated technology for applicant and account decisions, many SME decision processes continue to rely solely on traditional underwriter-based practices, favoring paperintensive and time-consuming manual reviews. Of course, there is no single correct methodology for every decision area an institution must address. SME underwriters expertise is the best methodology in evaluating certain decisions for example, in risky SME market segments, or decisions involving higher exposures. In these and other cases scoring and automated technology should not completely replace judgmental decision-making practices. However, by determining where scoring and automation will drive greater value in supporting decisions, financial institutions can significantly enhance their ability to pull the levers of profitability on their SME portfolios. This paper discusses the benefits of scoring applied to certain decisions, the types of scoring models and the best uses of each, and responses to the most common questions about adopting scoring technology.»»increasing Profitability with Scoring Technology From the perspective of SME portfolio managers relying solely on judgmental evaluations and processes, today s lending environment with higher risk levels and new regulations can only add up to one thing: more work from additional documentation to review, and many more hours required to complete the tasks necessary to render a decision. That results in two undesirable consequences. First, it makes growth more challenging limited underwriting resources must take more time to process decisions. And second, it increases operational costs the additional time and cost to process decisions lowers overall portfolio profitability and can drive away applicants and customers who are good credit risks but are impatient for a decision. Today s most advanced scoring and automated technology solutions address these challenges operating in a completely automated fashion without the need of underwriter intervention, or in a semi-automated fashion with minimal underwriter intervention. Institutions adopting scoring and automated technology can: Maintain strong risk management and appropriate loss exposure. More easily comply with today s demanding regulatory requirements by making consistent and defensible decisions, and by avoiding bias in decisions. Reduce operational costs, adding to profitability. Expand into new markets to quickly stimulate portfolio growth. With a nominal investment in scoring models and automated platforms, fast training and implementation time, and low FTE requirements and operational expenses, SME portfolio managers can quickly add to overall enterprise profitability. Here s how Fair Isaac Corporation. All rights reserved. page 2

3 Risk Management The ability to identify good versus bad risk has a major effect on the profitability of portfolios. Scoring models or more specifically, risk scoring models analyze a business s past performance to predict how likely the business is to pay their credit obligations as agreed (based on odds ratios and rank-ordering of businesses, as discussed later). Using complex algorithms built from vast amounts of business profile and historical performance data, scoring models enable financial institutions to confidently make accurate, reliable and fast applicant and account decisions based on empirically derived risk detection. While scoring can be used across the customer lifecycle in originations, account management and in collections it is particularly beneficial in originations. Originating a new account is a critical decision area where the ideal goal is to identify and control 80% of identifiable risk before the account is booked; this sets the stage for future risk performance and yield potential from that customer. Scoring helps institutions with SME portfolios automatically make profitable decisions on new business applicants and protect against future losses. A variety of decisions, based on score as well as on the institutions current policies and strategies, can be programmed into loan origination systems: for example, in addition to accept/decline decisions, scoring and automated technology can be used to establish appropriate line or loan amount, or set pricing and terms that mitigate risk while maximizing profits. Regulatory Compliance Scoring-based automated platforms can support various national as well as international regulatory requirements, such as Fair Credit Lending practices, capital requirements and model management. Because of scoring s empirical, formula-based approach to decision support, financial institutions serving SME markets can make consistent decisions, reducing bias or human error, while supporting the institution s ability to document activities and deliver standard reports of performance activities summarized by score ranges. As regulators examine the institution s scoring models and seek to determine if they are being used appropriately, advanced scoring and automation solutions today make it easy to store and pull records to show the correlation and overall consistency of scores to decisions, as well as any stored documentation affecting a score-based decision. Reduced Operating Expenses By determining decision areas that are best-suited for scoring-based decision making, financial institutions can cut out the unnecessary expense of assigning underwriters to decisions that don t require their expertise. Whereas an underwriter s review can typically take days, scoring can greatly expedite the speed to decision from days to hours, or even to minutes in some cases. That frees up valuable underwriter time to focus on the decisions that most need their expertise. Growth Through Market Expansion and Faster Customer Service Scoring technologies enable financial institutions with SME portfolios to stimulate growth by quickly expanding into different SME market segments. Institutions can select new market segments that meet their services charter and policies, selecting the segments that would be best served by scoring technology vs. manual review. In addition, institutions can rely on external industrywide data to analytically identify potentially profitable and lower-risk market segments Fair Isaac Corporation. All rights reserved. page 3

4 With scoring and automated technology, institutions can safely approve requests in new markets, resulting in rapid growth of the portfolio. With the promise of same-day turnaround on requests, institutions using scoring can keep applicants from applying at competitor institutions and quickly gain a reputation for fast and reliable customer service.»» Getting Started with the Right Scoring Model In initiating scoring into their decision support operations, financial institutions can select from several different types of scoring models. The choice will be shaped by factors such as budget, data availability, time-to-market and the institution s business objective. With the option of choosing from various model types, there s an entry point for scoring for just about every institution, as well as a path to apply additional types of models over time. All scoring models will improve performance and lead to better risk control. But institutions must understand which model type is best suited for their current constraints and objectives, as well as the advantages and disadvantages of each model type. Following is a description of four model types generic, pooled, expert and custom highlighting the pros and cons of each. Generic Models One quick way to initiate scoring is by acquiring scores on a decision-by-decision basis from any of several business and/or consumer bureaus throughout the world. Bureau-based models are built on a combination of consumers and/or business s trade line performance covering many types of products. Cost and speed are probably the major advantages of using these models; they eliminate the need to wait for model development (such as a pooled or custom model, discussed below), which can take anywhere from weeks to six months. However, the trade-off is in predictive power. Since generic models are based only on a consumer s or business s trade lines, they don t reflect any financial information, nor any application data on the small business itself, or on the consumer profile of the small business owner, which is a strong predictor in other types of models. Pooled Models Pooled models are scoring models built on pools of business and business owner profiles and performance data gathered from financial institutions serving specific SME markets. Financial institutions today can acquire highly segmented pooled models by business type, and further segmented by criteria such as size of business, geographic location, and by financial product. When building a model for a specific business type such as auto lending leading model developers analyze large amounts of application data (business data, but also data on the business owner s consumer credit history profile) and performance data from hundreds or thousands of institutions serving that segment. Pooled models can be licensed as off-the-shelf and quickly implemented. They help financial institutions that want to enter specific market segments immediately with the confidence of minimizing risk exposure (knowing they are based on the data from other institutions serving the market). They also provide the flexibility to enter a variety of markets: leading model developers offer pooled models for nearly 80 market segments Fair Isaac Corporation. All rights reserved. page 4

5 While pooled models may provide greater predictive power than generic models, and in some cases more predictive power than expert models (discussed next), they will not be as strong as custom models. However, they provide greater flexibility in entering a wider variety of markets, and don t require the large financial outlay of custom model development. Custom models can be better if there is enough data which usually means 1,000 bad account records as a desired amount of data. Expert Models For financial institutions that lack sufficient data for empirical model development, expert models provide a means to apply scoring technology, or predictive analytics, to improve risk assessment for a particular market segment. These models are named expert models because they are built from the expertise of model developers who have vast experience in developing similar types of models in similar markets. In lieu of analyzing data, the modelers use their expertise to determine the best predictive characteristics to include in the model for identifying levels of risk. Generic Models Pooled Models Expert Models Custom Models Expert models provide greater $ $$ $$$ $$$$ predictive power than generic Days to install Weeks to install Weeks to install Months to install scores, and can serve as an entry point to a path of further No additional No additional No data at start, Needs lots of data needed data needed data needed later data now modeling use and sophistication: As the institution uses the expert Good Very good Ongoing model Ongoing model performance performance maintenance Maintenance model, it can collect application and performance data based on Better Ideal performance performance its decisions from the model that can be applied to future custom model development. Requiring less development time than With a variety of models to choose from, there s an entry point for any financial institution with SME portfolios custom models, expert models to implement scoring into their decision-making operations. An institution can select a model type based on help institutions quickly enter factors such as budget, data availability, time-to-market and business objective, as well as the advantages and new markets, or begin applying disadvantages of each model type. scoring to markets the institution currently serves. However, the professional expertise needed to develop an expert model may not always be readily accessible to some financial institutions, in which case pooled models offer an effective and efficient alternative. Custom Models An institution serving a particular SME market segment for an extended period of time may have collected sufficient applicant and performance data to develop a custom model for that particular portfolio. Offering the most powerful predictive performance of all model types, a custom model can benefit the institution in several ways. For example, if the institution desires to reallocate underwriters expertise to new market segments, the model could be gradually used in place of their judgmental decision-making process. However, not every institution is in a position to develop a custom model, or have a model developed for them. First, sufficient data needs to be collected for a model development, which in itself requires careful planning. Custom models can also require a significant expenditure outlay and detailed budget planning. In addition, development can take up to six months, not to mention subsequent time for training and transitioning to the model Fair Isaac Corporation. All rights reserved. page 5

6 Custom models have helped institutions achieve significant, quantitative payback including 20% to 30% increases in approvals and 15% to 30% reductions in bad debt; however, while models are recommended as the cornerstone of the originations decision, in order to be truly effective, they must support organizational credit policy and be viewed as part of an overall strategy. This strategy should incorporate the effective use of judgmental overrides, which, if used properly, can provide the greatest potential for improving the credit-granting decision and yield greater improvements in portfolio quality.»» The First Step in Adopting Scoring: Addressing Common Challenges Without prior experience in SME scoring, many financial institutions have common, understandable concerns mostly directed at measuring the value of scoring, and disruption to operations. The following concerns are the most common objections posed by these institutions. CONCERN: I don t understand how I can depend on a model to always make the right decision. Financial institutions first need to understand what scoring is, and what it isn t. That starts with understanding that scoring models are not crystal balls, and will sometimes drive an approval for a business that will go delinquent or default on an agreement. Here s why. Scoring models are built on odds-to-score ratios. When data on businesses are analyzed by the model (and its underlying algorithm), the model generates a score for each business that reflects its odds of repayment on the financial product (loan, credit, etc.). For example, a score of 400 might correlate to an odds ratio of 8:1 meaning for every eight businesses that repay as agreed, one will fail to repay as agreed (note that this is just an example and that all models produce various odds-to-score ratios). In this way, models rank-order businesses in terms of their likelihood of repayment. Lenders then are in complete control based on their risk criterion, they choose what decisions to make (accept/decline, pricing, etc.) based on a score s correlating odds of repayment. So using scoring does not always result in a good decision. Even with a score that correlates to 100:1 odds, a default is possible. But what institution wouldn t take those odds? CONCERN: I don t want to replace underwriters or my current practices with scoring. I think my policies and the resources I use such as business bureau data are sufficient. Scoring doesn t replace underwriters it helps organizations focus underwriter expertise where it is most needed. Scoring can be deployed in a couple of ways working in a completely automated fashion, or in conjunction with underwriter reviews. First, as discussed earlier, financial institutions can rely on scoring models to more quickly and costeffectively expand into new market segments in order to generate growth, or increase profitability in existing portfolios through greater operational efficiencies and strong risk management. Scoring is ideally suited to automate decisions for low exposure amount financing to boost volume without significant expenses, enabling underwriter expertise and policies to be applied to higher dollar amount financing. For example, it doesn t make sense for an underwriter to be evaluating three years of financials and other data for a $20,000 loan; and, conversely, it doesn t make sense to apply scoring on its own to a $1 million loan. Therefore, scoring opens the door to growth by quickly approving tens or hundreds of those $20,000 loans. However, it should also be noted that even when using scoring, underwriters have the ability to review the score and override the score decision, if necessary Fair Isaac Corporation. All rights reserved. page 6

7 Scoring can also be used as just one of many decision factors in an underwriting process. As underwriters weigh factors in their evaluation process, a score can be considered as just one input in a decision. The weighting of a score in a decision depends on factors such as business goals, current promotions, risk profile, and also the experience of the underwriter. In addition, financial institutions should keep in mind that they cannot always rely on business bureau data. For many market segments, institutions may not get a hit on [smaller] businesses; and if a lender gets a hit, the data is usually sparse. Scoring helps compensate for situations when business bureau data is unavailable or unreliable by analyzing additional inputs such as consumer information on the business owner, application information and financials. CONCERN: We believe in relationship banking, and rely solely on the three Cs. In scoring models, data is correlative, and therefore measures of creditworthiness, capacity and collateral are reflected through the overall data-based credit profile of the business owner. In an automated environment, scoring helps lenders better serve the business with a streamlined process and fast approval notification. It improves customer service, and attracts businesses. In a judgmental environment, scoring can be used as a complement to deepen lenders insight of a business. For example, a community banker may know the reputation of a local business, and gain insight through analysis of financials. But the addition of the profile generated through scoring can provide the lender with another dimension of the business, and enable the lender to respond accordingly with more precise actions. CONCERN: We already use our own in-house models. Some financial institutions use custom models. Their belief is that custom models will always outperform generic or pooled models, and that custom models provide their organization with a competitive advantage. While these reasons are generally valid, there may be some circumstances in which efficacy may, in fact, be sacrificed. First, custom models may be built on a limited dataset which does not allow for segmentation and model development on specific, unique sub-populations. While the custom models may validate on these sub-populations, they will generally not be as effective at predicting behaviors as models built specifically on the sub-population data. In addition, they must be validated on new markets before they can be deemed suitable, and that analytic effort is usually hampered by limited data and experience. Custom models provide a competitive advantage based on the strength of their unique data; but that strength is limited to the portfolios the data is based on, and therefore doesn t help institutions expand into new markets. By contrast, pooled models are built from large datasets that target a specific portfolio type (i.e., SME) and cover multiple segments of interest. They therefore provide much greater predictive power for those specific markets. Second, the performance of an institution s in-house custom models will, as you would expect, degrade over time. This deterioration is due to many factors, such as changes in the market, economic cycles, and simply the length of time since the model was developed. It is expensive to re-develop models, and institutions often wait until the performance metrics are simply too poor to tolerate before engaging in redevelopment efforts. Performance, in the meantime, degrades. Pooled models, when used on a second dimension, provide a strong means to shore up an institution s custom models. They can provide a refined assessment of risk Fair Isaac Corporation. All rights reserved. page 7

8 CONCERN: Won t this make demonstrating regulatory compliance difficult? Another benefit of scoring is that it makes compliance easier. Scoring provides lenders with an empirical method to demonstrate consistency and accuracy in lending decisions. With scores (on a business) and resulting performance data, a financial institution can quickly and easily demonstrate that its decisions were based solely on risk factors. Scoring provides a consistent, common language to summarize performance results and to document how decisions were made. Where human intervention occurs, it s comparatively easy to capture why there is an override or other judgment. In addition, even for financial institutions that are not regulated, scoring provides an empirical means to periodically validate the continuing precision of models essential in a dynamic economy. For example, periodic evaluation of scores related to ensuing performance can confirm a model s continuing strength, or highlight weakening of the model. Strategies based on scores can also be monitored in this way.»»conclusion: The Ideal Time to Implement Scoring Technology Many financial institutions with SME portfolios have realized the value of scoring to build meaningful growth. In addition, these institutions have learned that by using scoring they can continue to grow even in weak economic periods. That s why financial institutions inclined to adopt scoring technology should not let the economy dissuade them. Growth, from improving operational efficiencies within existing portfolios and from expanding into new market segments, is possible right now. And as the economy does recover, those institutions will have a major competitive advantage by being ready to provide immediate funding to reviving businesses Fair Isaac Corporation. All rights reserved. page 8

9 about FICO FICO (NYSE:FICO) delivers superior predictive analytics solutions that drive smarter decisions. The company s groundbreaking use of mathematics to predict consumer behavior has transformed entire industries and revolutionized the way risk is managed and products are marketed. FICO s innovative solutions include the FICO Score the standard measure of consumer credit risk in the United States along with industry-leading solutions for managing credit accounts, identifying and minimizing the impact of fraud, and customizing consumer offers with pinpoint accuracy. Most of the world s top banks, as well as leading insurers, retailers, pharmaceutical companies and government agencies, rely on FICO solutions to accelerate growth, control risk, boost profits and meet regulatory and competitive demands. FICO also helps millions of individuals manage their personal credit health through Learn more at For more information North America toll-free International web (0) FICO 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. 2934WP 01/13 PDF

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