»From» Industry Consensus to Business- Specific Actions

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1 Customer Centricity: Four Bank Success Stories Using predictive analytics and decision management to put customers at center stage By Dr. Andrew Jennings, FICO Chief Analytics Officer and Head of FICO Labs Number 78 There s a good deal of consensus around the idea that retail banks need to become more customercentric. It s a key topic with nearly every company I ve met with and nearly every industry forum I ve attended over the past few years. Publications by Gartner, McKinsey & Company, Ernst & Young, PricewaterhouseCoopers, Deloitte, the Wall Street Journal, the Financial Times, the Economist, Businessweek, Forbes and, of course, FICO have all addressed it. Nevertheless, many banks have yet to swing into action. I often hear executives and managers voice frustration at the lack of a navigable map between lofty industry goals and their own particular business situation. Yes, we agree that we need to become more customer-centric. Now what do we do? In this paper, I tell the stories of four banks that are taking significant strides on their own paths to customer centricity. They start from very different places, and not just in terms of geography. I ll be telling the stories of banks in markets obsessed with retaining and building customer relationships, as well as markets focused almost solely on growth. Of banks building on an already strong base of analytic decisioning, and banks still lining up the basics. How did a bank boost profit per customer by $121 and achieve a 600% ROI in one year? The banks I ll be talking about still have a way to go before they can claim to be fully customer-centric so why do I say they are successful? In each case, they re beginning to achieve the kinds of results in increased profits, expanded market share, reduced compliance risk and higher customer satisfaction we d expect to see in the transformation to customer centricity. Their achievements are good indicators that, as an industry, we re on the right path. Make every decision counttm

2 »From» Industry Consensus to Business- Specific Actions What s the right path toward customer centricity? No matter where your operations are today, a clear map can be drawn and next steps identified. And while your map will be specific to your business, the successes of other banks sharing some of your circumstances can provide a helpful template. Here are the stories and maps of four FICO clients that have succeeded in taking big steps toward customer centricity. Doing the right thing by their customers not only in terms of conduct but in more sensible and tailored allocation of products and limits, and better communication is leading to improvements in such key performance indicators (KPIs) as product utilization, customer satisfaction and compliance risk exposure. These upticks are, in turn, leading to improvements in market share and various financial performance measures. While I ll discuss these successes, I m also going to be frank in talking about problems faced and overcome. For this reason, I ll refer to the subjects of these stories only as Banks A, B, C and D. 1. Increasing Profit by Managing the Complexity of Customer-Level Decisioning Figure 1: Making decisions at the customer level can quickly become very complex NUMBER OF UNIQUE DECISIONS 10,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1, exposure levels = 10 possible decisions The idea of customer-level decisions makes obvious business sense. Take credit decisions, where it s clearly sensible to consider all relevant factors. These include the credit products a customer currently has or that the bank might offer, and the risk and reward implications of each. But the reality of making such decisions is enormously complex. Suppose a bank wants to determine an overall credit limit for each customer and allocate it across four credit products. As illustrated in Figure 1, if there were 10 possible initial exposures for each of the 4 10 exposure levels = 10,000 possible decisions NUMBER OF PRODUCTS four products, the number of potential credit limit combinations would be 10 4, or 10,000. And that doesn t take into account the complexity of all the input factors that would need to be considered to reliably and consistently select the most profitable decisions from the 10,000. Such complex decisions will become routine for many banks, however, as customercentric operations become the industry norm. Mathematical optimization is the key to getting a handle on this complexity and making customer-level decisioning widely practical. It s an ideal approach for finding the best balance between different, often conflicting profit drivers. June page 2

3 FICO case study: Bank A A Relevant to any bank that: f foperates in saturated credit markets where increasing profits from existing accounts is critical f fis concerned that accountlevel decisions could create over-exposure with some customers f fhas an extensive current account base generating rich data This regional division of a global banking group is a leader within its markets for net income, profitability, efficiency and fees-to-expenses ratio. Some of this success comes from making customer-level decisions, including setting global credit limits. Even so, the bank was leaving profit on the table at originations, as it was making exposure decisions based only on customer monthly income. Exposure allocation across products was all about controlling losses scoring models were used to define risk profiles and set eligibility cutoffs. Revenue criteria (e.g., which customers would use higher limits and to what extent, attrition risk) weren t being taken into account at all. To make more profitable decisions, the bank needed a comprehensive, scientific way of balancing risk with potential reward in four packages of its retail credit products. Each package (we ll call them Bronze, Silver, Gold and Platinum ) included a deposit account with an overdraft and a Visa card, as well as an opt-in line of credit. All packages except the Bronze also included an Amex card. Once the decision is made that a customer qualifies for one of these packages, how could the bank not only set the most profitable overall credit limit, but also distribute that exposure across component products in the most profitable way? Decision strategy optimization provided the answers. The first step was modeling the decision. Decision models are essential when dealing with complexity because they make explicit the relationships between all important factors. Models capture these relationships in mathematical equations (generally represented by arrows in decision model diagrams) and tie them to an overall objective like maximizing profit. As I mentioned, the combinatorial possibilities in decisions spanning multiple products can reach dimensions that are difficult to manage. One way to simplify these complex decisions is to break them into parts. As shown in Figure 2, we performed a two-part optimization, with both aimed at maximizing customer-level profit. Figure 2: Two-level optimization simplifies the identification of the best decision strategies Optimization #1: Exposure distribution across products Optimization #2: Package global exposure Risk: Inputs Application data Product info Customer info Overdraft Visa card Losses: PROFIT Credit bureau data Activation: Amex Amex Card Opt-in: Line of credit (loan) Line of credit (loan) page 3

4 The first optimization recommends distribution of package global exposure across the products in the package. We start with a global exposure based on historic amounts. The optimization takes into account package-level constraints such as exposure balance across products. It also incorporates product-level profit objectives, as well as product-level constraints (e.g., maximum exposure for Visa of no more than three times annual income and minimum loan exposure of $200). It s worth noting that use of constraints in mathematical optimization can also help ensure your decision strategies will meet local credit regulations, while still achieving the best results for the bank. The second optimization recommends the total package global exposure. It takes into account global constraints on the average exposure and loss rate. The average exposure was constrained slightly above the current business as usual (BAU) strategy, and the loss ratio was constrained below the BAU loss rate. Because diagrams like these can only summarize what s happening, it s worth pointing out that there are deeper components also handling a piece of this complex decision. As shown in Figure 3, these include activation models, which predict the likelihood a customer will take up an offer. They also include action-effect models, which, in my view, provide the real core of power for decision strategy optimization. They predict customer reactions to each of the possible bank actions and the impacts of these reactions on key performance indicators like revenue. Action-effect modeling is key to performing reliable simulations before deploying a new decision strategy. Figure 3: decision models incorporate many other models making predictions at the product level Optimization #1: Exposure distribution across products Optimization #2: Package global exposure Risk: Inputs Application data Product info Customer info Credit bureau data Activation model predicts customer likelihood Activation of product take-up Amex Overdraft Action-effect model predicts customer reactions (repayment behavior, Visa card etc.) to bank decision (productlevel exposure) and impact on revenue Amex Card Losses: PROFIT Opt-in: Line of credit (loan) Line of credit (loan) page 4

5 Figure 4: Using simulation to adjust constraints, you can explore a range of optimal operating points PROFIT Customer net income Optimized strategy 90% 110% 130% 150% 170% 190% % OF BASELINE GLOBAL EXPOSURE Figure 5: Performance lift from customer-level decision optimization Product Objectives Results per customer per 12 months Gold and Increase income without $116 income $121 profit Platinum increasing losses $5 losses Silver Reduce losses and $28 income $44 profit increase income $17 losses Bronze Reduce losses without reducing income BAU Customer package exposure In fact, when you ve optimized a decision strategy based on carefully constructed action-effect models, using a simulation tool is almost like having an interactive map with levers you can adjust to explore and understand performance drivers and trade-offs. What if? scenarios are reliable because they re rooted in the data-driven predictions about behavior and results. Bank A explored a range of optimal operating points, shown in Figure 4. This range which is often referred to as an efficient frontier demonstrates how the optimal decision strategy moves as the bank loosens or tightens the constraint of global exposure. The blue dot directly above the gray BAU dot shows that strategy optimization raises profit by 27% with no change in global exposure. The green dot (two positions to the right) shows that profit goes up even more when global exposure is allowed to rise to 110% of BAU. The inset chart shows that one of the ways the optimal strategy is raising profit is by doing a better job of balancing risk and reward based on measures like net income. Note the much smoother relationship between customer net income and package exposure for the optimized strategy, which means that more appropriate limits were being provided. 0 change income $9 profit $9 losses Did it work? Yes, quite spectacularly. Optimal product limits encouraged improved spend, balance and payment activity. As shown in Figure 5, Bank A reached all of its portfolio objectives while increasing overall profit. Results were so strong, in fact, that the bank was able to achieve project payback in six months and a one-year return on investment of 6 to 1. Next steps on Bank A s map to greater customer centricity: Explore additional opportunities for optimization at originations. This project had the side benefit of bringing risk management and marketing together at the same table to discuss strategies and ongoing results. It opened everyone s mind to the potential benefits of trying new approaches. Improve targeting of customer acquisitions. One add-on benefit of doing an optimization project in originations is that it helps identify those segments of applicants that are the most profitable. These can be used to target marketing and customer acquisition activities on getting more of these profiles of applicants through the door. page 5

6 Implement customer-level strategy optimization in other decision areas, such as customer management and collections. Help propagate strategy optimization across other units and markets. This company, which has a solid record of leveraging both global and local strengths, can use the success of this business unit as a template for others. Such an approach was taken by another FICO client, who created an optimization factory to accelerate the deployment of this advanced technique across more than 20 countries. In addition, implementing centralized automated model management would support Bank A s drive for higher profits by ensuring that all models and optimized strategies are frequently monitored, validated and updated as market and economic conditions change. 2. Boosting Market Share by Stepping Back to Put the Basics in Place As we discuss the way forward toward greater customer centricity, it s important to acknowledge that every bank has taken a different path to arrive at where it stands today. Acquisitions and mergers have left many banks with systems and processes that are far from what one would envision if designing customer-centric operations from a green field. Other banks have developed in markets that have grown like wildfire, consuming all of their resources just to keep up with demand and win market share. In such cases, banks often reach a point where they can no longer move forward without taking a few steps back to make sure they have a strong foundation in place for further growth. FICO case study: Bank B B Relevant to any bank that: ffoperates in growth markets f fis concerned about rising loss rates, particularly from new customer segments f fis subject to increasing regulation that could impact revenue and profits Having attained the #2 market share position in a maturing growth market, this bank needed a way to keep growing. After years of aggressive expansion, including into subprime populations, Bank B was seeing revenue growth slow and delinquency rates climb. As economic conditions began to deteriorate, bank executives cut marketing budgets in an attempt to rein in losses. Increasing competition made it more difficult to book new accounts and keep good customers. The bank slid to #4 in market share. To turn this trajectory around, executives knew Bank B had to make better decisions to drive profitable growth. Seeing the profit gains achieved by other banks using decision strategy optimization, they called in FICO. In some ways, Bank B was ready for optimization. It was an analytically astute organization, with a highly educated executive team and a staff comprising Ph.D.s in math, statistics and economics. It had analytics-driven decision applications for originations, customer management and collections. But Bank B wasn t using these applications for all they were worth even at the account level, much less for customer-level decisions. Because the bank wasn t following basic best practices for segmenting account populations and testing targeted treatments on segments, it wasn t obtaining full value from its data-driven applications. And these operational deficiencies would certainly prevent the bank from achieving the performance boost it was aiming to achieve from strategy optimization. page 6

7 Figure 6: Raring to go, but being held back by gaps in basic best practices Marketing Originations Customer Management Collections/ Recoveries No sharing of customer data or segmentation or decision outcomes Targeting new population (higher risk) with no historical data Heavy reliance on fee income Highest APR in market High attrition Deteriorating score performance Operational application errors Incorrect product assignment Outdated behavior score and credit line management strategies No systematic test-and-learn process (champion/challenger) for decision strategies Figure 7: Regaining market share and focusing on even higher performance Little differentiation in treatment of delinquent accounts for decision strategies No systematic test-andlearn process (champion/ challenger) for decision strategies No effective monitoring of results Bank B s operational problems were partly caused by a familiar structural problem: organizational silos. As shown in Figure 6, with each decision area operating essentially on its own, there was little consistency to how accounts within a population segment were treated. A customer with a delinquent account might be receiving calls from collections, while at the same time being awarded a credit line increase from customer management. Another problem, common in growth markets, was that the abundance of new customers enabled Bank B to succeed without having to take any real notice of the differences between them. More often than not, all accounts were treated the same, causing underperformance across the customer lifecycle. Lack of differentiation in marketing resulted in high acquisition costs and attrition rates for new accounts. In originations, it resulted in incorrect product line assignments and credit line decisions that drove revenues down and losses up. In collections, it resulted in wasted resources, high costs and lackluster results. MARKET SHARE #2 #2 #4 TIME Fix gaps and adopt best practices The good news for banks in such circumstances is that simply addressing operational deficiencies can substantially lift performance. FICO helped Bank B improve segmentation of account populations at originations, then carry it across decision areas. In each area, we helped the bank target decision strategies to segments, then measure effectiveness through controlled test-andlearn cycles with production populations. Ongoing testing, using the best practice of champion/challenger testing (pitting an alternative strategy against the current best performer, then promoting the winner) drives continued performance improvements. As a result of these changes alone, Bank B has been able to grow profitably, regaining its #2 position in market share. In addition, the bank now has the operational fundamentals in place to begin using optimization at the account level. By rapidly identifying winning strategies that might otherwise be discovered only through many test-and-learn cycles, Bank B can expect to achieve additional performance lifts in the range of 10 to 30%. page 7

8 Next steps on Bank B s map to greater customer centricity: Implement decision modeling in one decision area. Bank B should start with improving strategies in one area, such as originations for credit cards, by explicitly modeling the relationships between all important factors and tying them to KPIs. The bank can gradually expand this practice into all decision areas for that portfolio. Add simulation capabilities. The decision strategies resulting from modeling can be used with simulation tools to project KPI impact. Bank B can then compare results of champion/ challenger tests against these simulated results to identify performance gaps, which are opportunities for learning and improvement. Pilot optimization. The next step is to use mathematical optimization to identify the best decision strategies to achieve an overall result, given all objectives and constraints. Bank B can use simulation with optimized strategies to explore the impact on KPIs of adjusting constraints and making other trade-offs. The bank will then be able to select an optimal strategy and test it as the next challenger. Expand optimization. Bank B should then expand use of optimization within the pilot decision area. It could also begin standardizing metrics and propagating strategy optimization across other account-level decisions. Move toward customer-level decisioning. Bank B can gradually enhance its account-level decisions by bringing a few key pieces of data from other product areas into its modeling and optimization processes. 3. Reducing Compliance Risk by Strengthening Customer Centricity at Its Core Customers should be treated in a fair and ethical manner it s a simple statement and a principle that lives at the very core of a customer-centric operation. But it s not so simple to ensure across all the automated and person-to-person interactions that occur between banks and their customers today. Even leaders in customer centricity may discover they are more vulnerable in this respect than they had thought. Fortunately, the analytic and decision management solutions implemented across customer-centric operations also add strength at the core. FICO case study: Bank C C Relevant to any bank that: f foperates in markets where regulations are increasing or changing f fis concerned that its sales force is making the right deals for both customers and institution f fwants more visibility into and control over the quality of its interactions with customers This major bank, like many others in its market, was fined by regulators for misconduct in selling fee-based extra services to new accountholders. Moreover, the light shined on this problem made it clear that over-zealous sales activities weren t the institution s only vulnerability to fines and reputational damage from regulatory noncompliance. Bank C executives began asking questions such as: Are automated originations processes confirming that customers understand and accept product and credit terms? Are collections agents making the required disclaimers at the proper time in the conversation, and avoiding using inappropriate language and threats? The answers convinced them the bank needed to put in place policies, systems and processes to provide better visibility into and control over compliance risk exposure. As part of this solution, Bank C deployed analytic models to score new sales (based on customer and product characteristics) for compliance risk. The objective was to identify potential high-risk cases for review and follow up with the customer. To improve efficiency, the bank got started by working with FICO to implement an automated scoring service across a number of different product lines, including credit, savings and insurance. page 8

9 But in such a dynamic market, where regulations, bank products and customer behavior are all changing, how could the bank ensure that the deployed models continued to accurately identify the risk level of these sales? Factors indicative of high risk today might not be significant a few months from now, and vice versa. The solution was to also implement centralized, automated model management, illustrated in Figure 8. Regularly scheduled model validation processes will fully document analytic performance, generate alerts when it drops below specified thresholds and even capture actions taken in response to validation findings. An accompanying model development environment will allow the compliance risk models to be quickly refreshed and deployed back into the scoring service. This framework also has the potential to support other conduct risk assessments, such as those used for collections calls. Figure 8: Managing the analytic models that help reduce conduct and compliance risk Centralized, automated model management Tracking, monitoring, ongoing validation, alerts, management reporting, etc. Model Data Mart Scoring models analyze just-booked sales for potential conduct issues and other risk factors that might require attention Speech, text and predictive analytics detect problems in collections calls, like forgetting disclaimers and using inappropriate language or threats /a/s/k/t/ +! + + /b/e/s/t/ Follow up with a customer service call or......confirm with a self-service message Next steps on Bank C s map to greater customer centricity: Extend centralized, automated model management to all analytics used in customer decisions and interactions. As Bank C applies more analytics to its customer decisions and interactions, it can bring all of these within the same central oversight and control discussed above. Apply a wider range of analytics to managing compliance risk. Analytic solutions that support social link analysis have the potential to help Bank C discover any concentrations of behavior across employee groups that might be increasing the company s compliance risk. Text and speech analytics can alert supervisors in sales, customer service or collections to conversational characteristics and patterns that may indicate compliance problems. Use intelligent self-service communications to confirm customer assent. Bank C can do more customer follow-ups without increasing expenses by using automated communications to check in with customers via their mobile devices or other preferred channels after a sale or other transaction. With a touch or two, customers can confirm that they understood and accepted the terms, and provide feedback on their satisfaction with the process. page 9

10 4. Raising Customer Satisfaction by Improving Communications Customer centricity is not about becoming so focused on engaging customers that we overwhelm them with attention. Rather, it s about making very careful decisions whether for marketing, collections or fraud management about why, when and how to make contact. Analytics and intelligent automated communications are the keys to making these individualized decisions efficiently and consistently across a large customer base. When customers are contacted with relevant information through the right channel at the right time, most welcome the interaction. (That was the attitude of a majority of respondents to a FICO global survey of thousands of smartphone users.) Such contacts may also increase customers perceptions of value and influence their behavior in ways that drive revenue and profit. FICO case study: Bank D D Relevant to any bank that: f fis looking for ways to provide additional services that customers value f fwants to reduce fraud losses and operational costs f fhas a high number of customer complaints about fraud management actions This bank, a global leader in credit cards, wanted to demonstrate its commitment to protecting customer information and assets from fraud, as well as its use of innovative technology for that purpose. By adding intelligent communications management to analytics-driven fraud detection, Bank D accomplished this goal, while also dramatically improving fraud detection performance and efficiency. In the case of Bank D, the new automated process, which generates batches of alerts every 15 minutes, replaced a slow, totally manual and very costly outbound dialing process. Looking at the diagram in Figure 9, you can see how this approach tightly links fraud risk analysis and customer contact actions with data driving both. Once the transaction is scored for fraud risk, the communications manager pulls together other data from multiple sources in real time to assess whether or not to contact the customer. Bank D s business rules drive that decision. If it s a go, a subsequent decision is then made (based on the level of urgency and the customer s behavior history and preferences) about how to make contact. In many cases, this contact happens in a self-serve mode: Customers are able to auto-resolve the situation by simply tapping a button or speaking a word to verify that it s really them making the transaction. Figure 9: Creating a closed loop between fraud risk detection and customer contacts 1 Assess 2 Decide 3 Act 4 Resolve Bank Fraud Detection System Customer Case Business Rules Voice SMS Internal & External Data Sources Mobile Direct Operational Data Captured for Fraud Analytics page 10

11 Read other Insights white papers on Big Data, customer centricity and analytic innovation: Harnessing the Speech Analytics Advantage (No. 76) Satisfying Customers and Regulators: Five Imperatives (No. 75) Extracting Value from Unstructured Data (No. 71) When Is Big Data the Way to Customer Centricity? (No. 67) Bank D is using the system not only to prevent card transaction fraud, but also to protect against identity theft, a particularly distressing crime for customers. When the fraud detection system is notified of a change of address the potential first sign of this crime the communications manager automatically sends out a text message via , SMS or mobile direct, in order to confirm whether the change was legitimate. According to a Bank D survey, customers are impressed. Not only were 76% of respondents highly satisfied with auto-resolution fraud checks, but 89% said they had increased confidence in using their cards again. It s quite likely that many of these more confident customers will increase utilization, thus positively impacting Bank D s revenue and profit. In addition, the system has reduced declines by 32% and point-of-sale referrals by 80% (with respective declines in complaint rates of 27% and 11%). Both of these improvements are also likely to raise utilization, fee income and profit. The stunning thing about these results, to my mind, is that Bank D achieved them while resolving 250% more fraud cases with zero staff increase even more evidence that becoming customercentric is good for bank financial performance. Next steps on Bank D s map to greater customer centricity: Add more advanced analytics to minimize false positives. Bank D is upgrading its fraud detection system to reduce the incidence of false positives (legitimate transactions scoring high for fraud risk). The upgraded system will allow for incorporation of the most advanced analytics. This may include adaptive analytics, which enable traditional fraud models to dynamically adjust to changing customer and fraudster behavior. Extend the solution across portfolios and products. Bank D can bring fraud scores and data from all of its credit card product lines (and other products) into the intelligent communications manager. In this way, the bank can assess customer behavior and preference data from the entire relationship and coordinate all contacts.»conclusion» Today, retail banks around the globe are working to become more customer-centric. They re doing it because the links between doing the right thing by your customers and improving the financial performance of your bank have never been clearer. The results reported here including increased profits, expanded market share, reduced compliance risk and higher customer satisfaction came from making step-by-step improvements in everyday decisions. Ultimately, customer centricity is a marathon, not a sprint. The most successful banks will be those that are smart about which steps to take next and good at learning as they go. The Insights white paper series provides briefings on research findings, technology innovations and recommended best practices from FICO. To subscribe, go to Dr. Andrew Jennings is the Chief Analytics Officer and Senior Vice President at FICO. Since joining FICO, he has worked with leading institutions worldwide to raise analytic performance. Previously he held senior analytics positions at Barclays and Abbey. Dr. Jennings has a Ph.D. in economics from the University of Nottingham. He regularly blogs on the Banking Analytics Blog and FICO Labs Blog. For more information North America Latin America & Caribbean Europe, Middle East & Africa Asia Pacific (0) info@fico.com LAC_info@fico.com emeainfo@fico.com infoasia@fico.com 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. 4009WP 06/14 PDF