In the latest edition of FINsights, Infosys partners with FICO to walk you through the various issues and roadmaps of analytics with the goal of providing insights to help launch successful analytics initiatives.
Transcript of "FINsights: Analytics in Collaboration with FICO"
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PrefaceWelcome to the Analytics in Financial Services issue of FINsights!Analytics. As the Information Age advances, business and social discussions about analytics abound.Particularly among executives who are focused on profitable growth and risk management, theeffective use of analytics is increasingly viewed as critical to success.In response to growing demand from our clients for information and services related to analytics, wevededicated this issue of FINsights, Infosys thought leadership journal for the financial services industry, tothe topic of analytics. Indeed, this quarter we decided to do something special: weve merged the expertiseof Infosys and FICO to create a one-stop compendium of viewpoints, roadmaps and research piecesaddressing a topic which carries increasing importance in our data-drenched world.The last decade has been characterized by an explosion in the volume and complexity of information.Organizations have developed enormous data warehouses cataloguing everything from transactiondetails to online activity, to dates of birth. For financial institutions, this abundance of informationrepresents both a powerful opportunity and a daunting challenge. When effectively organized,analyzed, and acted upon, it can drive customer retention, reduce credit risk, and improve cross- andup-selling. However, arriving at the point of proper application requires significant knowledge of andinvestment in analytics—not only the science, but also the practice of applying it when facingtightened regulatory, fiscal and other real-world challenges.Though the potential of analytics is nearly unlimited, many organizations get caught up in the “whens”,“wheres”, “whats” and “hows”. This issue has been created as an enabler—a tool for our readers to tap intothe true value of analytics and fully realize the potential of the information available to them. In it, weaddress a variety of analytics trends and challenges which have emerged in recent years. From masteringthe intricacies of unstructured analytics, transaction analytics and adaptive analytics, to the governanceand management of information, this issue fuses the know-how of Infosys and FICO to help informyour analytics strategy.We hope you find the result of our partnership enlightening. Each article in this issue was forged froma unique combination of domain expertise, discussions with clients and analysts, exhaustive research,and inter-company reviews. Our contributors put a tremendous amount of time and thought intoeach article, with the goal of producing an issue of lasting value – one that you keep close at hand inthe months ahead. From ideation to publication, this issue has been exciting to develop, read, anddiscuss. We hope youll agree and look forward to hearing your thoughts, comments and suggestions.Ashok VemuriMember, Executive Council and Global Head, Banking & Capital Markets PracticeInfosys Technologies LimitedCharles illExecutive Vice President, Sales and MarketingFICO
ContentPrefaceFrom the Editors’ DeskAnalytics for a New Decade01. Post-Crisis Analytics: Six Imperatives 0502. Structuring the Unstructured Data: The Convergence of 13 Structured and Unstructured AnalyticsRevitalize Risk Management03. Fusing Economic Forecasts with Credit Risk Analysis 2104. Unstructured Data Analytics for Enterprise Resilience 2905. Why Real-Time Risk Decisions Require Transaction Analytics 37Optimize to Drive Profits06. Ten Questions to Ask of Your Optimization Solution 4707. Practical Challenges of Portfolio Optimization 55Understand Your Customer08. Analytics in Cross Selling – A Retail Banking Perspective 6109. Analytics as a Solution for Attrition 6910. Customer Spend Analysis: Unlocking the True Value of a Transaction 77 011. A Dynamic 360 Dashboard: A Solution for Comprehensive 85 Customer UnderstandingFight Fraud More Effectively12. Developing a Smarter Solution for Card Fraud Protection 9313. Using Adaptive Analytics to Combat New Fraud Schemes 10314. To Fight Fraud, Connecting Decisions is a Must 109Improve Model Performance15. Productizing Analytic Innovation: The Quest for Quality, 117 Standardization and Technology GovernanceLeverage Analytics Across Lines of Business16. Analytics in Retail Banking: Why and How? 12517. Business Analytics in the Wealth Management Space 135
From the Editor’s DeskIn 1992, Walmart launched the first terabyte database—the race for information was on. In the nearly20 years since, large financial institutions and retailers alike have piled up data at a seeminglyexponential rate. 100 terabyte databases are no longer the exception, they are the norm. Businessesacross industries have been remarkably effective at amassing information. Where they have struggled,however, is effectively and consistently utilizing this information to drive revenues and cut costs.To make this data actionable, having a strong analytics program is a competitive necessity. From fraudto risk to marketing to collections, financial institutions have been accelerating their use of analytics,moving from rearview analysis of historical data to forward-looking predictive analytics—models thatare predictive and drive optimal decisions. A recent Forrester survey found that 31% of IT decision-makers are implementing or planning to implement advanced analytics packages in the near future. Ifyour bank isnt investing, your competitors are.The information race of the last 20 years has paved the way for an analytics race over the next decade.Firms want to know with more certainty how their customers are going to behave—who is likely toattrite, where they will spend next, and who is most likely to default on a mortgage or credit card—andwhat actions will guide customer behavior in a mutually beneficial direction. How effectivelystrategies are developed and implemented to extract and leverage information will likely define thewinners and losers of the financial services industry in the 21st century. In this day and age,information is indeed power.This issue of FINsights has been created to help your organization tap into the true value of analytics.We start by looking forward, delving into the musts of a post-crisis business environment, thepotential of unstructured analytics, and the advances in credit risk analysis. From there, we provide aseries of roadmaps and recommendations for applying analytics to three of the major challengesbanks face today: fraud, improving understanding of the customer, and risk management. The issuewraps up with a series of articles addressing analytics optimization, model performance, and vertical-specific analytics. We hope this issue serves as not only an interesting read, but also as a tool inlaunching and transforming your analytics initiatives.Chisoo LyonsVice President,Analytic Science,FICOSrinivas PrabhalaDelivery Manager,Head BCM STAR Technology Group,Infosys Technologies Limited
FINsights Editorial Board ASHOK VEMURI Member Executive Council, MOHIT JOSHI Senior Vice President and Global Head of Sales Head - Banking and Capital Markets Practice Banking and Capital Markets Practice Infosys Technologies Limited Infosys Technologies Limited RAJESH MENON LARS SKARI Partner Partner and Practice Manager Infosys Consulting Infosys Consulting BALAGOVIND KESAVAN CHISOO LYONS Head of Marketing Vice President Banking and Capital Markets Practice Analytic Science Infosys Technologies Limited FICO SRINIVAS PRABHALA Delivery Manager Head BCM STAR Technology Group Infosys Technologies Limited
Analytics in Financial Services01Post-Crisis Analytics: Dr. Andrew Jennings Chief Research Officer and Head of FICO Labs,Six Imperatives FICOWhile todays business press is filled with the message that analytics can help companies“do business smarter”, the largest gains will come from using analytics smarter. FICO hasidentified six imperatives for analytics in an environment where the past may no longer bea good model for the future. At the core of these imperatives is the decision model—a workingmodel that explains the relationships between all the drivers of a decision and its results.The decision model can be used in business planning and is a critical element in strategyoptimization. Using such tools is essential to understanding the business situation andcreating lasting business advantage. More analytics does not mean more of the same, Introduction however. Lessons learned from the crisis must now shape the way organizations build andOne response to the severity of the recent use analytics. Grave dangers lie in the naïveeconomic crisis is an increasing demand application of pre-packaged analytic techniques.for analytics. Forrester Research predicts thatthe market for predictive analytics and data With that in mind, this article describes sixmining will grow at a rapid pace to US $1.8 ways analytics needs to change to drive businessbillion by 2014. performance to higher levels.
buy a particular product, which related 1. Turn the “360-degree Customer view” Inside Out products are they most likely to buy within a specific range of time? Which channels does The new normal requires that companies will this customer use most, and does that pattern ultimately achieve integration and vary by season or by day of the week? coordination across multiple business lines, By using analytics to answer these kinds of products, channels and customer lifecycle questions, you can predict an individual management areas. At the same time, top customers sensitivity to the specific attributes performers are pushing beyond this effort to of an offer. You can also automatically generate bridge corporate views of the customer and population segments with similar sensitivity. focusing on developing a more holistic This is the key insight on which differentiation understanding of the customers view. In a can be built. sense, it is about “getting inside the customers head” and taking a 360-degree 2. Model the Decision to look outward. Optimize Performance To construct this inside-out customer view, While companies must continue to improve companies are bringing together data from scores and other predictive analytics, the a widening range of internal and external largest performance gains going forward will sources. The sense of urgency that came come from improving how analytics are used with the crisis is impelling them to tackle in decision strategies. It is, after all, better the thorny integration and organizational decisions that improve business performance, issues involved in sharing data across not better predictive models in isolation. product lines, channels and customer lifecycle decision areas. This means there is a need to model more than individual customers and their There is another aspect to this effort. No behavior. There is a need to model the one doubts that more data and more decision itself. Decision modeling is a relevant data lead to better models. fundamental technique for understanding Winners, however, wont just use that and improving decision strategies. data to build better models—they will use it A decision model, as shown in Figure 1, can to ask better questions. incorporate any number of predictive Take, for example, the way response analytics along with a multitude of other and propensity modeling has traditionally inputs. been done in marketing. Often what you All decision makers have a view of the way a are actually modeling is the offer you particular business situation operates just made—e.g., “how many customers in whether they recognize it formally or not. this new target population will respond to The great advantage of the decision model and accept this existing offer?” concept is that it makes that view explicit A smarter use of analytics is to model the and ties it to an objective, like maximizing offer you are about to make. That sales or profit. It also makes clear the decision means asking questions about which variables—like credit limit and interest rate, products individual customers are most or channel and price—and the constraints likely to buy next, and when they are most that need to be met, such as expected losses likely to make the purchase. If customers not exceeding some pre-determined value.6
Simplified view of a decision model for a credit line increase Figure 1At the core of this model are predictions initial expectations, rather than simplyabout how customers will react to potential following a long list of metrics.actions. Generally, these are the models that n because there is an explicit Third,determine the effectiveness of the decisions objective, the decision model forms anthat get created. However, it is very common excellent basis for the comparison andfor decision makers and analysts to create simulation of one possible strategytheir decisions with a complete absence of against another.any formal understanding of these action-reaction relationships. Not surprisingly, this In addition to making the drivers of aleads to lost opportunity. decision clear, a decision model can be “solved” by mathematical optimization.The decision model concept creates the best Optimization pinpoints the single beststarting place for three crucial steps to strategy for maximizing or minimizing aimproving decisions: particular goal. The output of optimization isnadapting the parameters andFirst, the assignment, at the level of individualconstraints of the model for some new customers, of the best actions or treatmentscircumstance forms a sound way of for what youre trying to accomplish at thecreating new decision logic. You are portfolio or organizational level. The resultingaddressing the change at the structural solution can be expressed as a decision tree.level, not editing some derived construct. Using optimization and simulation with an because the decision modelSecond, well-developed decision model, you canmakes the underlying relationships explore how much impact a currentexplicit, it becomes much clearer what constraint is having on your projected resultsinformation should be tracked to and what would happen if you adjusted it. Forunderstand if a strategy is playing out as example, you could answer the question: “Ifexpected. You can track results against we allowed a slightly higher level of bad debt 7
Optimizing a credit line management decision strategy Figure 2 losses, could we reduce attrition and improve only the economy, but changing customer profit?” As shown in Figure 2, this process behavior and new regulations. These identifies a spectrum of optimized developments are causing even more changes strategies—an “efficient frontier” of potential in consumer behavior. operating points. Exploring this frontier To cope with such upheavals, top performers helps you better understand key performance are continuing to analyze historical data, but dynamics and select the optimal operating also probing their markets by conducting point that is currently best for your business. rapid-cycle designed experiments. Theyre The drivers of an effective decision are there analyzing the results to learn as quickly as whether the decision maker recognizes them possible about changing market dynamics, explicitly or not. An analytically smart and to identify which offers, policies and business will realize this, and be well along the actions are working best now. They are path to improving business performance. following the proven principles of experimental design to learn more from fewer 3. Rethink What it Means to tests. This mature discipline aims at creating be Data-driven When the experiments whose results can be accurately Future is Not Like the Past analyzed and causes of variations understood. A well-designed series of To use analytics in smarter ways, companies experiments eliminates the need to test every can no longer rely solely on historical data. option—or even all the best options—because This can be seen clearly in the financial enough has been learned already to services industry, where consumers have extrapolate the outcome. changed their behavior in response to In a high-performing analytic organization, a economic stress. Creditors have changed certain proportion of new or “challenger” their offers and policies in response to not strategies will be purposefully designed to8
produce “controlled variation.” If you test individual with a credit score of 620 is likelyonly challenger strategies that are close to to have a default rate much closer to thathow you currently do business, you will limit traditionally associated with a score of 614.what you can learn from your data. If, In a very severe recession, the default rate ishowever, you push the design of some likely to look a lot more like that of a 600.challengers outside of the bounds of business This does not mean that the score isas usual, you will introduce variation into “broken”—it may rank-order risk just asyour data and expand what you can learn strongly as ever, but the performance offrom it. customers at each score has degraded.In a practical situation, it is never as simple With this kind of economic impactas the challenger beats the champion. simulation, lenders can select the economicWell-designed strategies do not even set forecast they believe is probable, then use theout with this goal in mind. They set out to associated index metrics to adjust their scorepush the relationships that underlie the cutoffs for credit approval. In this way, it isdecision model so that learning increases possible to maintain a fairly consistentunderstanding. Think of them not as default rate across changing economicchallengers to replace a champion, but as conditions.learning strategies. This cycle, in turn, leadsto a new champion and another round of 5. Balance Automationtesting. By exploring a wider range of with Expertisepossibilities, you increase the chances ofdiscovering unique insights that might More analytics does not mean less need forlead to competitive differentiation. You are human expertise. Automated discovery ofalso less likely to be caught flat-footed when patterns and automation of modelingthe forces of change shake up the status quo. processes are important tools that can speed the process of improving decisions. The 4. Factor Macro-economic economic meltdown has shown, however, Forecasts into Analytics that letting the machines do all the thinking can lead to catastrophe.It is clear that abrupt economic changes, such Analytic expertise informed by deep domainas severe recessions, can cause actual customer knowledge is essential to building effectivebehavior to shift from what historical data predictive and decision models. Thissays it should be. In the recent downturn, expertise is indispensable for dealing withcredit default rates rose significantly above everything from data bias to inadequatethe rates historically associated with standard sample sizes.credit score ranges. Todays analytic announcements focus on theToday, there are new analytic methods for need to use analytics to crunch throughforecasting how such macro-economic petabytes of data. While this is true, it is onlyconditions are likely to impact customer part of the story. Given todays dynamicbehavior and change results. These analytics market conditions, businesses need analyticsare now being used to generate an index of to make decisions for which they do not havehow much default rates are likely to increase petabytes of data—or at least not petabytes ofor decrease under a range of economic relevant data. That is why you need to rely onforecasts. This index enables lenders to see, analytic experts to get the most predictivefor example, that in a moderate recession, an value from what you have to work with. 9
Expertise makes the difference between analytic complexity must be justified. models that perform so-so and those that A senior manager should be able to get clear perform at a very high level. This answers to: “Why does this customer decision performance edge comes from the experts need to be so complicated? What value are ability to interpret nuances in the data we getting from it? What are we learning? in order to find the best predictive How is this going to make tomorrows characteristics for a desired performance decisions better than todays?” outcome. It comes from knowing how to Some complexity, of course, is good. It is validate models without “over-fitting” them how a company finds new niches of to the data they were developed from, and customers and tries to improve the decisions how to fine-tune models to a companys it makes about those customers. Much specific real-world business conditions. unnecessary complexity, however, can be Above all, the analysts understanding of the traced back to two root causes: 1) Trying to context in which a model will be used—and improve decisions by starting with the the decision it is supposed to improve—is decision tree rather than the decision model; absolutely critical. and 2) The inefficiencies of the decision tree as a means of representation. 6. Justify Complexity and Increase Transparency Editing a decision tree of any size is an inherently risky venture. This form of Businesses are operating in an increasingly representation is visually complicated, complex world, and it follows that analytics and therefore, working within it, one can must often be complex as well. Nevertheless, easily make mistakes. By comparison, the Comparison of two decision trees Figure 3 New strategy design tools simplify the visualization of complex strategies. This helps in the comparison of different strategies, such as in this example, which highlights the score cutoff change difference in two collection strategies.10
underlying decision model can be much nodes, and it is not unusual to have treessimpler to understand and change. Edit the with many thousands of nodes, hence thedecision model, and you will have a better magnitude of the problem.grasp of the structural reasons for the changesyou are making, and thus can have Conclusionconfidence in the decision tree derived fromit, no matter how complex. In the post-crisis era, the companies thatWhen it is necessary to edit or “prune back” succeed with analytics will not be those thatthe resulting decision tree a bit, you need simply use more of them, they will be thosetools that help you navigate across it to focus that use them in smarter ways. Above all, toin on just the places requiring attention. You really succeed with analytics, you need toalso need tools that help you understand how understand the context of the data, themuch fidelity to the decision model you are operational context of the decision and thegiving up by making these edits. underlying business relationships that you are modeling. You just cannot do that byThe best tools also enable you to compare the going click-click-click.original decision strategy to the changedstrategy. Starting with the decision model,you understand the structural reasons for the Referenceschanges. Comparing the trees then shows youthe differences in the decision logic. This just 1. Market Overview: The Business Intelligenceis not possible when comparing “raw” trees Software Market, Forrester Research, Inc.with anything beyond a small number of 10/23/09, p7 11
Analytics in Financial Services02Structuring the Unstructured Bala Venkatesh Group Project Kiran Kalmadi Senior Consultant, Shivani Aggarwal Consultant,Data: The Convergence of Manager, Infosys Technologies Infosys Technologies Limited Infosys Technologies LimitedStructured and Unstructured LimitedAnalyticsToday, 80% of business is carried out on unstructured data—documents, call center logs,blogs, wikis, tweets, and surveys. Neglecting to analyze such data leads to ignored risks,uninformed decisions, and missed opportunities. Financial services firms are increasinglyanalyzing unstructured data to understand customer needs, prevent frauds and expand thecustomer base. Analytics plays a key role in analyzing unstructured data and transforming itinto actionable intelligence. The rapid adoption of social media by the financial servicesindustry has resulted in an even higher percentage of unstructured data being generated.This has prompted firms to increasingly look at social analytics to derive structured insightsout of social media. As unstructured and structured data analytics are converging, financialinstitutions are looking for analytic vendors to come up with products that blendunstructured analytics (like social analytics) with structured analytics (risk analytics).This article analyzes unstructured data, the various analytics vendors in the space, andapplications in the financial services industry. have significant business value. However, due to Unstructured Data: What is it? insufficient search techniques and inadequate technologies, businesses are usually not ableUnstructured data refers to data that does to derive the right answers—leading tonot exist in a database. Unstructured data can inappropriate decision-making.be textual or non-textual and takes the formof text, audio and images (refer to Figure 1 for Unstructured analytics help businesses analyzemore on the different sources of unstructured unstructured data and transform it intodata). Unlike structured transaction data— actionable insights. These primarily consist ofwhich tells what customers did—unstructured text analytics, audio (or speech) analyticsdata provides insights into why they did it, and video (or image) analytics. Social mediawhat else they want to do, and what problems analytics are an important form of text analyticsthey may have. The answers to these questions making inroads.
Types of data Figure 1 Structured Semi-Structured UnStructured ? & Legacy Relation XML ? Web ? Databases EDI Documents ? E-Mails ? Spreadsheets ? Wikipedia ? ? with Proper Flat Files Multimedia (Video, ? Record Formats Audio) RSS Feeds ? Within Corporate Messages ? Static Real Time Internal ? ? Center Customer Documents Call Logs Sales Report ? ? Center Customer Marketing ? Representative Notes Material ?Rooms Trading Analysts ? ? News Breaking Reports ?Pricing Market Internet Formal/Legal ? ? Events Weather Filings (SEC, ? Actions Corporate FDIC) Chat Rooms ? Journals ? Social Media ? Platforms Text Analytics Unstructured Data Analytics Text analytics enable businesses to derive Analysis of any form of unstructured value from large quantities of text. This text data that helps transform it into can be available either in existing repositories actionable intelligence is called or can be newly generated or acquired. This is “Unstructured Data Analytics”. Structured done by extracting and interpreting relevant data analytics uses business intelligence information to reveal patterns and relationships. Figure 2 elaborates the text tools for querying and reporting, analytics process in detail. whereas unstructured data analytics utilizes text processing and keyword Text analytics is gaining importance in all searches (to locate documents in servers). industry segments (specifically the financial Unstructured analytics has evolved over services industry), mainly because of the huge time, moving towards next generation chunks of textual data being generated techniques like video and audio analytics month after month by every organization (which are rarely used in the financial (both within and outside the organization). services industry) and text analytics With the advent of Web 2.0 & 3.0, there is a (also known as text mining). greater emphasis on information-sharing14
Text analytics process Figure 2 Information Transforming Analytics Reporting Delivery Retrieval Text Collect and ? Content ? Selecting ? Different ? Taking steps to ? retrieve cleaning, attributes, mechanisms augment information removing discovering for notifying existing data & from both duplicates, patterns, results, like store enriched internal & language interpreting & dashboards, information external recognition, analyzing the alerts, etc. sources etc. resultsand user-collaboration using social In recent months, a host of social medianetworking sites. Interactions and posts on analytic products--from vendors such asthese sites play a huge role in shaping IBM, SAS, Scout Labs, and Radian6--haveconsumer sentiments about businesses, hit the markets. These products will helpservices, competition, and markets. Hence, banks and other financial services companiescorporations are investing in social media monitor and measure the performanceanalytics tools that use text analytics to of their social media campaigns—enablingunderstand customer sentiments, and banks to make informed decisions.address them proactively.Social Media Analytics Key Vendors in Unstructured Data AnalyticsSocial media analytics derive and measurekey results from social media. Social media The unstructured data analytics softwareanalytic tools use algorithms and approaches market is relatively new, and the vendors infor automated analysis of blogs, chats, emails this space are still emerging and shapingand other related social media. Key areas their lines-of-businesses. SPSS (acquired byaddressed by social media analytic tools are: IBM) and SAS are the major players. There are a number of other vendors whon the relevant blogs - IdentifyingFinding specialize in offering text analytic—tools andthe relevant blogs and forums for a tools for the monitoring and measurementbusiness of social media.nDetecting sentiment - Detecting the The Road Ahead for Vendorssentiments expressed about a company,product or new launch The future lies in bridging the gap between structured and unstructured data andnMeasuring the influence and authority creating products that blend unstructuredof key bloggers - Identifying the key analytics with structured analytics. Thebloggers and how they are influencing the challenge for vendors is creating structuredthought process of others on the web data from unstructured information. Then topics of interest masked inDetecting combination of various data sources andconversations on social network sites. different types of data to drive business is 15
definitely worth exploring for vendors. and perspectives about a companys services The road ahead for vendors will be or products through social media. Most characterized by: financial services firms still rely only on structured data analytics for customer n Integration of social media analytics intelligence. Unless these structured data with text analytics- Vendors will be analytics are blended with social media looking to integrate social media analytics, it is very difficult to achieve analytics with text analytics. In recent actionable customer intelligence. times, social media monitoring vendors have started to merge with text mining Stock Market Prediction analytic vendors. Social media Predicting stock market movements is a monitoring as a stand-alone capability of challenge for investors due to lack of vendors will, thus, not stay for long. consistent prediction methods. However, n Text analytics will be made available research shows that there is a strong as a component of other applications- relationship between news stories and stock Text analytics applications will move price movements. Predicting the stock price away from being siloed applications movements based on news items is gaining towards being a part or a component of increased importance in the text mining other applications in the business. In community. such a set-up, insights derived from Fraud Detection analysis of unstructured textual content will automatically flow on a real-time Financial institutions lose millions of dollars basis into the business for key decision- to fraud every year. In banking, fraud arises as making. a result of stolen credit cards, forged checks, misleading accounting practices, etc. n Convergence of various types of analytics- There will be a rapid growth of Financial services firms need to have combinations of different types of improved analytical capabilities to reduce analytics. For instance, text analytics fraud levels and the associated costs. with predictive analytics is expected to A common thread in the above three make rapid headway. Financial services application scenarios is a proper blend of firms will begin using a combination of structured analytics and various forms of text analytics and predictive analytics for unstructured analytics. A well-blended risk management and fraud management solution is much better than traditional on a large scale. analytics solutions. A key challenge in using unstructured Applications of Unstructured Analytics in Banking, Financial data analytics is that the unstructured Services and Insurance (BFSI) data rarely has a consistent internal infrastructure, or metadata (unlike the Figure 3 highlights a few of the specific structured data), and hence it is far more application areas within the BFSI segment complex to analyze and model. Despite these where unstructured analytics can be readily difficulties, businesses are incorporating, deployed. and should continue to incorporate, unstructured analytics efficiently into their Customer Relationship Management processes, mainly because of the extensive Customers share ideas, insights, experiences business value derived from such analysis.16
Application areas of unstructured analytics Figure 3 Customer Relationship Management Fraud Detection Stock Market PredictionImproving customer experience in a bank using unstructured analyticsScenario:noffered several services to customers through its online banking channel. EachA bank typically involved multiple complex user interactions.n using these services felt that the website was not user friendly and they startedCustomers moving to other banks based on that sentiment. They also started sharing negative sentiments about the bank in online forums.nMany customers did not even have the patience to respond to the online customer survey feedback form. Challenges Faced by the Bank Solution Recommendation using Unstructured Analytics ¡ Lack of tools for achieving a 360 ? Optimum Solution: A blend of view of customers, resulting in poor structured analytics using business customer retention rate. intelligence tools and social media analytics. Public relations disaster due to the ? ? utilizes social media analytic The bank negative sentiments expressed in tools to analyze online consumer online forums. forums and blogs. Along with this, the bank analyzes the Voice of Customer results collected from its own Business Intelligence (BI) tool. This blended solution helps to ? accurately predict the customer sentiment and invest intelligently on customer experience management. This a shift from the traditional approach of only analyzing the Voice of Customer survey and Net Promoter Score, which would not have yielded the desired result. 17
Stock market prediction using unstructured analytics Scenario: Stock market research is primarily based on two trading philosophies, namely, a) Fundamental ? - in which the prediction is based on the securitys data—price to earnings ratios, return on equities, etc., and b) Technical - which uses charts and modeling techniques for prediction. ? fundamental and technical analyses, information from quarterly reports and Apart from breaking news also plays a major role in the movement of share price. Prediction has always been a challenge, since there has not been much success in analyzing this textual data. Challenges faced Solution Recommendation using Unstructured Analytics ? stock market prediction Inaccurate Optimum Solution: A combination of when a breaking news story or Structured analytics (using technical quarterly results are declared, leading approach) and Unstructured analytics to huge losses for the brokers and (using fundamental approach). investors. In a technical approach, the historical ? data of a stock is analyzed and a linear regression is run to determine the price trend. In a fundamental approach, article ? terms in financial news, shareholders reports etc. are assigned a weight. Using the Bag of Words, Noun Phrasing & Named Entities techniques*, the textual key words that relate to “earnings” or “loss” are identified. ? of both the approaches are The results combined to arrive at a predictable outcome (up, down, or unchanged movement of the stock price). ? 4 for understanding the Refer Figure solution steps in a typical financial news analytical system. Financial news analytical system Figure 4 Fundamental Approach Technical Approach (Unstructured Analytics) (Structured Analytics) Textual Analytics New Regression Stock Techniques like DB Article Analysis Quotes Bags of Words Stock Error Analysis Stock Market Market Prediction Model Prediction * These are specific approaches that use linguistic textual representations.18
Detection of fraud in insurance companies using unstructured analytics Scenario: ? the insurance industry can occur in any stage of the transaction and can be Frauds in committed by any party (i.e. new customers, policy holders, third party claimants or any other party involved in the transaction). Typical frauds include inflating actual claims, misrepresenting facts, and submitting claims ? for damages that never occurred. Challenges Faced by Insurance Solution Recommendation using Companies Unstructured Analytics ?approach for fraud detection ·Reactive Optimum Solution: A combination of whereby the insurance companies structured predictive analytics, speech investigate only after a fraudulent analytics and social media analytics. claim is made. ? modeling combined with text Predictive ·Fraud investigation process is lengthy ? and social media analytics can be used and expensive, as an investigative to detect and prevent fraud. officer has to investigate personally to detect any suspicious activity in ? the predictive model are from Inputs to claims. internal & external watch lists of criminals who previously engaged in Limited fraud prevention mechanisms. ? fraud, diagnostic fraud indicators based on surveys taken by the claim handlers, anomaly patterns, profile details of individuals, etc. ? of the predictive model The results combined with the result of claimants speech analytics (to detect whether the claimant is lying or not) along with the results of social media analytics (to detect the relationship among the policy holders) can be used to generate a risk score of the claimant. ?score can then be used to This risk detect fraud customers even before issuing the policy. coming up with solutions to integrate Conclusion structured analytics with unstructured analytics. By converging unstructured analyticsThere is an increasing requirement into the structured analytics mix, businesseswithin organizations to inquire and are seeing substantial improvements in the accuracy and relevance of their analyticanalyze across structured and unstructured initiatives. Incorporating analytics blendsdata. Integrating unstructured data with into business processes is a growing trend;structured data is quite a challenge. Despite however, they must be correctly applied to athe challenges, many analytics vendors are specific business scenario, and companies 19
must act on the results appropriately. The convergence of unstructured analytics with structured analytics is no longer an “if”, but rather a “when”.20
Analytics in Financial Services03Fusing Economic Forecasts Dr. Andrew Jennings Chief Research Officer Carolyn Wang Senior Manager, and Head of FICO Labs, Analytics,with Credit Risk Analysis FICO FICOAs many in the financial services industry prepare for measured economic recovery, itscritical to take stock of a key lesson of the financial crisis: that risk, by its nature, is dynamic.Todays economic realities call for a paradigm shift in risk management—one that includesnew analytics that go beyond the traditional assumption that past risk levels are indicative offuture risk. This article discusses new methods for systematically incorporating economicdata into scoring systems, allowing lenders to balance consumer level information withchanging economic trends. It also shares results from lender applications of this methodologyto “get ahead of the curve” by more closely aligning risk strategies with future performance. In 2005 and 2006, for instance, a default rate of Why a New Approach is Needed 2% was associated with a score of approximately 650–660. By 2007, this default rate was associatedTraditional assumptions that recent default rates with a score of about 710—a 50 to 60 point shift.will be representative of future defaults works This risk shift was most likely due to economicreasonably well if the lending environment decline after a period of more lenient lendingremains relatively stable. But in situations where practices, such as adjustable rate mortgagesexternal factors are changing rapidly, it can be and no-documentation (“no-doc”) loans.dangerous to assume the risk levels associated Lenders in 2007 who made decisions assumingwith scores will remain stable over time. that 670 still represented 2% default saw increasesTake the recent credit crisis as a case in point. in their default levels, when the actual defaultFigure 1 (next page) shows the observed default was about 4.5%. ®rate at each FICO Score range for real estate loans. Many lenders do attempt to anticipate changesEach line represents a large random sample of in economic conditions and adjust strategiesexisting accounts evaluated over different time accordingly—for example, tightening originationperiods and the default rates one year later. policies and reducing credit lines in a recession,As the graph shows, the risk levels associated or loosening policies in an upturn. But lenderswith accounts in 2005 (blue curve) versus 2006 often make these changes judgmentally, and as a(red curve) were already diverging. One year later, result, there is a tendency to over-correct and missthere were much greater risk levels associated key revenue opportunities, or under-correct andwith all but the very highest scores (green curve retain more portfolio risk than desired.shifts more dramatically upward).
Risk levels can shift dramatically over time Figure 1 Next Evolution of influence the risk profile of newly booked Predictive Analytics and existing accounts. n Competition. As consumers are To restore profitability in a post-crisis presented with more (or less) attractive economy, lenders need an objective and offers by the competition, attrition will empirical approach to measure changing change the population. conditions and translate these into more effective strategies. The next generation More specifically, lenders need analytics of predictive analytics must go beyond that predict the impact of the above factors the assumption that only past risk levels on future risk levels for each account. In other are representative of future risk. Instead, words, the analytics must consider the risk estimates should also account for the macro-economic view of market conditions impact of: within the micro-analysis of individual consumer risk. The analytics must be n The economy. Consumers ability to flexible enough to take into account what repay changes as the economy shifts. is known—the historical data available Some lower-risk consumers may to derive past patterns and current economic refinance in downturns, leaving behind conditions—as well as what is expected— a portfolio of riskier consumers. forecasted views of the future. Others may reach their breaking points through job loss or increased payment Given the ready availability of economic data, lenders can first take into account the impact requirements. Higher-risk consumers of economic factors on future risk estimates. get stretched further, resulting in more In addition, lenders should begin to track frequent and severe delinquencies and changes in their strategies and competitive defaults. factors, which can be incorporated into n strategies. Changes in lender Lender future risk estimates as the data becomes policies across the account lifecycle can robust enough.22
Analytics Tuned to Future are expected to behave differently under Performance varying economic conditions. This methodology builds upon existingNext-generation analytics can provide risk tools used by lenders, enabling quicklenders with an understanding of how and seamless implementation. It can bethe future risk level associated with scores applied to a variety of scores, such aswill change, based on current and projected origination scores, behavior scores, broad-economic conditions. Based on past based bureau scores like the FICO® Score,dynamics, the analytics derive the and Basel II risk metrics.empirical relationship between the defaultrates observed at different score ranges When applied to these scores, lenders gain(e.g., the risk scores odds-to-score an additional dimension to their riskrelationship) as seen on the lenders predictions so they can better:portfolio, and historical changes in n Limit losses. Lenders would have greatereconomic conditions. Using this derived insight on how to tighten up creditrelationship, lenders can then input current policies sooner and for the rightand anticipated economic conditions to populations during a downturn.project the expected odds-to-score outcomeunder those conditions. They can examine n Grow portfolios competitively. Lenderseconomic indicators such as unemployment could more quickly determine when andrate, interest rate and Gross Domestic how to proactively loosen up creditProduct (GDP). policies as markets recover.With such a relationship, it is possible to n for the future. Lenders could Preparerelate the impact of economic factors on simulate the impact of future macro-odds, default rates and scores. This relationship economic conditions on scores,can be derived at an overall portfolio level or to better adjust longer-term strategiesmore finely for key customer segments that and stress-test portfolios. More accurately predict default rates with analytics Figure 2 tuned to economic impact 23
n Meet regulatory compliance. Lenders Over a three-year time span, predictions ® could better set capital reserves by from the FICO Economic Impact Service 2 creating more accurate, forward-looking, reduced the error rate by 73%, compared to long-run/downturn estimates required the traditional prediction. by Basel II. In practice, economically adjusted Grow Portfolios Responsibly analytics generate a more accurate risk prediction thats better tuned to Within originations, the lender primarily economic conditions. For instance, FICO determines whether or not to accept the loan recently applied this methodology—called as well as the initial loan price/amount. Using ® the FICO Economic Impact Service—to economically adjusted analytics, a lender can a behavior score used by a leading US set a cutoff score based on the anticipated credit card issuer. Figure 2 (previous page) future default rate, as opposed to the historical compares three metrics: default rate. The lender can maintain the desired portfolio risk levels by adjusting cutoff n The actual bad rate observed on the scores as the economy changes. portfolio over time (blue line). Figure 3 shows how a lender can view the n rates predicted by the behavior The bad current default rate by score range (orange score aligned to historical odds line) and predict how the default curve would performance (orange line)—traditional 1 shift under different economic conditions. approach . During a recession, the curve may shift to the n rates predicted by the behavior The bad dark blue line. The lender can update its score aligned to anticipated odds cutoff score from the “Current Cutoff” to the performance (green line)—FICO ® “Cutoff–Recession” based on empirical Economic Impact Service approach. The guidance. This allows the lender to limit economic conditions used were limited losses by proactively tightening credit in to what was known at the time of scoring. anticipation of the downturn. Flexibly adjust score cutoffs under different economic conditions Figure 324
Conversely, during a time of economic traditional behavior score across the fullgrowth, the curve may shift to the light range of account management actions.blue line. The lender can update its cutoff As an example, for a US credit card issuer,score to the “Cutoff–Growth.” This allows FICO retroactively used a FICO® Economicthe lender to loosen credit policies in Impact behavior score in place of theanticipation of an economic upturn, and traditional FICO ® TRIAD ® Customerbring in more profitable customers ahead Manager pooled behavior score for a credit lineof competitors. decrease strategy and for collection actions. TheFICO recently partnered with a European key question was: in April 2008 (a period of ® ®lender to apply the FICO Economic relative economic calm), could FICOImpact Service to its personal loan portfolio. Economic Impact Service help the lenderFaced with sky-rocketing delinquencies anticipate the economic turmoil six monthsthat were 2-3 times historical levels, the later and minimize its financial impact?lender sought to beat the market by FICO analyzed performance in October 2008proactively adjusting origination strategies and compared the different decisions madeahead of its competition. Using the by the two scores. Figure 4 shows the lineresults from the economically adjusted decrease results.analytics, the lender saw opportunity toimprove its profit per applicant by US $11.50. The columns of interest are the “Swap In” and “Swap Out”, since they illustrate whereA similar approach can be taken for initial different decisions would be made. Theloan amount and pricing strategies. second column identifies accounts that would have received decreases using the Improve Account Management Decisions Economic Impact score, but did not receive decreases by the traditional behaviorLenders use behavior scores to help score (the accounts would be “swapped in”manage accounts already on their books if the lender had used the Economicfor credit line management, authorizations, Impact score). The third column identifiesloan re-pricing and cross-sell decisions. accounts that the Economic Impact scoreAn “economically impacted” behavior score would not have decreased and the traditionalcould be used in place of or along with the behavior score did decrease. Economic Impact score better identifies higher-risk Figure 4 accounts for line decreases 25
The highlighted cells show that the behavior also would have not decreased accounts less score for these two populations are almost the sensitive to the downturn, reflected by same (swap-in: 643 vs. swap-out: 646). In other slightly higher scores. words, the behavior score identified both The actual bad rates seen six months later populations as at relatively the same risk level. reinforces that the Economic Impact score ® However, the FICO Economic Impact score identified riskier accounts (swap-in: 10.5% vs. was better able to distinguish risk among swap-out: 7.9%). If the lender had decreased these populations. Using this score, the credit lines on the appropriate accounts, it lender would have decreased more accounts could have realized a yearly loss savings of that would be negatively affected by the roughly US $2.4 million and a net savings of downturn (average score of 625). The lender US $1.7 million, shown in Figures 5–6. ® Yearly loss savings using FICO Economic Impact Service Figure 5 ® Yearly net savings using FICO Economic Impact Service Figure 626
Figure 7 illustrates how the lender could Limit Collection Losses have saved close to US $4 million by taking aggressive action earlier. FICOIn times of economic turmoil, its even more calculated this using the number of actualcritical for lenders to proactively manage bad accounts that would have receivedcollection efforts. Economically adjusted accelerated treatment, average accountscores can be applied on existing behavior or balance and industry roll rates.collection scores to help lenders identifywhich accounts will become riskier and Combining this roughly US $4 million inshould receive increased collection priority. collection savings with the US $1.7 million savings from the credit line decrease strategy,For the same US card issuer, FICO the lender would have saved US $5.6 million.retroactively used an economically impacted This illustrates the aggregate benefits of thebehavior score in place of the traditional service when used across two areas in abehavior score to treat early-stage (cycle 1) customer lifecycle. Clearly, the benefitsdelinquent accounts. Prioritizing accounts by would be scalable for larger portfolios. ®risk, the strategy using the FICO EconomicImpact behavior score would have targeted Set more Accurate Provisions41% of the population for more aggressive and Capital Reservestreatment in April 2008. FICO thenexamined the resulting bad rates six months When setting provisions and capital reserves,later (October 2008), and saw that these it is important to understand the risk in theaccounts resulted in higher default rates. portfolio under stressed economic conditions. Having forward-looking riskIn other words, the Economic Impact score predictions is explicitly mandated by Basel IIbetter identified accounts that should receive regulations, and should be part of anymore aggressive treatment in anticipation of lenders best practice risk management.the downturn six months later. Using thisstrategy, the lender would have been ahead of FICO worked with an Eastern Europeanits competition in collecting on the same lender to apply FICO® Economic Impactlimited dollars. Service to its Basel II Probability of Default ® Yearly loss reduction using FICO Economic Impact Service Figure 7 27
(PD) models. Using the derived odds-to-score aligned to current and future expected relationship between its PD score and economic conditions, lenders can more economic conditions, the lender can quickly adjust to a dynamic market and steer simulate the expected PD at a risk grade their portfolios for the uncertainties ahead. level under various economic scenarios. Thus, the lender can more accurately References calculate forward-looking, long-run PD estimates to better meet regulatory 1. Some scores are periodically “aligned” to requirements and calculate capital reserves. maintain a consistent odds-to-score relationship over time—for example, to Redefine Risk Management ensure a behavior score of 675 equals a Best Practices target odds of 30 to 1. Traditionally, behavior scores have been aligned to the Theres no better time for lenders to re- odds observed in the last six months. evaluate risk management practices in order to better prepare for measured growth or 2. Error rate is defined as the absolute buffer against a lingering recession. Forward- difference between the actual bad rate and looking analytic tools will become the risk predicted bad rate as a percentage of management best practices of tomorrow. actual bad rate. With improved risk predictions better28
Analytics in Financial Services04Unstructured Data Dilip Nair Project Manager, Srinivasan V Ramanujam Engagement Manager, Allen Selvaraj Senior Technical Architect, Banking and Capital Banking and Capital Systems IntegrationAnalytics for Enterprise Markets Practice, Markets Practice, Practice, Infosys Technologies Infosys Technologies Infosys TechnologiesResilience Limited Limited LimitedThe recent financial crisis has shown us how various industries and entities are linked to eachother through a complex web of relationships. This crisis also exposed the ability (or lackthereof) of companies, industries and even countries to plan and respond to the changesaround them. This article talks about how an analytics platform can help organizations tomonitor and manage changes proactively—thereby infusing a dose of resilience against rapid,unexpected changes. Two large financial organizations, Bank-A and its Introduction primary competitor, Bank-B, relied on the same offshore Vendor-X. When Vendor-X had financialEnterprise Resilience is an effort across the troubles, resulting in huge attrition and filing oforganization to anticipate and successfully bankruptcy, these two banks were at risk ofnavigate adversity. Literally, resilience refers to experiencing potential disruption of ongoingthe power or ability to regain the original shape, projects.form or position after being bent, compressed orstretched (subjected to adverse conditions). The Bank-A identified and responded to this incidentterm Enterprise Resilience has been subject to in real-time; they worked with other vendors tovarious interpretations in the industry as it varies ensure that their projects were de-risked beforefrom government agencies, to non-government the actual bankruptcy of Vendor-X occurred.organizations, to financial organizations, to IT Bank-B, on the other hand, was slow to identifyvendors. Enterprise Resilience is not disaster and respond to the incident. Their projectsrecovery or business continuity, but is the new continued to depend on Vendor-X, withDNA for risk management that alters the insufficient back-up, because Bank-A had theorganizations approach from proactive to first movers advantage to scoop up availableadaptive, assuming that the bridge from reactive resources. What followed is predictable. Bank-Asto proactive has been crossed. projects were delivered on time and under budget, and they were able to gain market share,Consider the following hypothetical example. It whereas Bank-B lost market share.is based on a real life scenario involving twoorganizations encountering the same incident, While this is a hypothetical scenario, the realitybut responding very differently. of market share gained and lost defines the new
world of business. In this flat world, the reported their 20 t h billion tweet). definition of risk, dependencies, and Another study puts the value of social risk management have all changed. networking sites beyond the Gross Domestic Traditionally, risk management delivered Product (GDP) of some countries (Facebook one-dimensional solutions—focused on – 11.5 billion, Twitter – 1.4 billion and mitigating risks by addressing vulnerable growing). This valuation is undoubtedly tied areas. This approach has failed to include to the enormous amounts of unstructured and address the various interdependencies data that users create on their profile pages, that characterize the flat world we operate walls or tweets. in today. By understanding the broader The best way to be prepared for any threat risk, and managing risk across the is for an organizations operation to mine extended enterprise, Bank-A demonstrated this huge amount of data and extract greater Enterprise Resilience. meaningful scenarios. In this article, the With globalization, enhanced regulatory focus is on how to mine unstructured scrutiny, a competitive marketplace, data channels with a specific objective in mind—build better resilience for an organizational interdependencies (at a organization. For simplicity, two data level never seen before), and evolving channels will be featured in this article technological challenges emerging, the types (highlighted section in Figure 1). The of risks being faced by the organization concept can be replicated for most other are new, unique and challenging. Being information sources: proactive and adaptive requires that organizations be aware of the ever-changing n (non-formal): With a strong user- · Twitter business and geo-political environment. base, any breaking news is immediately Doing so requires that organizations harness propagated through tweets. (Tweets are every bit of data that is available—federal text-based posts of up to 140 characters and regulatory updates, geo-political news, displayed on the authors profile that are weather updates, emergency service updates, visible either publicly by default or by business and financial updates, and social restricted followers.) networking sites. There are several services nnews websites (formal): Websites— · Media that offer the above in a packaged format. such as NewzCrawler, FeedDemon, However, one of the most undertutilized Google Reader, Omea Reader, Bloglines, data sources is the unstructured data NewsGator and scoopitonline—act as emerging from social networking sites. collectors of breaking news from various According to data scientists, nearly formal news channels and RSS feeds. 80% of todays data is unstructured. New information channels—like the Mining/ Modeling – Identification and internet (Facebook, Twitter), email, instant Classification messaging (chat), text messaging (SMS), and voice-over-IP (phone calls)—are The data channels described in Figure 1 are generating enormous stores of non- unstructured. The next step in the process traditional data at a mind-boggling is to mine/ model this data to derive potential pace. According to recent estimates, Twitter scenarios (highlighted section in Figure 2). users create approximately 7 Terabytes of There are many unstructured data analytics data in a day (on 30th July 2010, Twitter packages available in the market (for example,30
Step 1: Twitter and media websites as data channels Figure 1 Rich Profile Data (Location Attributes and Past Events) Twitter RS S Fe ed Improved s Data Google Mash-ups Analytics Response Landing Text Store for Geo-locational Reports & Zone Analytics Engine Times & Cost (ODS) Tagging Dashboards Savings Identification s eed & Classification SF Media RS WebsitesHIMI from Infosys, AeroText from Rock perform sentiment analyses to enablesoftware, Attensity360 from Attensity, behavioral and predictive capabilities.Lexalytics, Enterprise Miner data-mining n · Categorization of data and classifyingworkbench from SAS). These packages it into appropriate buckets, likeprovide a very good GUI-based approach environmental disasters, man-madefor viewing data to build, test and publish incidents/ threats, stock market changes,models using: geo-political activities like administrationn for words, word patterns or· Searches changes, policy changes, civil unrest, strikes strings to identify emerging events and to and boycotts, and other forms of gain insights into potential threats. The disruption to business. input to this would be a library of words, n fine-tuned by client, industry · Patterns word patterns or strings that are pre- segment and geography. The objective is selected and fed to the tool. to vary these patterns so they can capturen· Distinguish message patterns (tweets) for small, medium or large incidents while emotions and stress. This can be used to avoiding false positives. Step 2: Identification and classification of incoming data Figure 2 Rich Profile Data (Location Attributes and Past Events) Twitter RS S Fe ed Improved s Data Google Mash-ups Response Landing Text Store for Geo-locational Analytics Reports & Zone Analytics Engine Times & Cost (ODS) Tagging Dashboards Savings Identification s d Fee Media & Classification S RS Websites 31