FICO Predictive Analytics provides businesses across a variety of industries with:

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1 FICO Predictive Analytics provides businesses across a variety of industries with: Improved profitability from more targeted decisions across the lifecycle. Stronger analytic expertise and deeper views into consumers. Faster model implementation for accelerating time-to-value. Streamlined regulatory compliance with FICO s expertise in legal restrictions on data use and sound risk management practices. help you attain the best predictive models for your business needs whether you are new to scoring or have an experienced in-house analytic team. Our predictive models provide significant benefits used on their own or as an integral part of FICO s decision management solutions. We offer empirically derived custom models tailored specifically to proprietary product portfolios and customer bases, as well as pooled-data and expert (knowledge-based) models to reduce the time, expense and data demands of a custom development. What Is Predictive Analytics? Predictive analytics is an area of statistical analysis that deals with extracting information from current and historical data, and using it to make predictions about future, or otherwise unknown, events. In business, predictive analytic models exploit patterns found in historical, transactional, structured and unstructured data to better identify risks and opportunities. capture relationships across many factors to assess key business measures and drive better decisions. One of the most well-known applications of predictive analytics is credit scoring, which is used throughout financial services. Credit scoring models process a customer s credit history, loan application, customer data and other information in order to rank-order individuals by their likelihood of making future credit payments on time. The FICO Score, the world s number one credit bureau score, is a prime example of a credit score. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis, as well as the quality of the model design and assumptions made during development. That is where FICO s experience and innovative techniques can make a critical difference, particularly in the new world of Big Data Fair Isaac Corporation. All rights reserved. 1

2 Typical Predictive Analytics Adoption Roadmap Descriptive Predictive Decision Stochastic Level of Sophistication Collaborative Filtering Infoglide Link Analysis Segmented Ensemble Clusterbots Clustering FICO Scorecard Technology Logistic/Linear Regression Principal Component Analysis Time-to- Event Action Effect/ Uplift Neural Networks Economic Impact Large Scale FICO Decision Modeling and Cut-Off/Marketing NPV/Profit Data-Driven Strategy Development Expert Strategy Development Expert Segments Period of Adoption This graphic shows the typical modelling adoption roadmap that businesses follow, illustrating some of the different techniques most often used. Those in bold text indicate FICO solutions. Why Use Predictive Analytics? The primary purpose of using predictive analytics is to measure or rank order either a future probability or value. Being able to measure probability or value reduces the uncertainty that clouds future events, thus allowing better decisions to be made. Additionally, the use of predictive analytics often provides improved: Precision supporting more profitable and targeted decisions. Consistency across all channels, business units and geographies. Agility providing greater ability to adapt decisions on-the-fly. Speed executing decisions and processes faster. Cost expediting processes and cutting expenses needed to make decisions. Predictive analytics differs from data mining in that it is an analyst-guided (not automated) discipline. If data mining searches for clues, predictive analytics delivers answers that guide you to the next action. Similarly, business intelligence (BI) tends to focus on past performance. If BI tells you what happened in the past, predictive analytics helps you make better decisions going forward. Predictive analytics is often used as a general term to describe many different analytic techniques and approaches. At FICO, we focus on three related analytic disciplines, with a main focus on predictive models Fair Isaac Corporation. All rights reserved. 2

3 Predictive models analyse past performance to assess how likely a customer is to exhibit a specific future behaviour, often rankordering customers by their likelihood or probability of taking a particular action. Some of the predictive modelling techniques and approaches used by FICO include: FICO Scorecard Technology: These are FICO s proprietary predictive modelling techniques. They are unique in their ability to account for business rules, legal and operational constraints, and biased or missing data. As a result, FICO can generate scores that are not only highly predictive, but are also very interpretable and palatable to the business user. The patented algorithms use mathematical solver technology to optimise score weights for binary, multiple goal and continuous outcomes. Neural Networks: This is an approach that transforms a set of inputs, via linked, directed and weighted interconnections, into a set of predictive model outputs. FICO pioneered the use of neural networks, alongside patented customer profiling techniques, to create powerful predictions in areas such as fraud detection. Time-to-Event : This is a type of discrete time hazard model developed by FICO which looks to identify the probability of an event occurring over different time periods, in effect predicting when events are most likely to occur. Action Effect/Uplift : This is a type of causal model that infers from available data the effects of different potential actions on outcomes to help identify which would likely provide the best outcome. FICO has developed a range of techniques mainly focused on marketing and optimization projects. Economic Impact : These models provide predictions that are adjusted by changes in macroeconomic and market conditions, and thus help better understand both current and anticipated future conditions. Descriptive : These models quantify relationships in data in a way that is often used to classify customers or entities into groups based on their similarities, while maximising segment differences. They often identify many different relationships between customers or products, but do not usually rank-order customers by their likelihood or probability of taking a particular action. Decision (or Prescriptive) : These describe the relationship between all the elements of a decision the known data, predictions, the decision and the forecast results of the decision. They are increasingly used with optimization solvers to run different scenarios, which maximise certain outcomes while minimising or constraining others. Their main purpose and application is to identify improvements in decision strategies, but they re also used for scenario analysis and stress testing. FICO is a pioneer in this area, which is discussed in more detail in other FICO literature. Tracking Big Data FICO has developed a range of analytic tools to help take advantage of the increasing volume, velocity and variety of data available today. We can take structured and unstructured data, such as free form text, and help quickly identify what is of true value in the data. All the model types described previously can take advantage of FICO s approach and application of Big Data. FICO Predictive Analytic models get the most out of your data through innovative techniques to continuously drive more profitable decisions. FICO s innovation in predictive analytics include: Industry s first credit risk score for originations. First standardised credit bureau risk score. First behaviour credit risk score. First empirical approach to optimise decisions. First neural network-based fraud solutions. First customer level behavioural scorecard. First predictive systems for insurance fraud. First analytic systems for retailers to optimise offers. First adaptive analytics for fraud. First credit capacity scores. We continually pioneer the development of new predictive tools and techniques to meet today s business needs Fair Isaac Corporation. All rights reserved. 3

4 What FICO Does Best: Advanced Predictive Analytic Methodologies Each step in a custom predictive model development project has a direct impact on performance and your bottom line. When it comes to analytic techniques, FICO s decades of experience, proven methodology and partnership approach translate into greater lift from your models. Project Design Quality Data Drives Results. Data quality is essential to good predictive model performance. FICO s proven data audit and analysis techniques minimise noise in your data to help us build better models. Big Data Assessments. With the increased volume, velocity and variety of data available today, FICO can help you identify which structured and unstructured data sources can add incremental value to your predictive model build. Precise Performance Definition. FICO helps you achieve a more precise performance definition, the model s underlying logic for classifying accounts into differing categories for predicting values. We carefully filter your data to exclude elements that skew results and reduce model precision, ensuring that your data capture is accurate and complete. Superior Sample Selection. To build an effective predictive model, sample population selection is critical. We help you optimise sample sizing and time periods to best represent real-world behaviour. This ensures accurate results and eliminates skewing of data. We also partner with your business experts to integrate your unique considerations. Model Development Predictive Characteristics. FICO generates an extensive list of predictive characteristics for inclusion in the models, often using libraries FICO has generated over time from our broad experiences in model building based on standard and non-standard data sources. Reject and Performance Inference. FICO models assess the risk of all your throughthe- door applicants equally not just the customers that you booked. We statistically infer how rejected and un-cashed applicants would have performed as customers. This is critical to the success of your model, but it s one of many things our competitors don t do as thoroughly, or do at all, resulting in the danger of inaccurate performance assignment. Segmentation Analysis. The more closely model segmentation is tuned to the key groups in your portfolio, the stronger the results. FICO leverages sophisticated tools and domain experience to identify and compare segmentation splits. This allows us to assess the cost/benefit of building and maintaining additional models. Advanced Development Tools. Along with industry-leading methodology, FICO uses its advanced analytic tools, such as FICO Model Builder, to optimise model development. For example, our scorecard engineering functionality ensures that models are not over-fitted to a particular development sample. Through the use of constrained weight optimization and other advanced techniques, we ensure our models capture a characteristic s underlying predictive power, while filtering out noise in the development sample. In addition, we align scores to be consistent across multiple models, which simplifies training and usage. Weights Engineering Meetings. As a standard practice, we conduct handson weights engineering meetings where FICO Analysts work with you to finalise model weights using FICO Model Builder to interactively review the model, interrogate the data and analyse candidate characteristics. This meeting is used to finalise model development decisions, ensure that operational, palatability and deployment requirements are met, and facilitate handoff of the final model to you. can be delivered in a range of formats, including industry standard PMML. Model Deployment and Management Credit-Bureau Agnostic. FICO predictive models include bureau neutral characteristics that allow you the freedom of bureau choice. We select characteristics to ensure that they perform well regardless of the bureau source, format and version, thus easing implementation and use. Tracking and Reporting. As a standard practice, FICO will help you generate and interpret tracking reports, so you can take necessary action to avoid potential problems. These reports identify when a model has ceased to perform effectively, or when key changes occur in your populations, marketing and operations. FICO can also provide a model monitoring and validation service as part of its standard delivery project. Built-In Quality Control. Unlike many competitors, quality control is built into our standard methodology throughout the project from data audits through to final project manager reviews. This ensures that we deliver a quality product to our clients every time Fair Isaac Corporation. All rights reserved. 4

5 FICO Analytic Consulting In addition to developing predictive models, FICO also uses its extensive experience across many markets and products to provide best-practice consulting services. Typically, this consultancy consists of an objective assessment of the client s current state, leading to the development of a prioritised roadmap of opportunities and recommended actions. Areas covered include: Data Reviews and Audits Model Development Process Review Scores and Usage Review Credit Policy Review FICO Global Business Consulting One of FICO s core strengths is our ability to combine the expertise of our operational Business Consultants with that of our Analytic Consultants to ensure a more robust and businessfocused solution. Across the customer lifecycle, we can quickly help you identify areas where you can achieve rapid return-on-investment and move towards bestin-class decision making. FICO s Wide Range of Predictive Analytic Services and Areas of Expertise for scorecard and model development fit a wide range of specific business needs. In addition to the commonly used models listed on the following page, our one-of-a-kind custom modelling projects can help companies predict customer behaviour in almost any area of decision management. Scorecard Cut-off Strategy Review Development Methodology Training Performance Tracking and Monitoring Independent Model Validations Model Re-weight/Refresh Descriptive Predictive Decision Project Design Model Development Model Deployment and Management Analytic Consulting Current State Assessments 2017 Fair Isaac Corporation. All rights reserved. 5

6 FICO Case Study Nordea Bank is the largest financial services group in Northern Europe with approximately 11 million customers, 1,000 branches and call centres across all Nordic countries, and a highly competitive e-bank. Nordea has worked closely with FICO for over 10 years on a range of Predictive Analytic projects such as: Application scorecard developments for specific lending products. Customer-level behavioural scorecard developments. A range of Basel II models and services. Additionally, FICO provides Nordea with ongoing tracking and monitoring support to ensure that all their models are performing to expectations. In order to enhance and fine-tune Nordea s tracking of scorecard performance, a monthly scorecard monitoring set-up has been established in close cooperation with FICO. Based on the monthly monitoring reports from FICO and a tailored trigger point concept developed by Nordea, Nordea is able to perform a close and targeted followup and analysis of shifts in scorecard performance. This enables Nordea to analyse trends and significant shifts on a regular basis which constitute an early warning system for scorecard performance, enabling Nordea to take timely action, and also form the backbone of the yearly scoring validation exercise. Martin Rener Kristensen, Head of Scoring, Group Capital and Risk Modelling, Nordea Collections and Recoveries Collections: Likelihood of collecting on an account already seriously delinquent. Risk: Risk across a customer s total product offering. Expected Collection Amount: Calculates predicted recovery rate per account for treatment purposes (e.g., debt purchase). Revenue: Likelihood that a predefined level of income will be generated in the future. Profit: Likelihood that the customer, overall, will be profitable in the future. Time until charge-off: Estimation of time before a customer stops making payments and a lender must classify as a charge-off. Recoveries: Debt Placement and Loan Modification. Marketing Segmentation/Cluster Analysis: Using FICO s unique multi-dimensional supervised approach. Propensity to Respond or Take Up: Estimation for acquisitions, utilisation, cross-sell and up-sell. Time to Event: Estimation of when an event may happen. Uplift/Action Effect: Estimation of how much a particular action may change a forecasted outcome. Fraud Application Fraud: Likelihood those applying for accounts are doing so for fraudulent purposes. Transaction Fraud: Likelihood of various types of fraud based on transaction data and cardholder profiles. 1st Party Fraud: Identification of fraud undertaken in customer s own name, with no intention of repayment. 3rd Party Fraud: Identification of fraud undertaken by someone other than account holder. Link Analysis: Identifying connections between fraudulent entities/activities. Other: Insurance claims, Medical claims Fair Isaac Corporation. All rights reserved. 6

7 New Account Origination Risk: Likelihood of an account (new or existing) becoming seriously delinquent or written-off in the foreseeable future. Underwriting/Loss Ratio: Estimation of an insurance applicant s likely loss ratio. Prepayment: Estimation of time before a customer pays off borrowing. Time-to-Default/Closure: Estimation of time before a significant event occurs. Account and Customer Management Risk: Regular reassessment of an account s ongoing risk. Revenue: Estimation of the magnitude of revenue likely to be generated. Authorisations: Assessment of whether an over-limit transaction should be authorised. Revolve/Non-Revolve: Likelihood that a customer will revolve borrowing rather than pay in full each month. Attrition: Likelihood of an account closing, being dormant, churning or having reduced activity or prepaying. Insurance/loss ratio: Estimation of an insurance customer s likely loss ratio for renewals. Advanced Economic Impact Services. Advanced Scorecard Segmentation. Multiple Goal Scorecards. Continuous Outcome Scorecards. Text and Unstructured Data Scorecards. Customer Responsibility models. Causal models. One of a Kind models. built to meet your unique needs. Custom Credit Bureau Custom Credit Bureau Scores. Multi-Bureau Scores. Credit Capacity Indices. Economic Impact Indices. Regulatory/Capital Management Basel II/III Support. Discovery. PD, LGD and EAD model development. Independent Validations. Stress Testing. Use Test. Economic Impact models. Grade Migration models. IFRS9/Provisioning models. Solvency II models. NPV / RAROC type models. FOR MORE INFORMATION NORTH AMERICA info@fico.com LATIN AMERICA & CARIBBEAN LAC_info@fico.com EUROPE, MIDDLE EAST & AFRICA +44 (0) emeainfo@fico.com ASIA PACIFIC infoasia@fico.com FICO is a registered trademark 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. 3012PS_QE 09/17 PDF