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Predictive Modeling ASHK Seminar November 21, 2013 Welcome to the ASHK Seminar! ► Introduction to Predictive Modeling in Actuarial Science ► Fundamentals of Cross-Sectional Regression Modeling ► ► ► ► ► ► Extended Cross-Sectional Regression Modeling ► Agenda ► ► ► ► ► ► ► Introduction to Bayesian Computational Methods Bayesian Regression Models Longitudinal Modeling ► ► ► Page 2 Mixed Models Generalized Additive Models, including Non-Parametric Regression Fat-Tail Regression Models Spatial Statistics Supervised versus Unsupervised Learning Bootstrapping, including Simulation Bayesian Modeling ► ► Multiple Linear Regression Regression with Categorical Dependent Variables Regression with Count Dependent Variables Generalized Linear Models Frequency/Severity Models Time Series, including Lee-Carter forecasting Longitudinal and Panel Data Models Credibility and Regression Modeling “When the winds of change are blowing some people are building shelters, and others are building wind mills” Chinese proverb AGENDA Page 3 ► Define predictive modeling ► Discuss applications Let’s build some wind mills!!!! Page 4 “Change is changing” (Peter R. Porrino, EVP and CFO, XL Group) ”I think there is a world market for about five computers” Tom Watson, Chairman of the Board, IBM 1943 "Computers in the future may weigh no more than 1.5 tons" Popular Mechanics, forecasting the relentless march of science, 1949 “There is no reason for any individual to have a computer in his home” President and Founder, Digital Equipment Corp., 1977 “What do 13 guys in Seattle know that we don’t?” Ross Perrot (EDS) at the time when he was offered to buy Microsoft in 1980 “640K [of computer memory] ought to be enough for anybody” Bill Gates, Founder, Microsoft, 1981 “The concept is interesting and well-formed, but in order to earn better than a ‘C’, the idea must be feasible” Yale University Professor in Economics on Fred Smith’s (founder of FEDEX) term paper on next-day parcel delivery Page 5 Just a few definitions of Predictive Modeling… deling is a Predictive mo volves the strategy that in lection of a creation or se tempt to model in an at ible ss po project the ated with a ci so outcomes as given action. (Wikipedia) ) (wisegeek.com deling is a … Predictive mo t nique to predic statistical tech of rm fo …a future behavior nology that ch te g in data-min yzing historical works by anal ta and and current da odel to help generating a m outcomes. predict future (Gartner) Page 6 deling is the Predictive mo ch a model is process by whi en to try to created or chos best predict of an the probability outcome deling is a … Predictive mo athematical collection of m find] a techniques [to relationship mathematical ependent” between … “d dependent” variable and “in to insert them in variables to .. p to ical relationshi the mathemat values of the predict future target variable. (SAS) Really, what is Predictive Modeling? “Predictive “Predictive modeling modeling is is aa process process to to create create aa statistical statistical model model of of future future behavior” behavior” Society Society of of Actuaries Actuaries (SOA) (SOA) Predictive Modeling View Traditional View ► Set of tools, processes and applications that gather, integrate, query, analyze and report on information ► Focuses on known data that is well understood ► Utilizes traditional business measurements / metrics for risk assessment ► Page 7 Does not include emerging technologies/capabilities for advanced risk assessment ► Utilization of advanced data mining and advanced statistical techniques ► Focuses on mathematically measuring unknown relationships between data sets/ elements that cannot be easily managed ► Provides ability to visualize and explain results, and integrates them into the business decisionmaking and operational workflow Is there an opportunity for the actuaries? Page 8 Why Predictive Modeling is NOW? Industry Industry leaders leaders are are aggressively aggressively pursuing pursuing alternate alternate means means to to gain gain aa competitive competitive advantage advantage such such as as Predictive Predictive Modeling Modeling and and advanced advanced analytics analytics Use of Predictive Modeling- U.S Life Insurance Market (2011) Using PM 12% Not yet 42% Considering 46% Key Drivers ► Earnings demands ► Management emphasis on profitable growth ► Customer relationship focus ► Pricing pressure ► Technology innovations ► Proven approach Source: Market research (Gen Re 2011) Predictive Predictive Modeling Modeling Page 9 Why Predictive Modeling IS NOT NOW? While While GI GI achieved achieved considerable considerable success success in in using using PM PM for for pricing, pricing, the the nature nature of of life life insurance insurance products products (long-term (long-term duration, duration, low low frequencies) frequencies) makes makes Life Life Insurance Insurance aa less less attractive attractive target target Key Inhibitors ► Lack of management familiarity ► Size of initial investment ► Limited business understanding ► Enormous data preparation effort ► Lack of skills ► Modeling capability and capacity ► Proof of accuracy ► Implementation challenges Hesitacy of implementation- U.S Life Insurance Market (2011) Other 25% Size of initial investment 20% Lack of management familiarity 55% Source: Market research (Gen Re 2011) Page 10 The evolution of Enterprise Intelligence HIGH Optimization Enterprise Intelligence What is the best outcome? Prediction ► Optimization, Decision Analysis ► Predictive Modeling, Forecasting Complexity What will be happening? What is happening now? Analysis Reporting ► Statistical Analysis and Data Mining Regression analysis ► Time series analysis ► Dashboards, Scorecards, Metrics, KPIs ► Multidimensional analysis, Visualization ► What happened Page 11 Predict results that lie in the future Monitoring Why did it happen? LOW ► Business Value Presentation title ► Exploratory data analysis ► Discover trends and patterns within data Query, ad-hoc reporting HIGH Application #1: Predict Employee Performance Oakland A’s Page 12 ► In 1999 Oakland’s A baseball team was last in standing, one of the last in player salary, which lead to the departure of three stars they could not afford to keep ► Team’s GM - Billy Beane, assisted by a recent Harvard graduate Paul DePodesta, developed models to predict baseball player performance that contradicted scouts, who formed an opinion based on instincts and superiority of their experience ► Statistical analysis had demonstrated that on-base percentage is a better indicator of offensive success vs. speed, and is cheaper to obtain ► In 2002 and 2003 A’s were in the play-offs with $41M salary (vs. $125M NY Yankees) and remained a top-5 team thereafter, while being last in salary for another several years Application #2: Predict Response to Marketing Campaigns Cigna Page 13 ► Cigna is a $29B global health services firm with $53B in assets. Distributes directly and though affinity partners and brokers. Employs 35,800 and is a #103 on the Fortune 500 ► Customer Value Management group (CVM) analyzes “big data” from affinity partners to select the right telemarketing targets and identify the right marketing strategy ► Analytics improves response rates, determines the precise profile based on personal data, and suggests a contact strategy with tailor-made offers to optimize results ► Provides insights that telemarketers rely on to extend the right offers to the right customers at the right time Application #3: Analyze Agency Performance Westfield Insurance Page 14 ► Westfield - $1.4B in GWP, 2,200 people, 1,200 agents ► Senior leaders needed to gain a better understanding of the agents performance ► Transformed their analytics capability with Analytics Resource Center (ARC) and extended self-service analytics capabilities to everyone in the organization reaching ~40% of the staff use advanced analytics tools ► Established capability to evaluate and manage performance across its agency network ► Made data much more easily accessible, encouraging users to base their decisions on hard evidence rather than intuition Application #4: Predict Fraud Infinity Insurance Page 15 ► Infinity is a $1.2B motor insurer with 2,000 employees and 12,500 independent agents ► Was interested in 1) speeding the settlement of claims that did not contain elements of fraud which required ability to identify fraudulent claims; 2) making better use of its investigative staff; and 3) reducing its high monthly costs for outsourced subrogation ► Infinity implemented predictive analytics solutions, immediately reducing claims payments and improving customer service ► The accuracy of identifying fraudulent claims has doubled, ► Reduced SIU referral time from 45–60 days down to 1–3 days ► Achieved 403% return on investment from reduction in claims payments and enhanced subrogation Application #5: Integrated Solution Nationwide Financial Page 16 ► Nationwide is a $23B insurance and financial services company with $160B in assets. 36,000 employees and 3,500 agents. A+ by A.M. Best #108 on the Fortune 500 ► Focused on growth, they invested in building a DW with 100Tb of user data (10x printed U.S. Library of Congress ► Customer Knowledge Store integrates customer, product and externally acquired data for a single view of the customer ► Financial Performance Management provides a single, integrated data management and reporting environment ► Goal State Rate Management allows product, pricing and underwriting functions to access and analyze the same data to make informed decisions. ► Revenue Connection delivers easy-to-read dashboards and on-demand reports to Nationwide agents and field management A word on tools Page 17 Presentation title EY | Assurance | Tax | Transactions | Advisory About EY EY is a global leader in assurance, tax, transaction and advisory services. The insights and quality services we deliver help build trust and confidence in the capital markets and in economies the world over. We develop outstanding leaders who team to deliver on our promises to all of our stakeholders. In so doing, we play a critical role in building a better working world for our people, for our clients and for our communities. EY refers to the global organization and/or one or more of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. For more information about our organization, please visit ey.com. © 2013 Ernst & Young All Rights Reserved. This publication contains information in summary form and is therefore intended for general guidance only. It is not intended to be a substitute for detailed research or the exercise of professional judgment. Neither the Ernst & Young China practice nor any other member of the global Ernst & Young organization can accept any responsibility for loss occasioned to any person acting or refraining from action as a result of any material in this publication. On any specific matter, reference should be made to the appropriate advisor. www.ey.com