Predictive Modeling. ASHK Seminar. November 21, 2013

Transcription

1 Predictive Modeling ASHK Seminar November 21, 2013

2 Welcome to the ASHK Seminar! Agenda Introduction to Predictive Modeling in Actuarial Science Fundamentals of Cross-Sectional Regression Modeling Multiple Linear Regression Regression with Categorical Dependent Variables Regression with Count Dependent Variables Generalized Linear Models Frequency/Severity Models Extended Cross-Sectional Regression Modeling Mixed Models Generalized Additive Models, including Non-Parametric Regression Fat-Tail Regression Models Spatial Statistics Supervised versus Unsupervised Learning Bootstrapping, including Simulation Bayesian Modeling Introduction to Bayesian Computational Methods Bayesian Regression Models Longitudinal Modeling Time Series, including Lee-Carter forecasting Longitudinal and Panel Data Models Credibility and Regression Modeling Page 2

3 When the winds of change are blowing some people are building shelters, and others are building wind mills Chinese proverb A G E N D A Define predictive modeling Discuss applications Page 3

4 Let s build some wind mills!!!! Page 4

5 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 K [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

6 Just a few definitions of Predictive Modeling Predictive modeling is a strategy that involves the creation or selection of a model in an attempt to project the possible outcomes associated with a given action. (wisegeek.com) Predictive modeling is the process by which a model is created or chosen to try to best predict the probability of an outcome (Wikipedia) Predictive modeling is a statistical technique to predict future behavior a form of data-mining technology that works by analyzing historical and current data and generating a model to help predict future outcomes. Predictive modeling is a collection of mathematical techniques [to find] a mathematical relationship between dependent variable and independent variables to.. insert them into the mathematical relationship to predict future values of the target variable. (Gartner) (SAS) Page 6

7 Really, what is Predictive Modeling? Predictive modeling is a process to create a statistical model of future behavior Society of Actuaries (SOA) Traditional View Predictive Modeling 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 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 Page 7

8 Is there an opportunity for the actuaries? Page 8

9 Why Predictive Modeling is NOW? Industry leaders are aggressively pursuing alternate means to gain a competitive advantage such as Predictive Modeling and advanced analytics Use of Predictive Modeling- U.S Life Insurance Market (2011) Not yet 42% Using PM 12% 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 Modeling Page 9

10 Why Predictive Modeling IS NOT NOW? While GI achieved considerable success in using PM for pricing, the nature of life insurance products (long-term duration, low frequencies) makes Life Insurance a less attractive 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

11 The evolution of Enterprise Intelligence HIGH Optimization What is the best outcome? Enterprise Intelligence Optimization, Decision Analysis Prediction Predictive Modeling, Forecasting What will be happening? Predict results that lie in the future Complexity Monitoring What is happening now? Analysis Statistical Analysis and Data Mining Regression analysis Time series analysis Dashboards, Scorecards, Metrics, KPIs Multidimensional analysis, Visualization Why did it happen? Exploratory data analysis Discover trends and patterns within data Reporting What happened Query, ad-hoc reporting LOW Business Value HIGH Page 11 Presentation title

12 Application #1: Predict Employee Performance Oakland A s 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 Page 12

13 Application #2: Predict Response to Marketing Campaigns Cigna 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 Page 13

14 Application #3: Analyze Agency Performance Westfield Insurance 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 Page 14

15 Application #4: Predict Fraud Infinity is a $1.2B motor insurer with 2,000 employees and 12,500 independent agents Infinity Insurance 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 days down to 1 3 days Achieved 403% return on investment from reduction in claims payments and enhanced subrogation Page 15

16 Application #5: Integrated Solution Nationwide Financial 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 Page 16

17 A word on tools Page 17 Presentation title

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