Predictive Analytics and Price Optimization Michael E Angelina

Predictive Analytics and Price Optimization Michael E. Angelina, ACAS, MAAA, CERA Executive Director, Academy of Risk Management & Insurance Erivan K. Haub School of Business Saint Joseph's University

Agenda • Background • Predictive Analytics defined – IBM View, other definitions • Insurance Industry Acceptance and Uses • Demographics • Price Optimization – Issues

Data Analytics - Background • 2003 Yankees versus Red Sox, Game 7 – Pedro has the Yankees on the ropes; – Boston manager, Grady Little decides to stay with his starter in the 8 th inning – Managerial decision based on instinct, Pedro’s reputation, and his season • Season Stats: – – 14 -4 Won – loss record; 2. 22 ERA; . 586 OPS; 29 Games Started; 186 innings; (<7 innings per start) Only pitched into the 8 th inning 5 times all season Typically when he had 5 days of rest • Lets mine the data a little more; – OPS of. 586 for season; in 4 starts against the Yankees OPS was. 718 – OPS is on base plus slugging percentage: Inning 1– 5 6 7 OBP. 267. 295. 364 Slugging. 280. 395. 471 OPS. 534. 691. 835

Data Analytics (IBM view) • IBM survey of 1, 700 CEOs and public sector leaders identified technology change as the most critical external factor impacting organizations. • Three principal types of analytics solutions: – Descriptive –what happened? • provides information on past events (standard reporting, drill down/queries) • Utilizes reports, dashboards, business intelligence – Predictive –what could happen? • provides answers for decisions (anticipate) – Predictive modelling – what will happen next – Forecasting – what if these trends continue – Prescriptive – what should we do? • explores a set of possibilities and suggests actions - optimization • Factors uncertainty and recommends approaches to mitigate risks; • AIG has a Science Officer to lead this global initiative • Ace, Chubb, Travelers, and XL continue to advance analytics.

Predictive Analytics • Not new to the industry – Certain companies were inquisitive • State Farm in the mid-70 s; Progressive yesterday and today; Zenith in WC • Catastrophe modeling in the 90 s • What has changed – Computing power continues to increase exponentially – Availability and accessibility of data (internal, personal, and external) • Widespread acceptance in the business community – Demographic changes; Consumer changes – Innovate or Perish – Case Studies • Insurance Industry Acceptance – – Underwriting for personal lines and small commercial Risk Management (Reinsurers, direct property writers) Claims : personal and commercial lines Distribution – personal lines and small commercial

Case Study - Yellow Pages • In 2006 a one-inch ad in Manhattan, NY, cost $2, 500 • Full-page size ad cost $92, 000 • In 2011 the rough average price of a yearly ad decreased to $17, 000 [1] • According to an MSN study 70% of people do not open the Yellow Pages • Seattle in 2010 allowed its residents to opt-out of receiving the Yellow Pages • 2011 the 9 th U. S. Circuit of Appeals sided with Yellow Pages • By that time 79, 000 Seattle residents had opted-out [2] [2] • Failed to go digital fast enough 6 [1] [2]

Case Study - BLOCKBUSTER • Decade ago ruled the movie rental business • 25, 000 Employees • 8, 000 Stores • 6, 000 Public DVD rental machines [3] [3] • 2005 company was valued at $8 B [3] • Early 2000 s Blockbuster decided not to purchase Netflix • At the time Netflix was valued at $50 M • Current Netflix market cap is $20. 8 B [4] • Did not identify emerging technology • Filed for bankruptcy in 2010 [4] Image Source: 7 [4]

Analytics – Personal Lines • Credit Scoring – controversial but high predictive value • Telematics (Results of Deloitte Study) – – 25% favor; 25% opposed; 50% depends on the amount of the discount Income level not a differentiator Gender is not a significant differentiator Age is a significant variable • Younger drivers do not expect a large discount • Two-thirds of 21 -19 year olds are willing to try telematics versus 44% of over 60 year olds • 35% yes (21 -29) versus 15% yes (over 60) • Genie is out of the bottle – Personal lines – vehicle monitoring (bifurcated market: users and non-users) – Commercial lines – commercial auto: taxi devices – Behavioral shift – heightened loss control due to monitoring

Pause for a moment and reflect Visualizing the Generations Baby Boomers Generation X 9 Generation Y

Purchasing Influences [9] 10

Understanding Generation X • Grew up in a time of technological advancement – – – Likely to research and purchase online Values honesty and transparency Desires fast turnarounds Seeks tailored products and experience In 2013 75% of Generation X banked digitally [18] Graph Source: [18] 11 [17] Increased use of digital banking is transitioning to insurance purchasing habits

Smart Mobile Devices in Insurance [9] 12

Deloitte Study on small business owners Deloitte Small Business Study • Surveyed 750 small business insurance buyers with <25 employees if they would buy directly from insurers: [23] 13

Deloitte Cont. [23] 14

Price Optimization • Systematic and statistical method to help an insurer estimate a rating plan factoring in a competitive environment • Informs an insurer’s judgment when setting rates by producing suggested competitive adjustments to the actuarial indicated loss costs • Utilizes a variety of applied mathematical techniques (linear, nonlinear, integer programming) to analyze insurer’s data and other considerations • Enables exhaustive search across thousands of pricing alternatives in multiple scenarios to assist insurers in comparative rate analysis – Improves efficiency of rate setting process; – Enables companies to more accurately predict the outcome of their rate decisions

Ratemaking Process – Step Back • Regulatory Requirement – rates must be adequate, not excessive, or unfairly discriminatory • Process (per EPIC Consulting) – Actuaries determine expected losses, expenses, and profit loading – Management makes adjustments to reflect business considerations, marketing, underwriting, and competitive conditions – Regulators permit insurers to reflect judgment and competitive environment in rates – Rate Filer (Insurer) must ensure that filed rates are adequate, not excessive, or unfairly discriminatory – Actuaries can opine that the filed rates meet statutory standard if reasonably close to actuarial estimate (eg reserving)

Price Optimization - Proponents • Compare price optimization to traditional rating approach – Traditional approach: Base rate (loss cost) x adjustment factors • Adjustment factors based on age, gender, territory, make and model year – Price Optimization: Base rate 9 loss cost) x adjustments • Adjustments based on price optimization methodology • All companies consider customer response in pricing either underwriting criteria or marketing considerations – Price optimization is just more scientific (statistics versus judgment/market) • Loss Costs remain the foundation of the rate setting process – Price optimization factors typically are designed to stay within constraints imposed by confidence interval of cost estimates • Personal lines is a very competitive market as evidence by advertising spend by large insurers – Competition has decreased the size of the assigned risk markets

Price Optimization - Issues • Price Optimization has generated much controversy from Consumer Federation of America and some regulators • Relies on an analysis of the elasticity of demand of customers to raise prices above the cost-based estimate on some segments of the policyholders who are known to be less likely to change insurers when price increases are below a certain threshold – Great inertia in the personal lines market (people tend not to shop much), as evidenced by recent survey • 24% have never shopped for auto insurance (27% HO) • 34% rarely shop for auto insurance (33% HO) • 27% shopped within every other year for auto insurance (20% HO) – Price Optimization tries to find these policyholders!

Price Optimization - Questions • How does price optimization fit within the actuarial profession – Cost-based resides with actuaries; – Where does the demand competitive analysis reside? – Should actuaries be involved in price optimization at all ? • Is price optimization ratemaking or NOT ratemaking? – Actuarial code of conduct (precept 1? ) • Is price optimization in compliance with: – Statement of principles on ratemaking – Actuarial standards of practice – Actuarial practice note (ratemaking practice note does not exist!) • Should the actuary consider outcomes other than cost when making rates?