Predictive Analytics and Price Optimization

1. 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

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

3. 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 8th 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 8th 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 OBP Slugging OPS 1 – 5 .267 .280 .534 6 .295 .395 .691 7 .364 .471 .835

4. 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.

5. Predictive Analytics • Not new to the industry • Certain companies were inquisitive • State Farm in the mid-70s; Progressive yesterday and today; Zenith in WC • Catastrophe modeling in the 90s • 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

6. Case Study - Yellow Pages • In 2006 a one-inch ad in Manhattan, NY, cost $2,500 [1] • Full-page size ad cost $92,000 [1] • 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 [2] • Seattle in 2010 allowed its residents to opt-out of receiving the Yellow Pages [2] • 2011 the 9th U.S. Circuit of Appeals sided with Yellow Pages [2] • By that time 79,000 Seattle residents had opted-out[2] • Failed to go digital fast enough

7. Case Study - BLOCKBUSTER • Decade ago ruled the movie rental business [3] • 25,000 Employees [3] • 8,000 Stores [3] • 6,000 Public DVD rental machines [3] • 2005 company was valued at $8B [3] • Early 2000s Blockbuster decided not to purchase Netflix [4] • At the time Netflix was valued at $50M [4] • Current Netflix market cap is $20.8B [4] • Did not identify emerging technology • Filed for bankruptcy in 2010 [4] Image Source:

8. 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

9. Pause for a moment and reflectVisualizing the Generations Baby Boomers Generation X Generation Y

10. Purchasing Influences [9]

11. Understanding Generation X • Grew up in a time of technological advancement [17] • 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] Increased use of digital banking is transitioning to insurance purchasing habits Graph Source: [18]

12. Smart Mobile Devices in Insurance [9]

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

14. Deloitte Cont. [23]

15. 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, non-linear, 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

16. 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)

17. 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 9loss 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

18. 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!

19. Price Optimization - Questions • How does price optimization fit within the actuarial profession • Cost-based resides with actuaries; • Where does the demand and 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?