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Predictive Analytics and Price Optimization Michael E. Angelina, ACAS, MAAA, CERA Executive Director, Academy of Risk Management & Insurance Erivan K.

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Presentation on theme: "Predictive Analytics and Price Optimization Michael E. Angelina, ACAS, MAAA, CERA Executive Director, Academy of Risk Management & Insurance Erivan K."— Presentation transcript:

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 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: InningOBPSluggingOPS 1 – 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 9 th 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 6

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] 7 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 reflect Visualizing the Generations 9 Baby BoomersGeneration XGeneration Y

10 Purchasing Influences 10 [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] 11 Increased use of digital banking is transitioning to insurance purchasing habits Graph Source: [18]

12 Smart Mobile Devices in Insurance 12 [9]

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

14 Deloitte Cont. 14 [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?

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