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 –

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 What has changed
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 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
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 % 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 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]

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?