Integrating Telematics Into Rating Plans. CAS Ratemaking and Product Management Seminar March 12th, 2013

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1 Integrating Telematics Into Rating Plans CAS Ratemaking and Product Management Seminar March 12th, 2013

2 CAS Antitrust Notice The Casualty Actuarial Society is committed to adhering strictly to the letter and spirit of the antitrust laws. Seminars conducted under the auspices of the CAS are designed solely to provide a forum for the expression of various points of view on topics described in the programs or agendas for such meetings. Under no circumstances shall CAS seminars be used as a means for competing companies or firms to reach any understanding expressed or implied that restricts competition or in any way impairs the ability of members to exercise independent business judgment regarding matters affecting competition. It is the responsibility of all seminar participants to be aware of antitrust regulations, to prevent any written or verbal discussions that appear to violate these laws, and to adhere in every respect to the CAS antitrust compliance policy.

3 Project Greenlight Image Credit: Shutterstock / Oleg Liubimtsev

4 Mission Impossible? Expenses Created by UBI Break Even L/R Improvement on UBI Book = D x ( d + 12 x T x w ) x P D x N x TV x T x P UBI Premium Base Hypothetical Calculation D = # of devices T = useful life (years) N = device rotations / yr. V = vehicles outfitted 5, ,000 P = avg. premium rate d = device cost (% of P) w = monthly service (% of P) $ % 1.0% = N x T x D

5 There s Some There Here Date Time (UTC) Latitude Longitude 2013 /01 /28 07:05: Outside Temp. ( F) Cloud Cover Precipitation Visibility (Miles) Wind Speed (MPH) 31 Partial Drizzle 10 5 Speed (MPH) Driver Side Seatbelt Perform. (RPM) Engine Temp ( F) 44 / 41 Engaged 2250 / Road Type Elevation (> Sea Level) Posted Limit (MPH) Ambient Speed (MPH) # Bars w/in 5 Miles Local * Trip Mileage Trip Time Trip Fuel (Gallons) Accel. Events :32: Event #1 Start Event #1 Duration Event #1 Type Event #1 Max g-force 07:05: :00:00.57 Braking 0.632

6 Drill Down or Aggregate Up

7 Data Considerations Granularity Credibility Homogeneity Salability Analytics Alternatives

8 Image Credit: Shutterstock / Oleg Liubimtsev Integration Options Authenticate Substitute Refine Enhance Accelerate Overhaul

9 Some Overlap Collision Classification Factor Braking Incidents per Hour Driver Age Source: ISO Fleet Data Q/E September 30, 2012

10 Mixed Signals 0.70 Braking Incidents per Hour Collision Model Year Relativity Vehicle Age Source: ISO Fleet Data Q/E September 30, 2012

11 Tempered Effect Braking Incidents per Hour Moderate (>= 0.5 g-force) Harsh (>= 1.0 g-force) Collision Loss Cost of Drive Through Micro-Territory Collision Loss Cost (Reference) Source: ISO Fleet Data Q/E September 30, 2012

12 Exploratory Data Analysis Basic Reasonability Extreme Outliers Gaps Duplication Relational Edits Correlations

13 Correlations Example Correlation Matrix Variable 1 1 Mileage Average Speed Moderate Braking Severe Braking Vehicle Age Source: ISO Fleet Data Q/E September 30, 2012

14 Give Me A Brake Per 100 Miles g-force g-force Local Roads Arterial Roads Major Roads Harsh Braking Incidents Per Hour Driving g-force g-force g-force > 2.00 g-force No Precipitation Light Rain Heavy Rain Sleet / Snow <= 30 MPH MPH > 55 MPH

15 Dimension Reduction Principal Component Eigenvalue Proportion Braking % Braking % Braking % Braking % Variable Braking1 Braking2 Braking3 Braking4 Brake_05h_rLocal Brake_10h_wRain Brake_20h_sHiSpd Brake_08h_sLoSpd Brake_15h_agg Note: Results displayed are hypothetical.

16 Variable Interactions 1.20 Braking Incidents per Hour Ages Ages Source: ISO Fleet Data Q/E September 30, 2012

17 Model the Effects /* hypothetical SAS to implement telematics-sourced rating variables on top of existing rating plan */ Proc GenMod Data=MyLib.UbiBook; class avspd_range; p=1.8; y=_resp_; a=_mean_; variance var=a**p; deviance d= ; /* <- insert messy formulate here */ model pure_prem = braking1 braking2 braking3 /* from PCA */ avspd_range * pct_gtlim left_05h pct_peak pct_early / link=log scale=d offset= ln_presprem type3; output out=mylib.ubibook_scored pred=ubi_prem; Run;

18 Big Data Notwithstanding External Data and/or Model Alternate Dependent Variables Heuristic Approach Reduced Scope / Oleg Liubimtsev Image Credit: Shutterstock

19 Practical Considerations Roll-Out Observation Privacy and Data Handling Intellectual Property Insurance Regulation

20 Survey Says Source: Insurance Services Office, Will Consumers Agree to Install Telematics Devices in Their Cars? (2010)

21 Case in Point Territorial Refinement Discount-Only Non-Contiguous Bands 100% 75% Mileage by Risk Zone Areas At Least as Risky As Garage 90+ Day Observation 50% Filed in 25+ States in % 0% -25% -50% -75% -100% Areas Less Risky Than Garage Source: ISO Fleet Data Q/E September 30, 2012

22 Post-Implementation Metrics Loss Ratio Retention Behavioral Improvement Growth Rate Challenges Lack of Data Long Term Return Period Volatility Hawthorne Effect Image Credit: Shutterstock / Oleg Liubimtsev

Questions and Remarks / Oleg Liubimtsev Image Credit: Shutterstock Contact jweiss@iso.com or 201.469.

com/telematics No part of this presentation may be copied or redistributed without the prior written

23 Questions and Remarks / Oleg Liubimtsev Image Credit: Shutterstock Contact jweiss@iso.com or No part of this presentation may be copied or redistributed without the prior written consent of ISO. This material was used exclusively as an exhibit to an oral presentation. It may not be, nor should it be relied upon as reflecting, a complete record of the discussion.

Using Telematics Data Effectively The Nature Of Commercial Fleets. Roosevelt C. Mosley, FCAS, MAAA, CSPA Chris Carver Yiem Sunbhanich

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