"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

September 14, 2021

Connected Cars - Telematics Use cases - Research Paper

Paper - How much telematics information do insurers need for claim classification?

Key Notes

  • By comparing a few classification algorithms, we find that logistic regression with lasso penalty is the most suitable for our problem
  • Telematics data become redundant after about 3 months or 4,000 kilometers of observation, at least from a claim classification perspective
  • Telematics data fall under the definition of “personal data”, and must therefore be handled according to the relevant legislation
  • In this analysis, only collision coverage claims were considered, i.e. the target column given as input to the classification models is the indicator of a collision claim, at-fault or not
  • Instead of having the indicator of a claim as the response variable, we would instead have the number of claims, moving us into a counting regression context
  • Low claims / Medium / High claims we could bucket and assign premium accordingly

Classical features selected for the analysis

“usage”, “travel habits” and “driving performance”. Claimants (those who have claimed at least once during their observed year) and the non-claimants (those who have not claimed during their observed year)

Summary of Features

Collected Feature

  • VIN
  • Trip number
  • Departure datetime 
  • Arrival datetime 
  • Distance 
  • Maximum speed

Derived Features

  • annual_distance
  • commute_distance
  • conv_count_3_yrs_minor
  • gender
  • marital_status
  • pmt_plan
  • veh_age
  • veh_use
  • years_claim_free
  • years_licensed

Derived Features by “usage”, “travel habits” and “driving performance”

  • avg_daily_distance
  • avg_daily_nb_trips
  • med_trip_avg_speed
  • med_trip_distance
  • med_trip_max_speed
  • max_trip_max_speed
  • prop_long_trip
  • frac_expo_night
  • frac_expo_noon
  • frac_expo_evening
  • frac_expo_peak_morning
  • frac_expo_peak_evening
  • frac_expo_mon_to_thu
  • frac_expo_fri_sat

More Reads

Keep Thinking!!!

No comments: