Disruptive technology for auto insurance entrepreneurs: New paradigm using telematics and machine learning

Currently, Motor insurers are playing a passive role in terms of identification of risk incidents for the policy holders. Traditional insurance does not differentiate safe drivers and unsafe drivers. Since they do not have the vehicle telematics data of the policy holders. Many insurance corporations are planning to utilize telematics data to build a model of predictive risk for policy holder and claim possibility. They can reward safe drivers by low premiums and/or no-claim bonus. Likewise, unsafe drivers need to pay extra risk premium. This means drivers have a stronger incentive to adopt safer practices. This paper describe black-box auto insurance predictive model utilizing basic telemetry like GPS sensor data for usage based insurance. Predictive model is developed using binary logistic regression machine learning technique. It is an informative paper for entrepreneurs since it highlights the business proposition from an insurer perspective to gain competitiveness in highly commoditized insurance market.