Smart Auto Insurance: High Resolution, Dynamic, Privacy-Driven, Telematic Insurance

TL;DR

This paper introduces a privacy-focused, blockchain-based telematic auto insurance system that reduces data sharing and incentivizes honest behavior through game theory, enhancing trust and security.

Contribution

It presents a novel blockchain-enabled telematic insurance framework that minimizes data sharing and promotes honest client behavior using game theory.

Findings

Reduces data sharing between insurer and insured

Incentivizes honest behavior through game theoretical analysis

Enhances privacy and security in telematic insurance systems

Abstract

Data driven approaches to problem solving are, in many regards, the holy grail of evidence backed decision making. Using first-party empirical data to analyze behavior and establish predictions yields us the ability to base in-depth analyses on particular individuals and reduce our dependence on generalizations. Modern mobile and embedded devices provide a wealth of sensors and means for collecting and tracking individualized data. Applying these assets to the realm of insurance (which is a statistically backed endeavor at heart) is certainly nothing new; yet doing so in a way that is privacy-driven and secure has not been a central focus of implementers. Existing data-driven insurance technologies require a certain level of trust in the data tracking agency (i.e. insurer) to not misuse, mishandle, or over-collect user data. Smart contracts and blockchain technology provide us an opportunity to re-balance these systems such that the blockchain itself is a trusted agent which both insurers and the insured can confide in. We propose a "Smart Auto Insurance" system that minimizes data sharing while simultaneously providing quality-of-life improvements to both sides. Furthermore, we use a simple game theoretical argument to show that the clients using such a system are disincentivized from behaving adversarially.