Smart Auto Insurance: High Resolution, Dynamic, Privacy-Driven, Telematic Insurance
This paper addresses the problem of privacy and trust in data-driven auto insurance for both insurers and the insured, offering an incremental solution by integrating existing technologies.
This paper proposes a "Smart Auto Insurance" system that leverages mobile and embedded device data for personalized insurance, while prioritizing user privacy and security through blockchain and smart contracts. The system aims to minimize data sharing and includes a game-theoretical argument suggesting clients are disincentivized from adversarial behavior.
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.