With the rise of Big Data in the past few years allowing new opportunities for granular data aggregation, there is growing cause for concern that the insurance industry is lacking the infrastructure to support this new global trend. With IoT sensors now able to determine the state of people and things in real-time, new opportunities and risks have emerged in trying to dictate and standardize the classification and utility of the resultant large batches of data they collect.
The current state of the insurance industry sees a host of mismanagement in long and numerous insurance claim paper trails, resulting in sizeable amounts of errors and fraudulent activity. With an estimated 5-10 percent of P&C claims being fraudulent, bad actors have often been able to successfully manipulate at least one part of the insurance claim process- whether it be in misrepresenting their personal identity or in the details of their insurance policy. Other concerns lie in the insurance industry’s costly administrative inefficiencies when it comes to auditing data from third parties and contract validity as information becomes increasingly available from point innovations within the emerging IoT landscape.
Distributed ledgers serve to automate and standardize many of the paper-based, due diligence processes within claims filing, adjudication, and settlement. With smart contracts, insurance providers are able to predefine the conditions requisite for a claim to be paid out, ensuring the specificity and authenticity from the claimant in all parts of the process. By blending the use of sensors and blockchain, the emerging usage-based insurance (UBI) model allows insurance companies, especially in the auto insurance industry, to seamlessly obtain and utilize encrypted and immutable data about the usage, state, and condition of machinery at any given time. In drawing upon a distributed database with heavy encryption, insurance companies are able to capitalize on distributed ledgers in ameliorating privacy and security concerns from their clients whilst automating operational processes and conducting more efficient real-time and ex post facto data analysis.