Intelligent underwriting: applying machine learning to risk assessment

Intelligent underwriting (IU) is a new, specialized area of work in the smart insurance space that seeks to harness the latest advancements in artificial intelligence (AI) and machine learning (ML) to deliver near-term rewards for the global insurance community and consumers. Policies can be better priced and discounted, thereby relinquishing the untimely and undue transactions between creators of risk and insurers. The insurance business will apply these advances to deliver higher corporate net profits and shareholder valuations. Service empowers insurers to positively interact more quickly, adequately, and accurately with consumers and policy owners throughout the insurance process life cycle, from the selection of the product to coverage settlement. No technology disruption will rescue laggard insurers, but for those insurers that proactively embrace intelligent underwriting, the advantages it brings will raise their business fortunes considerably (Ribeiro et al., 2016; Wüthrich, 2020; Henckaerts et al., 2022).

For consumer-facing insurers, improved consumer experiences, driven by information technology for acquiring and servicing insurance needs, are enhancing loyalty towards the insurance brands. Back-office operations driven by core technology systems, however, have been slow in transforming. Processes in many operational and support areas are particularly in need of radical overhaul. These areas work best with speedy touchless processing of clean business that is the low frequency by definition. Intelligent underwriting answers directly to this history of disruption in back office operational and support areas. For reasons that we will cover, these core processes - operations, risk assessment, pricing, actuarial, grievance settlement, and so on - have missed the AI-ML train in recent years when a majority of the world's focus has been on digital enablers of front office customer-facing business (Yoon et al., 2018; Wüthrich & Merz, 2021).