The Indian insurance sector needs to adopt predictive analytics to tackle risks surrounding early claims, no matter how slim the margin, say Jasjeet Singh, partner, and Jayesh Raj, senior manager, of Financial Services Analytics Advisory at the professional services firm EY.

Early death claims (claims received within 0-2 years of policy issuance) are a primary risk focus for Indian life insurers. Typically, early death claim rates range between 0.2% and 1.0% of policies issued, with a high proportion of such claims being fraudulent (for example, dead-man insurance, misrepresentation of health/financial information), the authors wrote in Forbes India.

Detecting early-claim risk before issuing a policy can not only help insurers reduce operational costs, it can also make life insurance more affordable, as the saved cost is passed on to consumers. Despite the focus on reducing early-claim risk, most Indian insurers still rely on intuitive, rule-based frameworks rather than predictive analytics-driven automated workflows for underwriting. This results in high false positives (rejected cases that would not have resulted into a claim), higher physical verification costs and longer decision cycles.

The authors say that a holistic approach is necessary to build a differential underwriting workflow for modelled risk categories of customers–- for example, very high risk (0.5%) are auto rejected, high risk are referred for on ground verification & mandatory medicals (1%), medium risk (5%) are referred for mandatory medicals etc.

They also say that while predictive models can help insurers contain originations risk, there is a strong case for the industry as well to share risk data. Incorporating a central insurance data custodian to maintain and share industry-wide data repositories, including risk information on geo-locations, sourcing agents, claims and high risk customers, can enable better underwriting decisions.