Claim Analytics across Multiple Insurance Lines of Business

Abstract

Claim loss payouts attribute most significantly to the overall costs of any insurance company and subsequently have a greater impact on its profits. Settling claims early, detecting fraudulent claims, increasing customer satisfaction and customer retention through recurring business have become increasingly important. The key to gain competitive edge is the ability to quickly and efficiently explore and understand data, settle claims and enhance customer experience. Claims processing, for any line of business in insurance (i.e. auto, life or health) is time consuming and labor-intensive involving multiple systems and several business units. To address the above challenges and support the claim handlers decision, information that is available during the first notification of loss (FNOL) is used to predict claim severity using predictive analytics. A claim that is straight forward with reliable evidences can be considered as a simple claim which can be settled quickly and dealt by a junior adjustor. While a claim which involves accident, death or a police case can be considered as complex and needs a team of senior adjustors or lawyers to get involved. Using the prediction model, the claims department can classify the claims based on its severity and assign it to the respective team thus improving its operations. Additionally the research also involves analysis of similarities and differences between the key attributes across three lines of business in insurance (auto, life and health) that impact the claims severity. By further studying the claim trends across these lines of business for a particular geography or demography, business can further refine the risks considered during underwriting or can design new products with add-ons which will be beneficial to the customers and contribute to increased business.

Keywords: Insurance, Claim Analytics, Claim Severity, Fraud Detection, FNOL, Customer Satisfaction

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