Insurance Underwriting And Claims With AI

Insurance companies constantly evaluate risk during underwriting process. Tricky part is not to underestimate or overestimate the risk profile. Lot of factors are considered when designing the policy and one the main metrics of concern is the claims that are likely to be incurred over the next year. User profile metrics are matched with the policy metrics to evaluate the likely premium to be charged.

Deep learning can understand relationship between different features and create a complex model that can optimize the end goal. What do you give as the end goal or target to optimize?

Most time it’s the premium or no of claims. Deep learning find attributes that has made a successful claim from the previously available data. It then forms demographics, user behavior, external factors those led to a claims so that if a similar condition is prevalent in the coming year then your claims are going to increase.

There can be a misguided optimizations as well such evaluating customers only based on credits and reject people with the risk of applying for a claim. Insurance is about spreading out the risk to other insurance holders. It’s not about rejecting everyone who are at risk. It’s about a ratio number of people that’re likely to apply a successful claims over the number of people in insurance.

Proper use of AI in claims management would be finding a specific number of least risky policy over one policy with potential claim. It’s the optimization of this ratio that would enable a good underwriting with automation. Insurance right now use rule based underwriting and case based underwriting.

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