Claims Optimization: Is All Fair in Motor Insurance Payouts?
Data & Machine Learning
If you have ever bought something online, there is a high probability that you have been subject to price optimization, a process whereby companies cleverly use big data and analytics to determine how customers will respond to different prices through different channels to maximize operating profits. In short, prices are flexed based on a customer’s propensity to buy.
In the insurance industry, price optimization is adopted with risk-rated pricing models used to calculate customer premiums on insurance products. It is also an approach that could be employed with the claims element of insurance policies, optimizing the financial settlements that are paid out when claims are made. When, how and with whom this is done throughout the claim’s lifecycle can, however, lead to questions of fairness.
In Ireland, there has been growing concern about how fraudulent claims and overly generous financial awards have impacted policy pricing and how that, in turn, has negatively affected private individuals and small businesses. A recent incident in Ireland involving a high-profile politician is a case in point. The individual made a substantial claim for an accident that involved falling off a swing in a hotel bar, but eventually, they withdrew the claim. At some point in the claim process, both the hotel and its insurance company would have decided to ‘fight’ the claim. This decision can be perceived as rational and practical, and in fact, inevitable. Indeed, it was probably a decision made by a human rather than a machine. However, what if it wasn’t? Moreover, does it matter?
In this article, we look at when and how data and machine learning is used to optimize motor claims payments and discuss whether or not this is ethical in the fourth industrial revolution.
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