Can An AI Determine Which Potentially Fraudulent Motor Insurance Claims Are Worth Contesting?

Imagine you’re a claims manager for a motor insurer who’s been given 100 potentially fraudulent first notifications of loss to review. The question you’ve been asked is a simple yet stark one: of these 100 cases, how many is the insurance company likely to successfully repudiate and how many will it have to pay?

It’s a question that demands all your insight, knowledge and years of experience. On the face of it, it seems to be a question best suited to human intelligence rather than machine intelligence.

But new technology means that is changing. Rapidly.

The idea of using artificial intelligence (AI) and algorithms to detect fraud has been around for a long time. It’s been used in banking for several years and insurers have long been aware of the concept, but our sector has been held back by one major problem: the quality of the data insurers hold.

Data is the lifeblood of AI. The more data an algorithm—a set of rules—can crunch, the more it learns. And even though insurers store vast quantities of data, the formatting is inconsistent, and its storage varies from business to business.