Machine Learning Catching On In Insurance, But Challenges Remain

Emerging tools such as artificial intelligence and natural language processing are being used in the insurance sector, but costs remain high and there are questions about bias being introduced into machine learning, according to a speaker at the Public Risk Management Association’s annual meeting Monday.

‘Everything is smart these days,’ said Brian Billings, vice president of predictive analytics in Ballwin, Missouri, for Midwest Employers Casualty Co., part of W.R Berkeley Corp., and such devices as cell phones and televisions now collect data from their users. ‘All of that technology is being driven by the use of data.’

Machine learning, including artificial intelligence and natural language processing, takes the data being collected and tries to predict some kind of outcome, Mr. Billings said, such as a numerical value or, in the case of the insurance sector, a claims scenario.

With natural language processing, a model is trained to read text, Mr. Billings said. Such technology can take a 40-page discharge summary and extract specific relevant text, such as all doctors’ or lawyers’ names, or medical notes. ‘It has huge implications in the claims adjusting space.’