Source – https://www.forbes.com/
Insurance works with large amounts of data, about many individuals, many instances requiring insurance, and many factors involved in solving the claims. To add to the complexity, not all insurance is alike. Life insurance and automobile insurance are not (as far as I know) the same thing. There are many similar processes, but data and numerous flows can be different. Machine learning (ML) is being applied to multiple aspects of insurance practice.
Insurance is about risk. The insurance industry sets rates based on expected payouts so that, hopefully, they end up with positive revenue. Setting rates and understanding payout in order to maintain profitability is complex, and the industry hope is that ML can help in achieving that goal. Note, here, I’m focusing more on ML than artificial intelligence (AI), because many of the complex statistical tools that are now considered ML can more efficiently accomplish some of the tasks than would neural networks, expert systems, or other purely AI tools.
There are multiple ways machine learning can help in the insurance industry. Let us take a look at three.
Health and life insurance are complex. There are multiple factors that go into understanding an individual’s risk factors for disease, illness, and mortality. Insurance underwriters have historically used a core set of factors such as male/female, age, and smoker/non-smoker. When other factors have been used, such as zip code, the problem of red-lining has appeared in insurance as well as the more well-known area of financial red-lining. Therefore, there are regulations about how some demographic information must be used.