Historically, insurers determine premiums by running linear regression on a small number of risk factors, largely reported by policy-holders. However, a good prediction model of individual insurance costs is becoming a business essential as competition in the industry and low customer switching costs become key drivers toward a pricing structure that covers insurers’ incurred costs....
Predictive algorithms allow insurers the opportunity to dramatically adjust premiums. A good model allows insurance companies to optimize pricing policies while predicting which clients are at higher risk to cause “large-loss cases” where they bring major lawsuits against the company during the insurance period.
More than the maximization of profit and income, price optimization also helps to increase the customer satisfaction and long-term loyalty.
A good pricing model includes the amount clients pay based on age, coverage type, client needs. It should also account for personal information like claims history, driving record, credit, gender, marital status, health, hobbies, job, and more.
Essentially, any data that helps to predict risk and price more accurately must be included in a good pricing model. For example, property insurance needs continual monitoring of variables like neighborhood claims, construction costs, weather, and natural disasters.
Additional useful data include information on customer types. In essence, machine learning algorithms adjust insurance products to customers based on personal preferences, behavior, and response to prices.
Verifying that the information clients provide to the insurance company is accurate and up-to-date is a major challenge. The biggest challenge, however, is climate change—the increasing frequency of natural disasters lead to premium increases and policy restrictions.
AXA, the large global insurance company, has used machine learning in a POC to optimize pricing by predicting “large-loss” traffic accidents with 78% accuracy.
Google Cloud Platform: Using machine learning for insurance pricing optimization
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