Managing tree models plasticity and mixing GLMs with regression trees for insurance ratemaking
Predicting policyholders' claims over a year is crucial for a Property-Casualty insurance company. These expenditures, popularly called losses, are incurred by the insurer when reimbursing the policyholders' claims. The insurance company is required to pay any legitimate claim made by a policyholder, in exchange the latter pays an amount of money, called the premium, to the company to buy this entitlement. Annual premium must be calculated with precision to ensure a fair deal on both sides.
It is the task of actuaries to set premiums for all policyholders; this is called ratemaking. Various classical statistical methods are used to set a policy premium rate, so maximize gain without losing existing customers to the competition. We explore here several data science techniques to help improve this ratemaking process.