Abstract

Rate making in insurance refers to the pricing of insurance premiums through calculations, by actuaries, and adjustments in various factors. Fair pricing of insurance products is of utmost importance for insurance companies to be able to face market competition and stay in business. Therefore poor rate making, which could be the result of poor prediction of risks, would be dangerous for insurers.

Insurance data is characterized by an imbalance between the number of policyholders that claim and those that do not. The majority of premium payers do not incur accidents and thus do not claim their losses, resulting in a large number of “zero–claims". However, it is very important for the company to identify the customers who are more likely in future to file a claim, because every claim incurs a cost to the enterprise. Chapter 1 proposes a sampling technique devised to improve the identification of the possible future losses by better tracking of the non–zero claims.

Generalized Linear Models have long been used by actuaries to accomplish the rate making task. The method is parametric and is based on certain assumptions about the distribution of the data. Insurance data with its probability mass at zero, do not fall exactly into the framework of GLMs. However, in the recent years, various new machine learning algorithms have provided improvement by being more effective predictors than GLMs. What these algorithms lack is interpretability. Chapter 2 uses simple algorithms like regression trees, in combination with GLMs, to create a pre–processed GLM that is more effective than a standalone GLM.

The endeavour of improving the classical GLM continues in Chapter 3. Here the another combination of trees and regularized GLMNet is used to produce results with more predictive capability that any one of these algorithms as stand-alone. The new results are interpretable as well as improved.