A Study on the Application of the Chain Ladder Model in Non-Life Insurance Loss Reserves: From a Python Implementation Perspective

Abstract

This paper looks at how the Chain Ladder model can be used for non-life insurance loss reserves, with a focus on doing it in Python. It also compares this approach with the more traditional Excel way of working. In non-life insurance, companies have to set aside reserves for losses that have happened but not yet been fully paid. When there are many products and large amounts of data, Excel can be slow, error prone, and hard to keep track of. This paper explains the main ideas behind the Chain Ladder model, shows how Python can be used to implement it, and discusses both the good points and the problems. The Python chain-ladder library supports several common methods: the basic Chain Ladder, the Mack model, Bornhuetter–Ferguson, and Cape Cod. Python is better than Excel when it comes to automation, reproducibility, and handling many files at once. However, the Chain Ladder model still depends on the assumption that past claims behaviour will continue into the future. Also, using Python in insurance companies is not always easy because people need training and regulators have to approve the methods.