Implementing Custom Loss Functions in Advanced Machine Learning Structures for Targeted Outcomes

In the era of rapid technological advancement and ever-increasing data availability, the field of risk modeling faces both unprecedented challenges and opportunities. Traditional risk modeling approaches, while robust, often struggle to capture the complexity and dynamic nature of modern risk factors. This paper aims to provide a method for dealing with the insurance pricing problem of pricing predictability and MLOT (Money Left On Table) when writing a book of risks. It also gives an example of how to improve risk selection through suitable choices of machine learning algorithm and acquainted loss function. We apply this methodology to the provided data and discuss the impacts on risk selection and predictive power of the models using the data provided.