Insurance Pricing

Insurance pricing research aims to develop accurate, fair, and efficient methods for determining premiums, balancing profitability with equitable risk assessment. Current efforts focus on leveraging machine learning, including neural networks (like feed-forward and split-boost architectures), gradient boosting machines, and generative models, to improve prediction accuracy and incorporate diverse data sources (e.g., climate models for catastrophe insurance, medical records for health insurance). This research is crucial for mitigating risks associated with increasingly frequent extreme events and ensuring the long-term solvency and fairness of the insurance industry, while also addressing ethical concerns around bias and discrimination in pricing algorithms.