The IFRS17 Regulated Travel Insurance Intelligent Non-Linear Regression based Inflation Adjusted Frequency-Severity Automated Loss Reserve Risk Pricing and Underwriting Model with Applications of the Actuarial Specific Gaussian Process Regression (GPR) Model

This paper presents a novel approach to actuarial modeling and risk pricing for travel insurance under IFRS 17 regulations. We develop an advanced, inflation-adjusted frequency-severity model using Gaussian Process Regression (GPR) to forecast claim frequencies, severities, and premiums. Our methodology integrates synthetic data generation, com- prehensive exploratory data analysis, and sophisticated GPR modeling to address the complexities of travel insurance pricing. We also incorporate an inflation adjustment model and perform extensive scenario and stress testing to assess the robustness of our predictions. The resulting model not only provides automated actuarial estimates of loss reserves and risk premiums but also offers a detailed calculation of IFRS 17 metrics such as Contract Service Margin (CSM) and Loss Ratio. This approach is distinguished by its innovative use of clustering and visualization techniques for underwriting, as well as its comprehensive analysis of financial health through simulated actuarial features and expenses. The findings contribute to more accurate and responsive insurance pricing strategies, enhancing compliance with regulatory standards and improving financial re-porting.

The authors declare no conflict of interest.

Not applicable

The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.