The Occupational Indemnity Insurance Modelling: Brighton Mahohoho XGBoost Probabilistic Automated Actuarial Reserving-Pricing-Underwriting - Mahohoho, Brighton; Chimedza, Charles; Matarise, Florance; Munyira, Sheunesu (Ital Publication, 2025)
This paper introduces the IFRS 17-Compliant Brighton Mahohoho Probabilistic Framework for Inflation-Adjusted Frequency-Severity Modeling in Occupational Indemnity Insurance, integrating AI-driven actuarial methodologies for loss reserving, risk pricing, and underwriting. Objectives: The framework ensures IFRS 17 compliance while enhancing actuarial accuracy and operational efficiency. Methods/Analysis: A simulation-based dataset of policy, claims, premiums, inflation adjustments, and underwriting data is generated. Claim frequencies and severities are modeled using Poisson and Gamma distributions, with inflation adjustments incorporated into reserves. XGBoost is applied for Automated Actuarial Loss Reserving (ALR) and Automated Actuarial Risk Pricing (ARP), while a weighted average approach estimates Automated Actuarial Loss Reserve Risk Premiums (AALRRP). Findings: Model accuracy is validated through MAE, MSE, RMSE, residual analysis, and scatter plots. IFRS 17 metrics—Contractual Service Margin (CSM), Fulfillment Cash Flows (FCF), Risk Adjustments, and Liabilities—are simulated, with sensitivity analysis ensuring robustness. Policyholders are segmented into underwriting clusters, incorporating expenses, outgo, and revenue to derive the Automated Net Actuarial Underwriting Balance (ANAUB). Novelty/Improvement: This integrated AI-driven actuarial framework significantly advances IFRS 17-compliant pricing and reserving, offering enhanced predictive accuracy, regulatory alignment, and improved risk assessment in occupational indemnity insurance.