The Advanced Actuarial Data Science Based AI-Driven Solutions for Automated Loss Reserving Under IFRS 17 in Non-Life Insurance

Abstract

This study introduces an AI-driven Automated Actuarial Loss Reserving Model (AALRM) designed to meet IFRS 17 standards for non-life insurance. The model leverages advanced machine learning techniques to improve accuracy, efficiency, and adaptability in loss reserves, with a specific focus on inflation-adjusted frequency-severity modeling. A unique aspect of this research is the integration of bancassurance services, enabling automated management for both microfinance and car insurance on a unified platform. This includes a no-claims bonus system that categorizes policyholders into four tiers—base, variable, final, and high-bonus—resulting in more precise risk assessments and enhanced customer retention. Among eight evaluated machine learning algorithms, the Random Forest (RANGER) outperformed others for estimating Aggregate Comprehensive Automated Actuarial Loss Reserves (ACAALR). The model’s effectiveness was validated through stress tests, scenario analyses, and comparisons with traditional methods like the Chain Ladder. Additionally, the study introduces a novel Robust Automated Actuarial Loss Reserve Margin (RAALRM) with adaptive bounds, addressing traditional limitations in reserve margin calculations. This AI-integrated approach significantly improves predictive accuracy, operational efficiency, and strategic decision-making, offering a scalable solution for the insurance industry.

Keywords

• Actuarial Data Science
• Artificial Intelligence
• Data Analytics
• Automated Actuarial Loss Reserves
• Actuaries
• Machine Learning