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automated actuarial Data Analytics-Based Inflation Adjusted Frequency Severity loss Reserving Model

Open Journal of Statistics 2024年第3期14卷 341-393页

作者： Brighton Mahohoho Department of Mathematics & Computational Sciences University of Zimbabwe Harare Zimbabwe

In this paper, the automated actuarial loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological advancement currently at hand. As a result, the development of the alternative Artificial Intelligence Based automated actuarial loss Reserving Methodology which captures diverse risk profiles for various policyholders through augmenting the Micro Finance services, Auto Insurance Services and Both Services lines of business on the same platform through the computation of the Comprehensive automated actuarial loss reserves (CAALR) has been implemented in this paper. The introduction of the four further types of actuarial loss reserves to those existing in the actuarial literature seems to significantly reduce lapse rates, reduce the reinsurance costs as well as expenses and outgo. As a matter of consequence, this helps to bring together a combination of new and existing policyholders in the insurance company. The frequency severity models have been extended in this paper using ten machine learning algorithms which ultimately leads to the derivation of the proposed machine learning-based actuarial loss reserving model which remarkably performed well when compared to the traditional chain ladder actuarial reserving method using simulated data.

关键词： automated actuarial loss reserves Artificial Intelligence Micro Finance loss Reserving Auto Insurance loss Reserving Both Services loss Reserving

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Artificial Intelligence-Based automated actuarial Pricing and Underwriting Model for the General Insurance Sector

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Open Journal of Statistics 2024年第3期14卷 294-340页

作者： Brighton Mahohoho Charles Chimedza Florance Matarise Sheunesu Munyira Department of Mathematics & Computational Sciences University of Zimbabwe Harare Zimbabwe School of Statistics & actuarial Science University of Witwatersrand Johannesburg South Africa

The automated actuarial Pricing and Underwriting Model has been enhanced and expanded through the implementation of Artificial Intelligence to automate three distinct actuarial functions: loss reserving, pricing, and underwriting. This model utilizes data analytics based on Artificial Intelligence to merge microfinance and car insurance services. Introducing and applying a no-claims bonus rate system, comprising base rates, variable rates, and final rates, to three key policyholder categories significantly reduces the occurrence and impact of claims while encouraging increased premium payments. We have enhanced frequency-severity models with eight machine learning algorithms and adjusted the automated actuarial Pricing and Underwriting Model for inflation, resulting in outstanding performance. Among the machine learning models utilized, the Random Forest (RANGER) achieved the highest Total Aggregate Comprehensive automated actuarial loss Reserve Risk Pricing Balance (ACAALRRPB), establishing itself as the preferred model for developing automated actuarial Underwriting models tailored to specific policyholder categories.

关键词： Artificial Intelligence automated actuarial loss reserves automated actuarial Risk Pricing automated actuarial Underwriting

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