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Anderson, J.D., Bolton, C.G., Callan, G.L., Cross, M., Howard, S.K., Mitchell, G.R.J., Murphy, K.P., Rakow, J.C., Stirling, P.A. and Welsh, G.E. (2007) General Insurance Premium Rating—The Way Forward. Summary of the Recommendations of the General Insurance Premium Rating Working Party (GRIP). British Actuarial Journal, 13, 637-644.
https://doi.org/10.1017/S1357321700001586

has been cited by the following article:

• TITLE: Artificial Intelligence-Based Automated Actuarial Pricing and Underwriting Model for the General Insurance Sector

  AUTHORS: Brighton Mahohoho, Charles Chimedza, Florance Matarise, Sheunesu Munyira

  KEYWORDS: Artificial Intelligence, Automated Actuarial Loss Reserves, Automated Actuarial Risk Pricing, Automated Actuarial Underwriting

  JOURNAL NAME: Open Journal of Statistics, Vol.14 No.3, June 27, 2024

  ABSTRACT: 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.