Yousuf, W., Stansfield, J., Malde, K., Mirin, N., Walton, R., Thorpe, B., et al. (2021) The IFRS 17 Contractual Service Margin: A Life Insurance Perspective. British Actuarial Journal, 26, e2.
https://doi.org/10.1017/s1357321721000015

has been cited by the following article:

• TITLE: Automated Actuarial Data Analytics-Based Inflation Adjusted Frequency Severity Loss Reserving Model

  AUTHORS: Brighton Mahohoho

  KEYWORDS: Automated Actuarial Loss Reserves, Artificial Intelligence, Micro Finance Loss Reserving, Auto Insurance Loss Reserving, Both Services Loss Reserving

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

  ABSTRACT: 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.