Title: Hybrid predictive modelling for insurance premium retention: integrating statistical and AI techniques

Authors: Ahmed A. Khalil; Zaiming Liu

Addresses: School of Mathematics and Statistics, Central South University, Hunan, China; Faculty of Commerce, Assiut University, Assiut, Egypt ' School of Mathematics and Statistics, Central South University, Hunan, China

Abstract: This research highlights the critical role of forecasting in the insurance industry and emphasises the premium retention ratio (PRR) as a key internal performance indicator for evaluating insurance company operations. Traditional time series models like ARIMA and exponential smoothing face limitations in capturing complex data patterns. To address this, the study proposes a hybrid predictive model that combines statistical time series models (ARIMA, EXP) with advanced AI techniques (ANN, SVR) to enhance PRR prediction accuracy in Egypt's Fire, Marine, and Aviation insurance sectors. Using 80% of data for training (1989–2015) and 20% for testing (2016–2021), the study demonstrates that hybrid models, particularly ARIMA-ANN and EXP-ANN, outperform conventional models. The findings suggest that incorporating ANN into these models significantly improves prediction accuracy. This research offers a novel approach to forecasting in the Egyptian insurance market and provides publicly accessible datasets for further comparative studies across different countries.

Keywords: artificial neural network; ANN; artificial intelligence; autoregressive integrated moving average; ARIMA; exponential smooth; Egyptian insurance market; statistical time series; support vector machine; SVM; insurance.

DOI: 10.1504/IJCSE.2025.147612

International Journal of Computational Science and Engineering, 2025 Vol.28 No.4, pp.371 - 382

Received: 22 Jan 2024
Accepted: 08 Sep 2024
Published online: 24 Jul 2025 *

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