Enhancing operational efficiency of insurance companies: a fuzzy time series approach to loss ratio forecasting in the Egyptian market

This article analyses the crucial significance of loss ratio (LR) evaluation in assessing the operational efficiency of insurance companies. Fuzzy time series (FTS) models are effective in modelling non-linear with uncertainties and providing adequate performance with limited data availability, which overcomes the shortage of conventional models used in Egyptian insurance market literature. This paper aims to introduce a comprehensive analysis of LR of the property and casualty sectors. Moreover, review and compare various forecasting FTS techniques to discuss the effectiveness of using FTS methods in forecasting LR. The suggested method is based on results from tests done with four models that had Huarng 465 partitions and interval length parts of 5, 10, 50, and 100. The results show LR prediction improved significantly. Yu and Cheng’s models using a Huarng 465 partition for training data and the Yu model with 100 partitions for testing data had high accuracy and low error.

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Publication Details

Journal
Journal of Business Analytics
Published
2024-08-26
DOI
https://doi.org/10.1080/2573234x.2024.2393609
Citations
3
Primary Topic
Stock Market Forecasting Methods
Type
article
Field-Weighted Citation Impact
1.47
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article

Enhancing operational efficiency of insurance companies: a fuzzy time series approach to loss ratio forecasting in the Egyptian market

Ahmed Ali, A. A. I. Khalil, Zaiming Liu
3 citations
Journal of Business Analytics
Stock Market Forecasting Methods
1.47
article

Enhancing operational efficiency of insurance companies: a fuzzy time series approach to loss ratio forecasting in the Egyptian market

Ahmed Ali, A. A. I. Khalil, Zaiming Liu
article en
3 citations

Abstract

This article analyses the crucial significance of loss ratio (LR) evaluation in assessing the operational efficiency of insurance companies. Fuzzy time series (FTS) models are effective in modelling non-linear with uncertainties and providing adequate performance with limited data availability, which overcomes the shortage of conventional models used in Egyptian insurance market literature. This paper aims to introduce a comprehensive analysis of LR of the property and casualty sectors. Moreover, review and compare various forecasting FTS techniques to discuss the effectiveness of using FTS methods in forecasting LR. The suggested method is based on results from tests done with four models that had Huarng 465 partitions and interval length parts of 5, 10, 50, and 100. The results show LR prediction improved significantly. Yu and Cheng’s models using a Huarng 465 partition for training data and the Yu model with 100 partitions for testing data had high accuracy and low error.

Journal of Business AnalyticsVol. 7(4)
Central South University (CN), Assiut University (EG)
Openalex Percentile: Top 16%
Stock Market Forecasting Methods
1.47
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