Using an adaptive network‐based fuzzy inference system model to predict the loss ratio of petroleum insurance in Egypt

Ahmed A. Khalil, Zaiming Liu and Attia A. Ali

Risk Management and Insurance Review, 2022, vol. 25, issue 1, 5-18

Abstract: Insurance companies and those interested in developing insurance services seek to use modern mathematical and statistical methods to study further and analyze all the company's corporate internal and external performance indicators. Loss ratio is a vital indicator used to measure performance and predict future losses in insurance companies. Many pivotal processors, such as underwriting and pricing depending on it. Therefore, accurate predictions assist insurance companies in making decisions properly. Thus, this paper aims to use the adaptive network‐based fuzzy inference system (ANFIS) and autoregressive integrated moving average (ARIMA) models in forecasting the loss ratio of petroleum insurance in Misr Insurance Holding Company from 1995 to 2019. We applied many ANFIS models according to ANFIS properties and used the first 21 years (1995–2015), making up the training data set, which represents 85% of the data, as well as the past 4 years (2016–2019). Which are used for the testing stage and represent 15% of the data. Our finding concluded that ANFIS models give more accurate results than ARIMA models in predicting the loss ratio during the investigation by comparing results using predictive accuracy measures.

Date: 2022
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https://doi.org/10.1111/rmir.12200

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Persistent link: https://EconPapers.repec.org/RePEc:bla:rmgtin:v:25:y:2022:i:1:p:5-18

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