Risks and Control of Insurance Undertakings

Cappiello, Antonella

doi:10.1007/978-3-030-43142-6_2

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Apart from their contribution to achieving a company's profit, maintaining financial position integrity, and improving competitive position in the market, insurance companies must concern themselves with the competition. That is particularly true in Egypt, where companies must contend with competition on domestic and foreign fronts while also dealing with recent economic changes in the Egyptian insurance market (Cappiello, 2020; Ibrahim et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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

Khalil

Liu

Ali

2022

Risk Manage Insurance Review

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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.

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Section: Introductionmentioning

confidence: 99%