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.

KEYWORDS
ANFIS, ARIMA, Egypt, forecasting, loss ratio, neuro‐fuzzy
system