Neural networks approach for determining total claim amounts in insurance

Turkan Erbay Dalkilic, Fatih Tank and Kamile Sanli Kula

Insurance: Mathematics and Economics, 2009, vol. 45, issue 2, 236-241

Abstract: In this study, we present an approach based on neural networks, as an alternative to the ordinary least squares method, to describe the relation between the dependent and independent variables. It has been suggested to construct a model to describe the relation between dependent and independent variables as an alternative to the ordinary least squares method. A new model, which contains the month and number of payments, is proposed based on real data to determine total claim amounts in insurance as an alternative to the model suggested by Rousseeuw et al. (1984) [Rousseeuw, P., Daniels, B., Leroy, A., 1984. Applying robust regression to insurance. Insurance: Math. Econom. 3, 67-72] in view of an insurer.

Keywords: Neural; networks; Least; squares; method; Total; claim; amount; Claim; amount; payments; Fuzzy; if-then; rules (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:45:y:2009:i:2:p:236-241

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