Dalkilic, Turkan ErbayTank, FatihKula, Kamile Sanli2020-06-252020-06-252009closedAccess0167-66871873-5959https://doi.org/10.1016/j.insmatheco.2009.06.004https://hdl.handle.net/20.500.12587/4366Tank, Fatih/0000-0003-3758-396XIn 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. (C) 2009 Elsevier B.V. All rights reserved.eninfo:eu-repo/semantics/closedAccessNeural networksLeast squares methodTotal claim amountClaim amount paymentsFuzzy if-then rulesArticle45223624110.1016/j.insmatheco.2009.06.0042-s2.0-70349233798Q1WOS:000271335800011Q2