Neural networks approach for determining total claim amounts in insurance


Dalkilic T., Tank F., Kula K. S.

INSURANCE MATHEMATICS & ECONOMICS, vol.45, no.2, pp.236-241, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 45 Issue: 2
  • Publication Date: 2009
  • Doi Number: 10.1016/j.insmatheco.2009.06.004
  • Journal Name: INSURANCE MATHEMATICS & ECONOMICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.236-241
  • Keywords: Neural networks, Least squares method, Total claim amount, Claim amount payments, Fuzzy if-then rules, ADAPTIVE-NETWORK, FUZZY-LOGIC, REGRESSION, VALIDITY
  • Karadeniz Technical University Affiliated: Yes

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. (C) 2009 Elsevier B.V. All rights reserved.