Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Multimodal Based Clouds Computing Systems for Healthcare and Risk Forecasting Based on Subjective Analysis Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A New Neuro-Fuzzy Inference System for Insurance Forecasting

Authors : Le Hoang Son, Mai Ngoc Khuong, Tran Manh Tuan

Published in: Advances in Information and Communication Technology

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Insurance forecasting is a matter of vital importance to insurance companies for analyzing of annual income, premium and loss reserving, loss payment, etc. Recent years have also seen increasing discussion within the actuarial community of the need for insurance forecasting techniques that are more solidly grounded in rigorous machine learning methodologies. Taking advantages of knowledge-reuse and learning capability for dealing with uncertainties, hybridization of neural networks and fuzzy logic could enhance the accuracy of forecasting for insurance applications. In this paper, we propose a novel neuro-fuzzy inference system for insurance forecasting. It uses multiple parameter sets where each set is responsible for a small subset of records. The aim of each parameter set is to minimize Mean Square Error within records of the subset. The learning strategy and a rule reduction method are also proposed. Empirically validation on the benchmark and real insurance datasets show the advantages of the new system.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter A Method for Clustering and Identifying HTTP Automated Software Communication
next chapter A New Schema to Identify S-farnesyl Cysteine Prenylation Sites with Substrate Motifs
Literature
1.
Shapiro, A.F.: An overview of insurance uses of fuzzy logic. In: Chen, S.-H., Wang, P.P., Kuo, T.W. (eds.) Computational Intelligence in Economics and Finance, pp. 25–61. Springer, Heidelberg (2007)CrossRef
2.
3.
Shapiro, F.: Insurance applications of neural networks, fuzzy logic, and genetic algorithms. In: Intelligent and Other Computational Techniques in Insurance: Theory and Applications (2003)
4.
Ana-Maria, B., Ghiorghe, B.: Application of autoregressive models for forecasting marine insurance market. Ovidius Univ. Ann. Ser. Econ. Sci. 13(1), 1125–1129 (2013)
5.
6.
7.
De Alba, E.: Claims reserving when there are negative values in the runoff triangle: Bayesian analysis using the three-parameter log-normal distribution. N. Am. Act. J. 10(3), 1–15 (2006)MathSciNetCrossRef
8.
Gaver, J.J., Paterson, J.S.: Do insurers manipulate loss reserves to mask insolvency problems? J. Acc. Econ. 37, 393–416 (2004)CrossRef
9.
Antonio, K., Beirlant, J.: Issues in claims reserving and credibility: a semiparametric approach with mixed models. J. Risk Ins. 75, 643–676 (2008)CrossRef
10.
Bernoth, K., Pick, A.: Forecasting the fragility of the banking and insurance sectors. J. Bank. Finance 35(4), 807–818 (2011)CrossRef
11.
Abdullah, L., Rahman, M.N.A.: Employee likelihood of purchasing health insurance using fuzzy inference system. Int. J. Comput. Sci. 9(1), 112–116 (2012)
12.
Son, L.H., Linh, N.D., Long, H.V.: A lossless DEM compression for fast retrieval method using fuzzy clustering and MANFIS neural network. Eng. Appl. Artif. Intell. 29, 33–42 (2014)CrossRef
13.
Martínez-Miranda, M.D., Nielsen, J.P., Wüthrich, M.V.: Statistical modelling and forecasting of outstanding liabilities in non-life insurance. SORT 36(2), 195–218 (2012)MathSciNetMATH
14.
Wüthrich, M.V., Merz, M.: Stochastic Claims Reserving Methods in Insurance. John Wiley & Sons, West Sussex (2008)MATH
15.
Siddique, N., Adeli, H.: Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing. John Wiley & Sons (2013)
16.
England, P.D., Verrall, R.J.: Stochastic claims reserving in general insurance. Br. Act. J. 8, 443–518 (2002)CrossRef
17.
Mulquiney, P., et al.: Artificial Neural Networks in Insurance Loss Reserving (2011)
18.
Mack, T.: Measuring the Variability of Chain Ladder Reserve Estimates, Casualty Actuarial Society Forum, pp. 101–182 (1994)
19.
Kaymak, U.: Defining a financial forecasting model for healthcare insurance companies, Eindhoven University of Technology (2013)
20.
21.
Beaver, W.H., McNichols, M.F., Nelson, K.K.: Management of the loss reserve accrual and the distribution of earnings in the property-casualty insurance industry. J. Acc. Econ. 35, 347–376 (2003)CrossRef
22.
Zhang, Y., Dukic, V., Guszcza, J.: A Bayesian non-linear model for forecasting insurance loss payments. J. R. Stat. Soc. Ser. A (Stat. Soc.) 175(2), 637–656 (2012)MathSciNetCrossRef
Metadata
Title
A New Neuro-Fuzzy Inference System for Insurance Forecasting
Authors
Le Hoang Son
Mai Ngoc Khuong
Tran Manh Tuan
Copyright Year
2017
Book
Advances in Information and Communication Technology

Print ISBN: 978-3-319-49072-4

Electronic ISBN: 978-3-319-49073-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-49073-1_9

Premium Partner

    Image Credits