A self-organizing predictive map for non-life insurance

  • Donatien HainautEmail author
Original Research Paper


This article explores the capacity of self-organizing maps (SOMs) for analysing non-life insurance data. Contrary to feed forward neural networks, also called perceptron, a SOM does not need any a priori information on the relevancy of variables. During the learning procedure, the SOM algorithm selects the most relevant combination of explanatory variables and reduces by this way the collinearity bias. However, the specific features of insurance data require adapting the classic SOM framework to manage categorical variables and the low frequency of claims. This work proposes several extensions of SOMs in order to study the claims frequency of a portfolio of motorcycle insurances. Results are next compared to these computed with variants of the k-mean clustering algorithm.


Neural network Self organizing map Non-life insurance Claims frequency Regression models k-means algorithm 



I gratefully acknowledges the BNP Cardif Chair “Data Analytics and Models for Insurance” for its financial support. I also thank Michel Denuit from the UCL for his constructive advices.


  1. 1.
    Arthur D, Vassilvitskii S (2007) \(K\)-means\(++\): the advantages of careful seeding. In: Proceedings of the 18th annual ACM-SIAM symposium on discrete algorithms, SODA’ 07, pp 1027–1035Google Scholar
  2. 2.
    Benzécri JP (1973) L’ analyse des données, T2, l’ analyse des correspondances. Dunod, ParisGoogle Scholar
  3. 3.
    Brockett P, Cooper W, Golden L, Pitaktong U (1994) A neural network method for obtaining an early warning of insurer insolvency. J Risk Insur 61(3):402–424CrossRefGoogle Scholar
  4. 4.
    Brockett P, Xia X, Derrig R (1998) Using Kohonen’s self-organizing feature map to uncover automobile bodily injury claims fraud. J Risk Insur 65(2):245–274CrossRefGoogle Scholar
  5. 5.
    Brockett P, Golden L, Jang J, Yang C (2006) A comparison of neural network, statistical methods, and variable choice for life insurers’ financial distress prediction. J Risk Insur 73(3):397–419CrossRefGoogle Scholar
  6. 6.
    Burt C (1950) The factorial analysis of qualitative data. Br J Psychol 3:166–185Google Scholar
  7. 7.
    Cottrell M, Ibbou S, Letrémy P (2004) SOM-based algorithms for qualitative variables. Neural netw 17:1149–1167CrossRefGoogle Scholar
  8. 8.
    Greenacre MJ (1984) Theory and applications of correspondence analysis. Academic Press, LondonzbMATHGoogle Scholar
  9. 9.
    Hainaut D (2018) A neural-network analyzer for mortality forecast. ASTIN Bull 48(2):1–28MathSciNetCrossRefGoogle Scholar
  10. 10.
    Huysmans J, Baesens B, Vanthienen J, Van Gestel T (2006) Failure prediction with self organizing maps. Expert Syst Appl 30(3):479–487CrossRefGoogle Scholar
  11. 11.
    Hertz J, Krogh A, Palmer RG (1991) Introduction to the theory of neural computation. Addison-Wesley, ReadingGoogle Scholar
  12. 12.
    Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lebart L, Morineau A, Warwick KM (1984) Multivariate descriptive statistical analysis: correspondence analysis and related techniques for large matrices. Wiley, New YorkzbMATHGoogle Scholar
  14. 14.
    Lloyd SP (1957) Least square quantization in PCM (Bell telephone laboratories paper. Published in journal much later: Lloyd., S. P., 1982). IEEE Trans Inf Theory 28(2):129–137CrossRefGoogle Scholar
  15. 15.
    Ogunnaike RM, Si D (2017) Prediction of insurance claim severity loss using regression models. In: Proceedings of the 13th conference on machine learning and data mining. Springer, New York, pp 233–247Google Scholar
  16. 16.
    Ohlsson E, Johansson B (2010) Non-life insurance pricing with generalized linear models. Springer, New YorkCrossRefGoogle Scholar
  17. 17.
    Rosenblatt F (1958) The perceptron: a probabilistic model, for information storage and organization in the brain. Psychol Rev 65(6):386–408CrossRefGoogle Scholar
  18. 18.
    Sakthivel KM, Rajitha CS (2017) A comparative study of zero-inflated, hurdle models with artificial neural network in claim count modeling. Int J Stat Syst 12(2):265–276Google Scholar
  19. 19.
    Wütrich M, Buser C (2017) Data analytics for non-life insurance pricing. In: Lectures notes.
  20. 20.
    Viaene S, Derrig RA, Baesens B, Dedene G (2002) A comparison of state-of-the-art classification techniques for expert automobile insurance claim fraud detection. J Risk Insur 69:373–421CrossRefGoogle Scholar

Copyright information

© EAJ Association 2018

Authors and Affiliations

  1. 1.ISBAUniversité Catholique de LouvainLouvain-la-NeuveBelgium

Personalised recommendations