Skip to main content
SpringerLink
Log in

Systems Simulation Analysis and Optimization of Insurance Business

  • Published:
Cybernetics and Systems Analysis Aims and scope

Abstract

Problems of computational actuarial mathematics, dynamic financial analysis, and optimization of insurance business and the possibility of their solution by means of parallel computing on graphics accelerators are discussed. The ruin probability and other performance criteria of an insurance company are estimated by the Monte Carlo method. In many cases, it is the only applicable method. Since the ruin probability is small enough, to achieve an acceptable estimate accuracy, an astronomical number of simulations may be required. Parallelization of the Monte Carlo method and the use of graphical accelerators allow us getting the desired result in a reasonable time. The results of numerical experiments on the developed system of actuarial modeling are presented, allowing the use of graphical accelerator that supports Nvidia CUDA 1.3 and higher.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. B. de Finetti, “Su un’impostazione alternativa della teoria collettiva del rischio,” in: Trans. 15th Intern. Congress of Actuaries, 2, New York (1957), pp. 433–443.

  2. H. Schmidli, Stochastic Control in Insurance, Springer-Verlag, London (2008).

    MATH  Google Scholar 

  3. N. L. Bowers, H. U. Gerber, J. C. Hickman, D. A. Jones, and C. J. Nesbitt, Actuarial Mathematics, Society of Actuaries, Itasca (1997).

    Google Scholar 

  4. V. Yu. Korolev, V. E. Bening, and S. Ya. Shorgin, Mathematical Fundamentals of Risk Theory [in Russian], Fizmatlit, Moscow (2011).

    Google Scholar 

  5. M. M. Leonenko, Yu. S. Mishura, Ya. M. Parkhomenko, and M. J. Yadrenko, Probability-Theoretical and Statistical Methods in Econometrics and Financial Mathematics [in Ukrainian], Informtekhnika, Kyiv (1995).

    Google Scholar 

  6. C. D. Daykin, T. Pentikainen, and M. Pesonen, Practical Risk Theory for Actuaries, Chapman and Hall, London – New York (1994).

    MATH  Google Scholar 

  7. J. L. Teugels and B. Sundt (eds.), Encyclopedia of Actuarial Science, Wiley, Chichester (2004).

    MATH  Google Scholar 

  8. R. Kaufmann, A. Gadmer, and R. Klett, “Introduction to dynamic financial analysis,” ASTIN Bull., 31, No. 1, 213–249 (2001).

    Article  Google Scholar 

  9. P. Blum and M. Dacorogna, “DFA — dynamic financial analysis,” in: J. L. Teugels and B. Sundt (eds.), Encyclopedia of Actuarial Science, Vol. 1, Wiley, Chichester (2004), pp. 3342–3355.

    Google Scholar 

  10. M. R. Hardy, “Dynamic financial modeling of an insurance enterprise,” in: J. L. Teugels and B. Sundt (eds.), Encyclopedia of Actuarial Science, Vol. 1, Wiley, Chichester (2004), pp. 3618–3628.

    Google Scholar 

  11. B. Norkin, “Parallel computations in insurance business optimization,” in: Proc. 1st Intern. Conf. on High Perform. Comput. (Oct. 12–14, 2011, Kyiv), Kyiv (2011), pp. 33–39.

  12. B. V. Norkin, “Paralleling of the methods of insurance company bankruptcy risk assessment,” in: Teoriya Optym. Rishen’ [in Ukrainian], V. M. Glushkov Inst. of Cybernetics NANU, Kyiv (2010), pp. 33–39.

    Google Scholar 

  13. B. V. Norkin, “Ruin probability of a controlled autoregression process,” in: Komp. Matematyka [in Ukrainian], V. M. Glushkov Inst. of Cybernetics NANU, Kyiv (2011), pp. 142–150.

    Google Scholar 

  14. B. V. Norkin, “Actuarial computing with the use of graphic processors,” in: Trans. Intern. Conf. on High Performance Computing (HPC-UA 2012, Kyiv, October, 10, 2012), NANU, Kyiv (2012), pp. 268–274.

    Google Scholar 

  15. B. V. Norkin, “On performing actuarial calculations on GPU,” in: Visn. NTUU “KPI,” Inform., Upravl. ta Obchysl. Tekhn., Zb. Nauk. Prats’, No. 56 (2012), pp. 113–119.

  16. A. V. Boreskov, A. A. Kharlamov, N. D. Markovskii et al., Parallel Computing on GPU. CUDA Architecture and Program Model [in Russian], Izd. Mosk. Univer., Moscow (2012).

    Google Scholar 

  17. B. V. Norkin, “Mathematical models for insurance optimization,” Cybern. Syst. Analysis, 47, No. 1, 117–133 (2011).

    Article  MathSciNet  Google Scholar 

  18. B. V. Norkin, “Insurance portfolio optimization,” Prikl. Statistika. Aktuar. ta Fin. Matem., No. 1–2, 197–203 (2011).

  19. G. B. Dantzig, “Need to do planning under uncertainty and the possibility of using parallel processors for this purpose,” Ekonom.-Mat. Obzor, 23, 121–135 (1987).

    MathSciNet  Google Scholar 

  20. G. B. Dantzig and P. W. Glynn, “Parallel processors for planning under uncertainty,” Ann. Oper. Res., 22, 1–21 (1990).

    Article  MATH  MathSciNet  Google Scholar 

  21. Wu. Haizhen, “Parallel computing using GPUs,” March 1, 2011, http://ecs.victoria.ac.nz/twiki/pub/EResearch/EcsTeslaResource/Parallel.Computing.Using.Graphics.Cards.pdf.

  22. Li J., Hu X., Zhanlong Pang Z., and Qian K., “A parallel ant colony optimization algorithm based on fine-grained model with gpu-acceleration,” Intern. J. Innov. Comput., Inform. and Control, 5, No. 11(A), 3707–3716 (2009).

    Google Scholar 

  23. E. A. Nurminskii and P. L. Pozdnyak, “Solving the problem of searching for the least distance to a polytope with the use of graphical accelerators,” Vychisl. Tekhnol., 16, Issue 5, 80–88 (2011).

    Google Scholar 

  24. Law of Ukraine “On insurance,” Vidom. Verkh. Rady Ukrainy, No. 18, p. 78 (1996), http://zakon1.rada.gov.ua/laws/show/85/96-Bp.

  25. A. I. Zaletov, Insurance in Ukraine [in Russian], Intern. Agency “BeeZone,” Kyiv (2002).

    Google Scholar 

  26. http://forinsurer.com/.

  27. A. Nakonechnyi, “Optimization of risk processes,” Cybern. Syst. Analysis, 32, No. 5, 641–646 (1996).

    Article  MATH  MathSciNet  Google Scholar 

  28. P. Cízek, W. Hardle, and R. Weron (eds.), Statistical Tools for Finance and Insurance, Springer, New York (2005).

    MATH  Google Scholar 

  29. H. R. Waters, “Some mathematical aspects of reinsurance,” Insurance: Math. Econ., No. 2, 17–26 (1983).

  30. IBM ILOG CPLEX V12.1. User’s manual for CPLEX, Intern. Business Machines Corp. (2009).

  31. W. Hoeffding, “Probability inequalities for sums of bounded random variables,” J. Amer. Statist. Assoc., 58, 13–30 (1963).

    Article  MATH  MathSciNet  Google Scholar 

  32. NVIDIA, CUDA CURAND Library (2010).

  33. Intel Digital Random Number Generator (DRNG): Software Implementation Guide, http://software.intel.com/en-us/articles/intel-digital-random-number-generator-drng-software-implementation-guide/.

Download references

Author information

Authors and Affiliations

  1. V. M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Kyiv, Ukraine

    B. V. Norkin

Authors
  1. B. V. Norkin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B. V. Norkin.

Additional information

The study was partially supported by the grant of the President of Ukraine for the support of scientific studies of young scientists, No. GP/F49/121.

Translated from Kibernetika i Sistemnyi Analiz, No. 2, March–April, 2014, pp. 112–125.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Norkin, B.V. Systems Simulation Analysis and Optimization of Insurance Business. Cybern Syst Anal 50, 260–270 (2014). https://doi.org/10.1007/s10559-014-9613-9

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10559-014-9613-9

Keywords

Search

Navigation