The Prediction of Continuity of Basic Endowment Insurance Fund Based on Markov Chain and Actuarial

  • Guofeng Liu
  • Shaobin Huang
  • Tianyang Lv
  • Yuan Cheng
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 126)


Basic endowment insurance is an important part of social insurance. At present, the researches on the continuity of basic endowment insurance fund are mainly actuarial methods which come from commercial insurance. To handle the uncertainty in social insurance, the paper proposes an analysis mechanism which adopts massive real data and founds on actuarial model and various forecasting methods. Firstly, after analysing the data and the characteristics of basic endowment insurance of China, the paper establishes the actuarial model of fund balance and uses various forecasting methods to predict the influencing factors of fund. Secondly, the Markov chain is used to forecast the number of the attendees that play different roles as paying and receiving. Finally, actuarial model and forecasting methods are combined to forecast the continuity of the endowment insurance fund. The experimental result shows the performance of our method is effective and feasible.


Insurance Fund Forecast Method State Transition Probability Insured Person Actuarial Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Yu, H., Zhong, H.: On the Sustainable Operation of China’s Basic Endowment Insurance System. Journal of Finance and Economics 9, 26–35 (2009)Google Scholar
  2. 2.
    Zhang, S.: Principles and Application of Social Security Actuarial. People’s Publishing House (2006)Google Scholar
  3. 3.
    Gebers, M.A., Peck, R.C.: Using traffic conviction correlates to identify high accident-risk drivers. Accident Analysis and Prevention 6, 903–912 (2003)CrossRefGoogle Scholar
  4. 4.
    Ma, X., Du, J., Dong, S.: Model of Spare Part Failure Rate Based on Linear Regression. Computer Simulation 11, 6–8 (2003)Google Scholar
  5. 5.
    Zhang, B., Zhang, J.: Application of Random Process. Tsinghua University Press (2004)Google Scholar
  6. 6.
    Liu, S., Dang, Y., Fang, Z.: Application of Grey System Theory. Science Press (2005)Google Scholar
  7. 7.
    Zhang, D., Jiang, S., Shi, K.: Theoretical Defect of Grey Prediction Formula and Its Improvement. Systems Engineering Theory and Practice 8, 1–3 (2002)Google Scholar
  8. 8.
    Li, J., Dai, W.: A New Approach od Background Value-Building and Its Application Based on Data Interpolation and Newton-Cores Formula. Systems Engineering Theory and Practice 10, 122–126 (2002)Google Scholar
  9. 9.
    Wang, X., Liu, X., Dai, F.: Improvement and Application of BP Neural Network Forecasting Algorithm. Computer Technology and Development 19, 64–67 (2009)Google Scholar
  10. 10.
    Li, X., Xu, J., Wang, Y.: The Establishment of Self-adapting Algorithm of BP Neural Network and Its Application. Systems Engineering Theory and Practice 5, 1–8 (2004)Google Scholar
  11. 11.
    Lv, T., Qiu, Y., Huang, S., Pang, Q.: State Prediction of Policy-holder of Basic Endowment Insurance for urban employees Based on Markov chain. In: The Conference on Web Based Business Management, vol. 2, pp. 790–796 (2010)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Guofeng Liu
    • 1
  • Shaobin Huang
    • 1
  • Tianyang Lv
    • 1
  • Yuan Cheng
    • 1
  1. 1.College of Computer Science and TechnologyHarbin Engineering UniversityHarbinChina

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