Skip to main content
SpringerLink
Log in

Risk decision analysis of commercial insurance based on neural network algorithm

  • S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021)
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

To improve the effect of commercial insurance risk decision, this paper applies neural network algorithms to commercial insurance risk decision under the guidance of machine learning ideas, and selects the neural network algorithm based on the actual situation. Moreover, this paper analyzes the nature of risks of commercial insurance, analyzes the types of risks and risk relevance, constructs a commercial insurance risk decision model based on neural network algorithms, and determines the system process. In addition, this paper uses a combination method of qualitative and quantitative to identify the influencing factors of risk estimation to obtain relevant influencing factors, and verify the model proposed in this paper in combination with experimental research. From the experimental research results, it can be seen that the commercial insurance risk decision system based on neural network algorithm is very good in terms of decision effect.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Acemoglu D, Ozdaglar A, Tahbaz-Salehi A (2015) Systemic risk and stability in financial networks. Am Econ Rev 105(2):564–608

    Article  Google Scholar 

  2. Allen P, Petsoulas C (2016) Pricing in the English NHS quasi market: a national study of the allocation of financial risk through contracts. Public Money Manage 36(5):341–348

    Article  Google Scholar 

  3. Bartram SM, Brown GW, Waller W (2015) How important is financial risk? J Financ Quant Anal 50(4):801–824

    Article  Google Scholar 

  4. Castellano R, Cerqueti R, Rotundo G (2020) Exploring the financial risk of a temperature index: a fractional integrated approach. Ann Oper Res 284(1):225–242

    Article  MathSciNet  MATH  Google Scholar 

  5. Chan EY (2015) Physically-attractive males increase men’s financial risk-taking. Evol Hum Behav 36(5):407–413

    Article  Google Scholar 

  6. Cohn A, Engelmann J, Fehr E et al (2015) Evidence for countercyclical risk aversion: an experiment with financial professionals. Am Econ Rev 105(2):860–885

    Article  Google Scholar 

  7. Cole ES, Walker D, Mora A et al (2014) Identifying hospitals that may be at most financial risk from Medicaid disproportionate-share hospital payment cuts. Health Aff 33(11):2025–2033

    Article  Google Scholar 

  8. Cooper WW, Kingyens AT, Paradi JC (2014) Two-stage financial risk tolerance assessment using data envelopment analysis. Eur J Oper Res 233(1):273–280

    Article  MathSciNet  MATH  Google Scholar 

  9. Cueva C, Roberts RE, Spencer T et al (2015) Cortisol and testosterone increase financial risk taking and may destabilize markets. Sci Rep 5(1):1–16

    Article  Google Scholar 

  10. Lee CJ, Andrade EB (2015) Fear, excitement, and financial risk-taking. Cogn Emot 29(1):178–187

    Article  Google Scholar 

  11. Linarelli J (2017) Luck, justice and systemic financial risk. J Appl Philos 34(3):331–352

    Article  Google Scholar 

  12. Lucarelli C, Uberti P, Brighetti G (2015) Misclassifications in financial risk tolerance. J Risk Res 18(4):467–482

    Article  Google Scholar 

  13. Magendans J, Gutteling JM, Zebel S (2017) Psychological determinants of financial buffer saving: the influence of financial risk tolerance and regulatory focus. J Risk Res 20(8):1076–1093

    Article  Google Scholar 

  14. Maggiori M (2017) Financial intermediation, international risk sharing, and reserve currencies. Am Econ Rev 107(10):3038–3071

    Article  Google Scholar 

  15. Montford W, Goldsmith RE (2016) How gender and financial self-efficacy influence investment risk taking. Int J Consum Stud 40(1):101–106

    Article  Google Scholar 

  16. Pinelis I (2014) An optimal three-way stable and monotonic spectrum of bounds on quantiles: a spectrum of coherent measures of financial risk and economic inequality. Risks 2(3):349–392

    Article  Google Scholar 

  17. Sosnovska O, Zhytar M (2018) Financial architecture as the base of the financial safety of the enterprise. Baltic J Econ Stud 4(4):334–340

    Article  Google Scholar 

  18. Su J, Furman E (2017) A form of multivariate Pareto distribution with applications to financial risk measurement. ASTIN Bull: J IAA 47(1):331–357

    Article  MathSciNet  MATH  Google Scholar 

  19. Tanimura T, Jaramillo E, Weil D et al (2014) Financial burden for tuberculosis patients in low-and middle-income countries: a systematic review. Eur Respir J 43(6):1763–1775

    Article  Google Scholar 

  20. Thomä J, Chenet H (2017) Transition risks and market failure: a theoretical discourse on why financial models and economic agents may misprice risk related to the transition to a low-carbon economy. J Sustain Finance Invest 7(1):82–98

    Article  Google Scholar 

  21. Wang GWY, Woo SH, Mileski J (2014) The relative efficiency and financial risk assessment of shipping companies. Marit Policy Manag 41(7):651–666

    Article  Google Scholar 

  22. Wanjohi SM, Wanjohi JG, Ndambiri JM (2017) The effect of financial risk management on the financial performance of commercial banks in Kenya. Int J Finance Bank Res 3(5):70–81

    Article  Google Scholar 

  23. West T, Worthington AC (2014) Macroeconomic conditions and Australian financial risk attitudes, 2001–2010. J Fam Econ Issues 35(2):263–277

    Article  Google Scholar 

  24. Zalik A (2015) Resource sterilization: reserve replacement, financial risk, and environmental review in Canada’s tar sands. Environ Plan A 47(12):2446–2464

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Insurance and Economics, University of International Business and Economics, Beijing, 100029, China

    Shanshan Wang

  2. Postdoctoral Station of China Construction Bank, Postdoctoral Mobile Station of Beijing, Beijing, 100032, China

    Zhenwang Zhao

Authors
  1. Shanshan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenwang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shanshan Wang.

Ethics declarations

Conflict of interest

The authors have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, S., Zhao, Z. Risk decision analysis of commercial insurance based on neural network algorithm. Neural Comput & Applic 35, 2169–2182 (2023). https://doi.org/10.1007/s00521-022-07199-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07199-0

Keywords

Search

Navigation