IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i10p1709-d817203.html
   My bibliography  Save this article

A Coupled Mathematical Model of the Dissemination Route of Short-Term Fund-Raising Fraud

Author

Listed:
  • Shan Yang

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Kaijun Su

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Bing Wang

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Zitong Xu

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

Abstract

To effectively protect citizens’ property from the infringement of fund-raising fraud, it is necessary to investigate the dissemination, identification, and causation of fund-raising fraud. In this study, the Susceptible Infected Recovered (SIR) model, Back-Propagation (BP) neural network, Fault tree, and Bayesian network were used to analyze the dissemination, identification, and causation of fund-raising fraud. Firstly, relevant data about fund-raising fraud were collected from residents in the same area via a questionnaire survey. Secondly, the SIR model was used to simulate the dissemination of victims, susceptibles, alerts, and fraud amount; the BP neural network was used to identify the data of financial fraud and change the accuracy of the number analysis of neurons and hidden layers; the fault-tree model and the Bayesian network model were employed to analyze the causation and importance of basic events. Finally, the security measures of fund-raising fraud were simulated by changing the dissemination parameters. The results show that (1) for the spread of the scam, the scale of the victims expands sharply with the increase of the fraud cycle, and the victims of the final fraud cycle account for 12.5% of people in the region; (2) for the source of infection of the scam, the initial recognition rate of fraud by the BP neural network varies from 90.9% to 93.9%; (3) for the victims of the scam, reducing fraud publicity, improving risk awareness, and strengthening fraud supervision can effectively reduce the probability of fraud; and (4) reducing the fraud rate can reduce the number of victims and delay the outbreak time. Improving the alert rate can reduce victims on a large scale. Strengthening supervision can restrict the scale of victims and prolong the duration of fraud.

Suggested Citation

  • Shan Yang & Kaijun Su & Bing Wang & Zitong Xu, 2022. "A Coupled Mathematical Model of the Dissemination Route of Short-Term Fund-Raising Fraud," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1709-:d:817203
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/10/1709/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/10/1709/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ma, Xiaoxue & Deng, Wanyi & Qiao, Weiliang & Lan, He, 2022. "A methodology to quantify the risk propagation of hazardous events for ship grounding accidents based on directed CN," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Bhattacharya, Utpal, 2003. "The optimal design of Ponzi schemes in finite economies," Journal of Financial Intermediation, Elsevier, vol. 12(1), pages 2-24, January.
    3. Huo, Liang'an & Wang, Li & Song, Naixiang & Ma, Chenyang & He, Bing, 2017. "Rumor spreading model considering the activity of spreaders in the homogeneous network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 855-865.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhiyong Zhou & Jianhui Huang & Yao Lu & Hongcai Ma & Wenwen Li & Jianhong Chen, 2022. "A New Text-Mining–Bayesian Network Approach for Identifying Chemical Safety Risk Factors," Mathematics, MDPI, vol. 10(24), pages 1-25, December.
    2. Hongyan Dui & Jiaying Song & Yun-an Zhang, 2023. "Reliability and Service Life Analysis of Airbag Systems," Mathematics, MDPI, vol. 11(2), pages 1-13, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Xiongding & Li, Tao & Xu, Hao & Liu, Wenjin, 2019. "Spreading dynamics of an online social information model on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 497-510.
    2. Xu, Jinghong & Du, Zhitao & Guo, Jianchao & Fu, Xiangling & Zhang, Yuqiang & Wu, Ye, 2018. "Empirical and modeling studies of WeChat information dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1113-1120.
    3. Klarita Sadiraj & Arthur Schram, 2018. "Inside information in Ponzi schemes," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 4(1), pages 29-45, July.
    4. Liang’an Huo & Fan Ding & Chen Liu & Yingying Cheng, 2018. "Dynamical Analysis of Rumor Spreading Model considering Node Activity in Complex Networks," Complexity, Hindawi, vol. 2018, pages 1-10, November.
    5. Zhang, Jing & Wang, Xiaoli & Xie, Yanxi & Wang, Meihua, 2022. "Research on multi-topic network public opinion propagation model with time delay in emergencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    6. Mário Cunha & Hélder Valente & Paulo B. Vasconcelos, 2013. "Ponzi schemes: computer simulation," OBEGEF Working Papers 023, OBEGEF - Observatório de Economia e Gestão de Fraude;OBEGEF Working Papers on Fraud and Corruption.
    7. Artzrouni, Marc, 2009. "The mathematics of Ponzi schemes," Mathematical Social Sciences, Elsevier, vol. 58(2), pages 190-201, September.
    8. Giannetti, Mariassunta, 2007. "Financial liberalization and banking crises: The role of capital inflows and lack of transparency," Journal of Financial Intermediation, Elsevier, vol. 16(1), pages 32-63, January.
    9. Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Ma, Laihao, 2022. "On the causation of seafarers’ unsafe acts using grounded theory and association rule," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    10. Feng, Jian Rui & Zhao, Meng-ke & Lu, Shou-xiang, 2024. "Accident spread and risk propagation mechanism in complex industrial system network," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    11. Linhe Zhu & Hongyong Zhao, 2017. "Dynamical behaviours and control measures of rumour-spreading model with consideration of network topology," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(10), pages 2064-2078, July.
    12. Feng, Xinhang & Jiang, Yanli & Gai, Wenmei, 2024. "Rural community response to accidental toxic gas release: An individual emergency response model during self-organized evacuations," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    13. Sezer, Sukru Ilke & Camliyurt, Gokhan & Aydin, Muhmmet & Akyuz, Emre & Gardoni, Paolo, 2023. "A bow-tie extended D-S evidence-HEART modelling for risk analysis of cargo tank cracks on oil/chemical tanker," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    14. Gande, Amar & John, Kose & Senbet, Lemma W., 2008. "Bank incentives, economic specialization, and financial crises in emerging economies," Journal of International Money and Finance, Elsevier, vol. 27(5), pages 707-732, September.
    15. Huo, Liang’an & Wang, Li & Zhao, Xiaomin, 2019. "Stability analysis and optimal control of a rumor spreading model with media report," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 551-562.
    16. Huo, Liang’an & Cheng, Yingying & Liu, Chen & Ding, Fan, 2018. "Dynamic analysis of rumor spreading model for considering active network nodes and nonlinear spreading rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 24-35.
    17. Chen, Shanshan & Jiang, Haijun & Li, Liang & Li, Jiarong, 2020. "Dynamical behaviors and optimal control of rumor propagation model with saturation incidence on heterogeneous networks," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    18. Zhang, Yuhuai & Zhu, Jianjun, 2018. "Stability analysis of I2S2R rumor spreading model in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 862-881.
    19. Li, Jiarong & Jiang, Haijun & Yu, Zhiyong & Hu, Cheng, 2019. "Dynamical analysis of rumor spreading model in homogeneous complex networks," Applied Mathematics and Computation, Elsevier, vol. 359(C), pages 374-385.
    20. DUGULEANĂ Andreea Raluca & TĂNĂSESCU Cristina Roxana & DUGULEANĂ Mihai, 2024. "A New Approach To Create Sustained Ponzi-Like Systems By Incorporating Perpetual Liquidity And Time Epochs," Management of Sustainable Development, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 16(1), pages 37-44, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1709-:d:817203. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.