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The general dynamic risk assessment for the enterprise by the hologram approach in financial technology

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  • George Xianzhi Yuan

    (BBD Technology Co., Ltd. (BBD), 21F, Palm Springs Building, No. 199, Tianfu Avenue, Chengdu 610093, P. R. China†Guiyang Institute for Bigdata and Finance, Guizhou University of Finance and Economics, Guiyang 550025, P. R. China‡School of Financial Technology, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, P. R. China§Business School and Advanced Institute of Finance, Sun Yat-Sen University, Guangzhou 510275, P. R. China¶Center for Financial Engineering, Soochow University, Suzhou 215008, P. R. China)

  • Huiqi Wang

    (#x2225;College of Mathematics and Statistics, Chongqing University, Chongqing 401331, P. R. China)

Abstract

The purpose of this paper is to discuss the general risk assessment under the Hologram framework for the enterprise based on big data language; and to illustrate the Hologram as a new tool for establishing a mechanism to evaluate SMEs growth and change in financial technology dynamically (here we mainly focus on SMEs as they are one of the very important classes for enterprises with less information available from financial accounting report and associated assets. Indeed, the approach discussed here is applicable to general enterprises).The key idea of our new approach is to introduce and use the “Hologram” (similar to, “holographic portrait” used in portrait holography), a platform for data fusion dynamically, as a tool and mechanism to describe the dynamic evolution of SMEs based on their business dynamic behavior. Through processing structured and/or unstructured data in terms of “related-party” information sets which analyze (1) “investment” and (2) “management” information provided by SMEs’ business behavior, and extracting “Risk Genes” from complex financial network structures in the business ecosystem, we can establish a “good” or “bad” rating for SMEs by using data fusion dynamically and financial technology. This method to assess SMEs is a new approach to evaluating SMEs’ development dynamically based on the network structure information of enterprise and business behavior. The framework introduced in this paper for the dynamic mechanism of SMEs’ development and evolution allows us to assess the risk of any SMEs (in particular to evaluate SMEs’ loan applications) even not available for critical data required in traditional finance analysis including information such as financial accounting and associated assets, etc. This new “Hologram” approach for SMEs assessment is a pioneering innovation that incorporates big data and financial technology for inclusive financial services in practical application. Ultimately, the Hologram approach offers a new theoretical solution for the long-standing problem of credit risk assessment for SMEs and individuals in practice.Since the information embedded in SMEs’ business behavior reveals the competition and cooperation mechanism that drives its stochastic resonance (SR) behavior which is associated with successful SMEs development, the two concepts of SAI and URR under the Hologram approach to risk assessment that identifies if an SME is “good” are based on the network generated from an SMEs’ related-party information in terms of “investment” and “management” dynamically, along with other available information such as related investment capital and risk control. Significantly, the Hologram approach to risk assessment for SMEs does not require critical data of traditional financial account and related assets, etc. which heavily depend on financial accounting and associated assets used by financial risk analysis in practice. Using big data and FinTech Hologram method discussed in this paper utilizes the related-party information (in term of investment and management) of each SME which exists in an embedded business network to overcome the situation for SMEs which always have not or have not enough in providing accounting and associated asset information in the practice.By the feature of each Hologram for a given SME, one always has the related-party information in terms of either investment, or management dynamically, which is indeed also an explanation for the reason why the new approach proposed only comes true only until the era of big data’s occurring by using ideas from financial technology today.Furthermore, this paper explores the implementation of the “Holo Credit Loan”, a pure credit loan without any collateral and guarantee launched in 2016, as practical applications of the Hologram approach. We illustrate the framework of SMEs risk assessment under the Holograms new theoretical basis for solving the long-standing problem of credit risk assessment for SMEs (and individuals). Moreover, this paper’ conclusion will address the performance of the “Holo Credit Loan”.

Suggested Citation

  • George Xianzhi Yuan & Huiqi Wang, 2019. "The general dynamic risk assessment for the enterprise by the hologram approach in financial technology," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 1-48, March.
  • Handle: RePEc:wsi:ijfexx:v:06:y:2019:i:01:n:s2424786319500014
    DOI: 10.1142/S2424786319500014
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    References listed on IDEAS

    as
    1. Anderson, Raymond, 2007. "The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation," OUP Catalogue, Oxford University Press, number 9780199226405.
    2. Berger, Allen N & Frame, W Scott & Miller, Nathan H, 2005. "Credit Scoring and the Availability, Price, and Risk of Small Business Credit," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(2), pages 191-222, April.
    3. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    4. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    5. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    6. Amir Bashan & Ronny P. Bartsch & Jan. W. Kantelhardt & Shlomo Havlin & Plamen Ch. Ivanov, 2012. "Network physiology reveals relations between network topology and physiological function," Nature Communications, Nature, vol. 3(1), pages 1-9, January.
    7. Sergey V. Buldyrev & Roni Parshani & Gerald Paul & H. Eugene Stanley & Shlomo Havlin, 2010. "Catastrophic cascade of failures in interdependent networks," Nature, Nature, vol. 464(7291), pages 1025-1028, April.
    8. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    9. Andrew G. Haldane & Robert M. May, 2011. "Systemic risk in banking ecosystems," Nature, Nature, vol. 469(7330), pages 351-355, January.
    10. Thorsten Beck & Asli Demirgüç-Kunt & María Pería, 2011. "Bank Financing for SMEs: Evidence Across Countries and Bank Ownership Types," Journal of Financial Services Research, Springer;Western Finance Association, vol. 39(1), pages 35-54, April.
    11. Frame, W Scott & Srinivasan, Aruna & Woosley, Lynn, 2001. "The Effect of Credit Scoring on Small-Business Lending," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 33(3), pages 813-825, August.
    12. Alan Lucas, 2001. "Statistical challenges in credit card issuing," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 17(1), pages 83-92, January.
    13. Scott Shane & Toby Stuart, 2002. "Organizational Endowments and the Performance of University Start-ups," Management Science, INFORMS, vol. 48(1), pages 154-170, January.
    14. Alessandro Vespignani, 2010. "The fragility of interdependency," Nature, Nature, vol. 464(7291), pages 984-985, April.
    15. Beck, T.H.L. & Demirgüc-Kunt, A. & Martinez Peria, M., 2011. "Banking financing for SME's : Evidence across countries and bank ownership types," Other publications TiSEM c4d491c9-2cc6-40f6-ad55-a, Tilburg University, School of Economics and Management.
    16. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    17. Carlota Perez, 2002. "Technological Revolutions and Financial Capital," Books, Edward Elgar Publishing, number 2640.
    18. Allen Berger & Adrian Cowan & W. Frame, 2011. "The Surprising Use of Credit Scoring in Small Business Lending by Community Banks and the Attendant Effects on Credit Availability, Risk, and Profitability," Journal of Financial Services Research, Springer;Western Finance Association, vol. 39(1), pages 1-17, April.
    19. W. Scott Frame & Michael Padhi & Lynn W. Woosley, 2001. "The effect of credit scoring on small business lending in low- and moderate-income areas," FRB Atlanta Working Paper 2001-6, Federal Reserve Bank of Atlanta.
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    Cited by:

    1. Wang, Huiqi & Yuan, Wei & Yuan, George, 2022. "The mechanism for SMEs growth by applying stochastic dynamical approach," Finance Research Letters, Elsevier, vol. 48(C).
    2. Sun, Yunchuan & Zeng, Xiaoping & Zhao, Han & Simkins, Betty & Cui, Xuegang, 2022. "The impact of COVID-19 on SMEs in China: Textual analysis and empirical evidence," Finance Research Letters, Elsevier, vol. 45(C).

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