Skip to main content
SpringerLink
Log in

Smart Precision Finance for Small Businesses Funding

  • Article
  • Published:
European Business Organization Law Review Aims and scope Submit manuscript

Abstract

Small businesses are at the heart of most economies. Yet their combination of high risk and opacity with uncertain return dissuades banks and other investors from providing the necessary financial backing to get a business off the ground. Overcoming the significant asymmetries of information is simply not seen as worth the high transaction costs required. Technology—particularly digitalization and data analytics—has in recent years lowered the transaction costs of small business lending through automated systems capable of analyzing varied data sources indicative of the borrower’s solvency and stability. Ownership of this data by large service providers has, in this new market, tended to indicate a potential capture of small business borrowers by the lending firms with the largest networks. This paper sketches the parameters of an application for the ‘smart precision financing’ of small businesses, through an analysis of the legal and technological innovations that could be introduced to make such financing simpler while allowing the market to retain dynamism, thus increasing funding and funding options available to small businesses. The precision financing concept seeks to decrease information asymmetry and transaction costs while also limiting agency risk of a borrower misapplying funds. The recommendations offered are also designed to be complementary to eventual business growth toward venture capital investment.

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

Notes

  1. For the example of the US, see Mills (2018), p 17.

  2. These firms appear in the successful investments of venture capitalists, documented in Nicholas (2019), chapter 8.

  3. This has been documented in the US by Federal Reserve Banks (2019), pp 10–11 and globally by ADB-OECD (2014), pp 14–15.

  4. Mills (2018), pp 45, 71–72.

  5. Jensen and Meckling (1976).

  6. Mills (2018), pp 72–78.

  7. Mills (2018), p 84.

  8. Mills (2018), p 77.

  9. Bongiovanni and Lowenberg-Deboer (2004).

  10. Cliff, Brown and Treleaven (2010), pp 11–13.

  11. Twinsectra Limited v. Yardley and Others [2002] UKHL 12.

  12. Examples are the US Small Business Administration (see https://www.sba.gov/), the EU Executive Agency for SMEs (see https://ec.europa.eu/easme/en) and the Hong Kong initiative, startmeup.hk (see https://www.startmeup.hk/about-us/).

  13. Federal Reserve Banks (2019), p iv. The definition used by the Federal Reserve Banks is a business that employs between one and 499 persons.

  14. European Commission (2019). The definition currently used by the European Commission is a business that employs between one and 249 persons.

  15. Farrell and Wheat (2016), p 9.

  16. European Commission (2019).

  17. US Small Business Administration Office of Advocacy (2019).

  18. Eurostat (2019) (see https://ec.europa.eu/eurostat/web/products-eurostat-news/-/WDN-20180627-1).

  19. Farrell and Wheat (2016), p 14.

  20. Federal Reserve Banks (2019), pp 8–11.

  21. As Mills notes, ‘Even as late as 2017, small business loan assets held at U.S. banks had not reached pre-recession levels. In fact, by 2017, the share of small business loans as a percentage of all business loans at banks had dropped to about 20%, down from over 30% before the crisis’. Mills (2018), p 40.

  22. Fuscaldo (2019).

  23. Rice and Swesnik (2012), pp 15–16.

  24. Schneider and Schütte (2007), p 7.

  25. Dai Tian (2017), also see http://www.yongqianbao.com.

  26. Federal Reserve Banks (2019), p 17.

  27. Raj (2019).

  28. Mills (2018), pp 77–78.

  29. McAfee and Brynjolfsson (2017), pp 140, 200.

  30. Baums (1992).

  31. Ghosh (2012), p 81.

  32. ‘Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting nonobvious and useful patterns from large data sets […] the terms data science, machine learning, and data mining are often used interchangeably.’ Kelleher (2018), p 1 (emphasis in original).

  33. Mills (2018), p 87.

  34. See https://www.paypal.com/workingcapital/.

  35. Kabbage (2019).

  36. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) [2016] OJ L 119/1, Arts. 6-7.

  37. Katz and Shapiro (1994), p 106.

  38. McAfee and Brynjolfsson (2017), p 265.

  39. Federal Deposit Insurance Corporation (2018), pp 5–6.

  40. PYMNTS (2019).

  41. Verhage (2019).

  42. Elbardan and Kholeif (2017), p 1.

  43. Mills (2018), pp 96–98.

  44. The SAP corporate website offers ERP functions covering customer relations management, network and spend management, digital supply chain, human resources and people management, database and data management, analytics—including enterprise planning and predictive analytics—and customized application development with machine learning and internet of things. See https://www.sap.com/uk/products.html.

  45. This is the offering for the package that in the UK costs £27 monthly. See https://quickbooks.intuit.com/uk/accounting-software/.

  46. The system could also be brought into a distributed ledger format to preserve older entries and thus forestall falsification, even if the borrower has full control over modifying data entered on the ledger.

  47. Lee (2018), pp 155–157.

  48. Federal Deposit Insurance Corporation (2018), p 11.

  49. Lee (2018), p 155.

  50. Case (2019).

  51. Rauchs et al. (2018), p 37.

  52. Farrell and Wheat (2016), p 7.

  53. These were Construction, Health Care Services, High-Tech Manufacturing, High-Tech Services, Metal and Machinery, Other Professional Services, Personal Services, Real Estate, Repair and Maintenance, Restaurants, Retail, and Wholesalers. Farrell and Wheat (2016), p 26.

  54. Farrell and Wheat (2016), pp 5–6.

  55. The Federal Reserve Banks when questioning about 6000 small businesses in 2018, found that the leading challengers borrowers found with lenders were the difficultly of the application process and the long wait for the credit decision. Federal Reserve Banks (2019), p 20.

  56. Bratton (2006), p 47.

  57. Bratton (2006), pp 49–50.

  58. Yang et al. (2018), p 3.

  59. Yang et al. (2018), p 3.

  60. Donald and Donald (2020).

  61. The automatic ‘civility check’ performed by an algorithm to screen for foul or overly strong language in angry emails before sending, proposed by Thaler and Sunstein, provides a relatively accurate image of the function suggested here. See Thaler and Sunstein (2008), p 237.

  62. Myers (1984), pp 584–585.

  63. The inability to use borrowed funds to finance new projects unknown and not discussed at the time of lending may seem overly restrictive to growth and agile development, but this could be corrected by including some sort of mutually acceptable definition of a funded item in the original contract.

  64. Federal Reserve Banks (2019), p 7.

  65. Ghosh (2012), p 230.

  66. This is referred to as a Quistclose trust, as it was first devised in the case of Quistclose Investments v. Barclays Bank plc [1970] AC 567 (HL).

  67. Twinsectra Limited v. Yardley and Others [2002] UKHL 12, paras. 68–69.

  68. Twinsectra, para. 100.

  69. In Twinsectra, the debtor, a solicitor for a purchaser of real property, agreed that (1) ‘The loan monies will be retained by us until such time as they are applied in the acquisition of property on behalf of our client’, and (2) ‘The loan monies will be utilised solely for the acquisition of property on behalf of our client and for no other purpose.’ (Twinsectra, para. 58).

  70. Bratton (2006), p 50.

  71. Re McKeown [1974] NILR 226. Discussed in Worthington (1996), p 65.

  72. Worthington (1996), pp 64–65.

  73. Nicholas (2019), p 211.

  74. Kupor (2019), p 10.

  75. Akerlof (1970), p 500.

  76. Donald and Miraz (2019), pp 121–127.

References

Download references

Acknowledgements

This paper was completed with the generous support of the National University of Singapore Centre for Business and Finance Law and a Direct Grant from The Chinese University of Hong Kong. I would like to thank Emilios Avgouleas, Andreas Cahn, Lin Lin, Suresh Khilani, Joseph McCahery and Hans Tjio for comments on earlier versions of this paper. All errors remain my own.

Author information

Authors and Affiliations

  1. Faculty of Law, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, People’s Republic of China

    David C. Donald

  2. Centre for Financial Regulation and Economic Development, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, People’s Republic of China

    David C. Donald

Authors
  1. David C. Donald
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David C. Donald.

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

Donald, D.C. Smart Precision Finance for Small Businesses Funding. Eur Bus Org Law Rev 21, 199–217 (2020). https://doi.org/10.1007/s40804-020-00180-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40804-020-00180-1

Keywords

Search

Navigation