Smart Precision Finance for Small Businesses Funding
David C. Donald - The Chinese University of Hong Kong
-- My recent paper, "Smart Precision Finance for Small Businesses Funding," published in the European Business Organizations Law Review and available in read only copy at this link, presents the essential concept of a smart precision finance mechanism for use in small business funding. This concept requires three main components to be operational: data useful for the lender but owned by the borrower, full or partial automation of funding injections, and thoughtful structuring of the loan contracts.
First, the smart precision finance mechanism would employ digitalized data to determine the creditworthiness of a loan applicant. Use of digital data, and in particular data other than traditional credit scores, is already key to existing fintech business models in the small business finance sector. This, in particular, includes bank account, invoicing and payment data. Because providers of banking, payment and general support services (like those of an Amazon) process such data for small businesses, their existing network data has given them a competitive edge in small business lending. The tendency of network economics will in all probability converge market development in a way that small businesses are captured within the data fiefdom of one large service provider or another, which could create serious market entry barriers for both small businesses outside those fiefdoms and competing lenders, thus giving a handful of super-lenders de facto control of both fund flows and the data necessary for selecting young firms.
The smart precision finance concept would, by contrast, include a reliable, self-owned instrument for sourcing such data, so that each small business could control it, generate data at will, and move to connect to any given lender. Such source can be found in ERP systems, which are software arrangements that channel within a firm and in relation to its customers and service providers the kind of information that a lender would find useful in assessing creditworthiness. The ERP data thresholds found to signal creditworthiness could be calibrated against environmental data about the applicant, its market and the likely cash buffer position it would achieve compared to firms active in other sectors of the economy.
The primary challenge presented by such use of ERP data is the possibility of ‘data management’ loan fraud by a borrower. In developing the smart precision finance model, options within existing ERP software should be sought to sufficiently bond data. If such function does not currently exist, software developers should be encouraged to create it. Third party lenders must be confident that the information they are shown by borrowers is complete and accurate, and such confidence would lower the borrowers’ cost of capital.] Introduction of distributed ledger technology could be considered in this regard.
The second component of the precision finance concept is the ‘smart’ trigger to release funds or to advise doing the same via a decision support system. This automated element will lower ongoing transaction costs for the lender and make smaller loans worth pursuing by decreasing their administrative burden. Financing with this structure will also be more attractive for the borrowers, as it will facilitate and accelerate access to capital. The smart trigger would essentially be a data linkage between the ERP system and the operational system of the lender. The interface would be coded to reflect key provisions in the financing agreement, and would not need to be significantly more complex than a garage door opener—with a given configuration of data meeting the needs of the lender’s mechanism to open the secure funding portal and allow a given amount of cash to exit. Although I refer to this as ‘smart’, use of a DLT does not seem necessary, as the data and funding linkage between lender and borrower would be private and proprietary and would not require the kind of heavy cryptography protection provided by DLT—unless this were to be incorporated on the ERP side to prevent loan fraud through alteration of data, as mentioned above. Automatic release of funds could be replaced by an automatic message to a loan officer suggesting release. Such decision support system arrangement can also be specifically designed to forestall or overcome biases that have been detected or are reasonably to be expected by management in a given lending organization. The automated recommendation could be specifically calibrated to take the skewing of data under persisting bias into account.
The third component of the smart precision finance mechanism is to legally arrange the shape and release of funds both advantageously for the enforcement of the lender’s claim and—for those firms having such potential—to facilitate transitioning into the type of funding that might be more useful at a later stage in a fast-growing firm’s life. The precision earmarking of funds in the proposed concept can be structured to provide Quistclose resulting trust protection of the funds under UK and Hong Kong law. With regard to future development, each loan or even individual injection of cash under a given framework loan could be memorialized in a negotiable instrument that could be transferred at a later time or even converted into equity under certain conditions at a future date. If in this way early funding is conceived with an eye toward taking the small business through its various stages of development toward venture capital investment, a firm could move through the financial ecosystem with fewer transitional shocks.
The concept presented in the paper is essentially complete, but implementation will require detailed analysis of ERP software options (including anti-fraud safeguards), the specification of parameters for entrance into the lending relationship and for the release of individual fund injections, as well as a thorough analysis of the Quistclose-type resulting trust in each jurisdiction where the smart precision financing mechanism is to be implemented. These are my ongoing areas of research for the project.