FinTech and Commercial Finance
FinTech has made inroads into many aspects of finance, especially in the SMB sector in payments and unsecured loans. Expect further encroachment as machine learning and AI continue to advance. When the decision-making is based on credit scores and similar available information, it is relatively easy to establish algorithms to automate these credit decisions. FinTech companies will continue their growth in the SMB space especially with banks continuing to pull away from this segment.
Some lending products like secured lending would seem less likely to be disrupted by FinTech. FinTech is coming, and sooner rather than later. Here are some examples:
· Business Development
When a company can present their credit needs on an online marketplace accompanied by key financial ratios, collateral and qualitative info, then deals can automatically be routed to finance companies that are most likely to complete the deal, This could make matching a prospective borrower to a lender more efficient. Think LendingTree but for commercial finance.
· Data Processing
There are APIs that pull from Accounting Systems, Banking Systems (i.e. Plaid) and websites (for financial and non-financial data). This coupled with AI and RPA will facilitate analysis and the generation of reports. This information pull and workflow process could further streamline the underwriting / audit / loan management process.
AI has already made inroads into journalism (click: The Impact of AI on Journalism (forbes.com); it is not a stretch to think the same could be coming to finance. Many aspects of a Confidential Information Memorandum are rote like tables and can be automatically populated.
· Borrowing Base Calculation:
It is likely that traditional appraisals and audits will be with us for some time, but the processes with each will continue to be streamlined with technology.
o Accounts Receivable (“AR”): An AR aging can be evaluated quickly to establish an estimated collectability. Traditional ineligibles like past-due and cross-aged can be automatically calculated. Credit scores and other metrics can be pulled in to estimate collectability on other accounts. This ability to arrive “intelligently” at an estimated recovery on AR could streamline this aspect of underwriting and loan management.
o Inventory: Inventory is comprised of SKUs that are at varying stages of production. The estimated recovery on raw materials and finished goods can be easily calculated by the recent prices for these “end” products both from current Company sales as well as external marketplaces (i.e. Amazon, Alibaba).
o Machinery & Equipment: If equipment can be discretely identified, then these pieces should be able to be matched against an equipment database. This assumes that the recovery information is readily available in a real-time database, which may not be the case right now.
o Real Estate: Real estate valuation is often based on comparables. The more information that can be matched against a real-time database, the more the valuation will be automated and based on real-time data.
FinTech will continue to advance in commercial finance; expect a more efficient marketplace as a result.
