A Fintech is a firm that has integrated technology into the offering of financial products including improvements to the use and delivery of those products. Many of the Fintechs are offering loans to small businesses, which have been otherwise under-served by the banking sector.

The challenge for banks is the lack of in-depth information on small businesses as well as the cost and time it takes to shepherd a small business loan through the underwriting process and obtain approval from a lending committee. For these reasons, banks would rather expend their resources on making larger loans. A good benchmark is SBA 7(a) loans, which average just under $500,000 each.

Enter the Fintechs, who use automation to facilitate the application process and underwriting of a variety of smaller loans, both consumer and business. Examples include online lenders BlueVine, Kabbage, and OnDeck, which all offer fast approvals on small business loans and lines of credit for as little as $5,000.

To accomplish this, the Fintechs rely on two things: Big Data and Machine Learning (ML). By being able to process huge amounts of data, and then learn from the decisions made, they are able to efficiently and economically process small loans. Since 2015, more than $1 billion has been invested by the Fintechs in Machine Learning solutions.

To take advantage of machine learning, which is capable of analyzing many variables simultaneously, a Fintech loan to a small business is handled by dividing the credit process into discreet variables for assessing risk -- essentially the same concept as credit scoring. For example, this can involve using automation to capture and analyze the last three month's bank statements, verify addresses, and access payment data. By reducing an unwieldy process into a collection of small tasks, these software solutions are able to deliver a decision, which has been compared to all previous decisions, in near real-time.

It's been predicted that "machine learning and automated features will become an industry standard for risk management in finance before 2030." Considering the pace of change in other sectors, that may be a conservative estimate.

The innovations being realized by Fintechs bear monitoring by business credit executives. There are those who will argue that it won't work here, reasoning that the business credit environment is not as homogenous - after all each Fintech product has a specific focus. In fact, many B2B companies have products and distribution channels that exhibit a high degree of homogeneity, which could be managed in an automated risk environment.

In most cases, there is no reason ML tools can't be applied to specific segments of your customer base, if not most of your portfolio, to generate actionable intelligence. Furthermore, we have already seen that AI-driven remittance processing solutions, which rely on ML, are able to deliver attractive returns on investment in environments with much lower numbers of remittances than traditional automated cash applications can effectively handle.

Change, driven by technology and automation, is coming to the business credit world whether you like it or not.

Transformative change can be scary, but your fears can be overcome. If you are willing to learn and adapt, embracing change can instead become a career enhancer.