How AI Is transforming commercial and specialty lending

Dave Kucera
Head of Financial Institutions Group at Capital One
 

Technology is transforming the financial services industry in ways that would have been unimaginable just a few years ago—a trend originally confirmed by Capital One’s 2019 survey of the asset-backed securities (ABS) professionals at SFIG Vegas.

Ninety percent of the respondents declared that adopting new technologies is a critical component of their preparation for the next economic cycle.

The single most important of those new technologies, identified by 35 percent of the respondents as the primary driver of change, was artificial intelligence (AI).

Although artificial intelligence is not new, it increasingly has the power to have a transformative impact, thanks to a confluence of developments. The first is AI’s growing sophistication and power.

The emergence of AI as a practical tool has also been fed by dramatic advances in the computing power needed to run complex algorithms, by greater access to this power through cloud services, and by the ready availability of vast amounts of data that AI requires to perceive patterns and make predictions.

With an increased ability to capture, digitize, and aggregate information from sources such as public records, payment flows, and operational data, organizations now have access to the volume and quality of information needed to advance even the most complex AI use cases.

Today, AI is already being used to diagnose diseases, translate languages, and guide autonomous vehicles—and many of the algorithms behind these tools are being applied to the financial sector.

Lenders are already using them to automate and streamline documentation, identify new markets, and even assist in managing capital markets activities.

Artificial intelligence is no longer viewed as a distant innovation, but as an active force shaping how lenders approach decision making, compliance, and customer engagement. As technology continues to advance, its applications in commercial and speciality lending have become both broader and more targeted. 

From automating documentation to enhancing credit evaluations, AI is helping lenders streamline operations while navigating increasingly complex financial environments. The following sections explore where these technologies are gaining traction and how they’re influencing the business of lending today. 

The pattern recognition inherent in artificial intelligence and the analytical power of the smart algorithms that underlie it offer many benefits for lenders. Among them, the ability to automate and streamline increasing aspects of the approval process.

While this advantage is more pertinent to consumer lending where standardized forms are the rule, it is being adapted to the commercial finance approval process as well.

For instance, AI can easily learn the types of acceptable answers for each area of a loan document, conduct a preliminary review, and flag exceptions for further examination.

Not only does this application of AI reduce processing time, but it enables underwriters to focus on higher-level tasks.

Furthermore, the ability of AI-powered programs to understand how rules are to be applied in different situations helps lenders make sure that the information they require of borrowers and the processes they use to evaluate them comply with relevant local laws.

This quality is particularly important for national lenders such as mortgage finance companies that must adhere to overlapping federal, state, and sometimes local rules.

Lenders are using AI to identify likely borrowers for their loans, pinpointing specific companies and leaders who are most likely to be interested in financing for a particular purpose—whether to purchase equipment or secure a line of credit, for instance—and who are also good credit risks.

Essentially, AI is helping lenders make the most of their funds by allowing them to narrow their focus to high-potential customers and tailor their products accordingly. In many cases, lenders are even using AI to help them identify the right traditional or social media channel to reach each target group.

AI’s emerging role in securitization has drawn increasing attention across global markets. In recent years, fintech lenders have privately placed consumer-loan securitizations that rely heavily on advanced data analytics and proprietary AI models, rather than traditional human-led decisioning, to manage and operate portfolios exceeding $100 million.

In practice, AI-driven models are already being used to evaluate and select unsecured loan assets at scale. Some industry leaders suggest that as these technologies mature, automated decisioning could play a larger role in portfolio construction across asset classes such as CLOs, RMBS, and other ABS structures. 

The influence of AI is being seen in large ways and small. Perhaps most controversially, it is starting to be used to assess the credit risks of consumer and commercial loans.

Traditionally, underwriting has largely relied on a series of well-established financial calculations to determine a business’ or consumer’s capacity to repay a loan. AI represents a fundamentally different approach.

The conclusions it draws might be derived from comparing an individual business or consumer with a cohort of hundreds of thousands or even millions of businesses or consumers with its exact characteristics.

Given a specific collection of data points and a large enough data set of comparable borrowers, artificial intelligence may be able to deliver a highly reliable assessment of creditworthiness that lenders can use in combination with or in place of traditional methods. However, this usage is already bringing transparency into question and will continue to face headwinds as regulators seek to understand companies’ complex algorithms.

In addition to increased accuracy, this broader approach may allow a more comprehensive lending decision to be made that might, for example, include willingness to pay as well as ability to pay.

It also has the potential to allow lenders greater leeway in fine-tuning their pricing on a case-by-case basis. Finally, the inclusion of these nontraditional metrics also makes AI particularly valuable for assessing the creditworthiness of start-ups and businesses who do not have a significant credit history. Essentially, AI is allowing lenders to potentially access a market that had been out-of-reach before.

AI offers comparable advantages to commercial lenders. Factoring companies, for instance, are starting to use AI to better assess the value of the receivables they finance as well as to accelerate the decision-making process, which is crucial in such short-term transactions.

AI has also started to make its appearance in equipment finance, where its ability to harness nontraditional forms of information to determine creditworthiness overcomes one of the challenges that equipment finance companies confront—the limited amount of financial data generated by their small business borrowers.

The emergence of AI is also having a more macro-scale impact. It’s possible that one reason that the current business cycle has lasted as long as it has is that technologies like AI have enabled lenders to find value and secure competitive advantage that couldn’t be found before, whether it’s making better credit decisions, improving business operations, or enhancing the customer experience.

As a result, one could argue that more credit has flowed into the economy because lenders have been able to serve more people with less risk.

As a company that provides financing solutions for non-bank consumer and commercial lenders, and as a leader in the digital transformation of the financial industry, we at Capital One are working closely with clients as they explore and incorporate new technology into their business.

 

Editor's Note: This article was originally published by ABF Journal in August 2019. It has since been revised to reflect the latest advancements in AI and how those developments are actively influencing the commercial and speciality lending space today.