Integrating AI into private lending operations will likely increase profits, allowing companies to to hire additional staff, develop new products, and expand into new markets.

When ATMs were introduced more than 50 years ago, there was widespread fear that bank tellers would soon be out of jobs. Yet, the number of bank teller jobs has grown faster than the labor force as a whole. If you had predicted that outcome in the 1970s, you would have been in the minority. The ATM did not replace tellers; it transformed their roles and, paradoxically, increased their numbers (Bureau of Labor Statistics, Occupational Employment Survey).

By looking at historical precedents like the ATM, you can get a sense of the impact AI and automation could have on private lending jobs. The idea that AI will replace loan processors and underwriters tends to be exaggerated. Instead, individuals and organizations that harness the power of AI will have a competitive advantage over those that do not. Eventually, AI will become table stakes, and those not using it will likely be left behind.

Institutional and fintech lenders have been leveraging AI in their operations for years. Consider the online lender Affirm for example. They have developed sophisticated algorithms that are trained on extensive data sets to help them make better underwriting decisions. According to their website, their underwriting models have been trained on billions of data points to assess a consumer’s repayment ability.

Yet it was only with the recent surge in popularity of technologies like ChatGPT that AI truly became a buzzword. This newfound attention highlights the transformative potential of AI in various applications. To understand this shift, it’s essential to explore two notable subsets of AI: machine learning and generative AI.

Machine Learning vs. Generative AI

Machine learning involves training algorithms on large data sets so they can make predictions or decisions without being explicitly programmed for specific tasks. It has been around for decades and has found applications across numerous industries. However, in the private lending sector, machine learning hasn’t resonated as strongly because lenders primarily rely on the value of the asset being financed, and assessing this value hasn’t posed significant challenges that machine learning could address.

Generative AI, on the other hand, represents a more advanced and versatile branch of AI that can create new content, such as text, images, or even entire documents. This technology holds great promise for private lenders due to its ability to analyze vast amounts of data from various files such as financial statements and legal documents. By quickly detecting discrepancies, errors, and inconsistencies, generative AI can streamline the document review process, significantly reducing the time and effort required by human analysts. This capability not only enhances efficiency but also improves accuracy, making generative AI a valuable tool for private lenders looking to optimize their operations.

Applications for Private Lenders

Although these new technologies have numerous practical applications for private lenders, here are two with immediate potential:

Communications. Private lenders can harness the power of AI to raise more capital by efficiently producing high-quality investor memos, reports, and other communications. By automating the drafting process, lenders can produce compelling, well-structured documents that attract more capital. AI can analyze past communications to generate new content that resonates with potential investors, helping lenders secure additional funding and build stronger investor relationships.

Loan processing. Loan processing is one of the most time-consuming aspects for private lenders, often proving tedious for both the borrower and the lender. Although some borrowers may be highly organized and present files in perfect order, it is common for lenders to receive documents with incomplete or missing information. Lenders must then spend hours meticulously reviewing these documents, taking notes, and compiling lists of issues that need to be resolved. Once borrowers resubmit their documents, the review process begins again. Generative AI can streamline this workflow, significantly reducing processing time and enhancing efficiency.

Lenders looking to upgrade their loan processing functions can benefit from AI. When selecting a loan origination system (LOS), lenders should seek systems with advanced capabilities for extracting, comprehending, and summarizing information from various documents. By leveraging such tools, lenders significantly reduce the time required for loan approvals and need fewer staff to process loans, improving productivity and accelerating the approval process.

Impact on Jobs

As noted, a common fear is that increased productivity due to AI will lead to fewer jobs. However, history suggests otherwise, as noted by James Benson in his book “Learning By Doing: The Real Connection Between Innovation, Wages, and Wealth.” Benson writes that before ATMs, an average urban bank branch required more than 20 tellers. When ATMs were installed, however, the number of tellers required to operate a branch dropped to nearly half. The faster and more convenient banking experience generated more demand from customers, driving more frequent visits. With new cost savings, it became cheaper to operate a branch, prompting banks to open new ones. Benson notes that the number of urban commercial bank branches increased 43% between 1988 and 2004. As a result, the total number of teller jobs increased from 200,000 to nearly 600,000.

It is often challenging to understand how new technology will create new jobs, particularly a technology designed to drive automation. Yet, history shows that labor-saving technology often creates more jobs. For instance, in his June 6, 2016 article “What the Story of ATMs and Bank Tellers Reveals About the ‘Rise of the Robots, and Jobs,” James Pethokoukis writes that when scanning technology was integrated into cash registers, the number of cashiers increased. Likewise, he notes that when legal offices began using electronic discovery software, the number of paralegals grew. In yet another of Pethokoukis’ examples, in the 19th-century textile industry, the number of weavers continued to rise despite most of their work becoming automated. More automation meant the price of cotton cloth fell, and people used more of it.

It’s unrealistic to believe that the private lending market is operating at peak efficiency or that the demand for capital for business-purpose loans is being perfectly met. According to the National Association of Home Builders’ Feb. 6, 2023 article titled “Aging Housing Stock Signals Remodeling Opportunities,” the median age of owner-occupied homes is 40 years, with around 60% built before 1980 and approximately 35% before 1970. Although interest rates undoubtedly influence real estate rehab projects, swift and efficient access to capital is a crucial factor for borrowers looking to undertake more projects. By reducing friction and eliminating roadblocks for both borrowers and lenders, we can expect not only an increase in the capital used by real estate investors but also an influx of new investors entering the market. This would lead to additional revenue for private lenders.

A crucial aspect of leveraging AI for growth is the ability to reinvest incremental earnings into new ideas. Lenders with a strong vision for innovation are more likely to succeed in the AI-driven landscape. As Jensen Huang, CEO of Nvidia, aptly noted in an October 16, 2023 YouTube interview, only companies that run out of new ideas need to worry about AI. If you believe your organization lacks the creativity to reinvest earnings, it might be time to join a company that prioritizes innovation or to become a driver of innovation within your own company.

Companies experiencing growth typically do not reduce headcount. Instead, they invest in their workforce to sustain or further accelerate growth. Private lending companies that successfully integrate AI into their operations are likely to experience increased profitability. These profits can be used to hire additional staff, develop new products, and expand into new markets.

When automation made it cheaper to operate a bank branch, banks began opening up new branches and hiring new tellers, and tellers began focusing on more complex transactions instead of handling the mundane ones. In private lending, AI will transform job roles, enabling loan officers, processors, and underwriters to concentrate on high-value activities and improve customer engagement. By becoming proficient in AI technologies and leveraging them to improve their work, private lending associates can ensure their relevance and value in the private lending industry.