The news beat on AI continues as, “You must have it!” But now we also hear “companies are struggling with AI” too. One recent survey found that 71% of CFOs are "flying blind" and struggling to monetize AI. Initiatives built around a disruptive technology like AI are always challenging because both the technology and its applications are unfamiliar. In the case of AI they can also be abstract. “Work smarter not harder” is a great tagline, but “smarter” is hard to measure without standardized testing.
I was once asked by a private equity Managing Partner, “What is the best measure of the performance of a CTO?” At the time I was leading the technology strategy of a business of interest for his firm. I surprised myself by coming up with a quick answer, “Gross margin.” In a product business, gross margin (GM) is a measure of the competitive advantage, brand strength and production efficiency. Since GM is defined as the difference between the revenue produced and the cost of goods (labor and materials) as a percentage of that revenue, it is a good measure of technological advantage. A technology like AI decreases the cost of the revenue via labor efficiency and/or increases the revenue of the offering by making it a better match to customer needs, i.e., increases price. Gross margin is a great financial measure of the business impact of both the technology strategists and the contribution of technology investment.
Unfortunately, gross margin is less helpful in equipment finance. Revenue in EF is the interest and fees generated by the capital deployed and gross margin is defined as the difference between the revenue and the cost of capital deployed, i.e., cost of funds, as a percentage of revenue. The first challenge with GM as a technology metric in Equipment Finance is that cost of funds tends to be viewed as more or less a constant driven by macroeconomics. The second is that this definition does not see the effects of the labor and process efficiencies that are the principle promises of an automation technology like AI. For that, Operating Margin is a better tool. Operating Margin includes the costs of producing a unit of financing revenue, e.g., underwriting, documentation, customer servicing, etc. Operating margins, and profitability, improve when technology creates efficiencies and can lead the way to success.
“Does AI save money or make money?” The answer, of course, is both but the opportunity of margin analysis is that it can guide AI strategy and initiatives to the primary business objective – profitable growth. To follow are three ways margin analysis can guide an effective deployment of AI in equipment finance.
Engage risk but avoid the downside
Risk is the probability of undesired outcomes, like delinquency and default. But risk also drives reward in market equilibrium. As a result, a strategy of avoiding risk reduces revenue and both gross and operating margins. “A” credits are safer, but the customers demand lower interest rates, so the capital deployed generates less revenue. Since the cost of originating and underwriting are basically the same for the different credit types, A credits typically have lower gross margins. Further, the competition for A credit customers is more intense because they tend to be in high demand by those with the lowest cost of capital, banks and large captives, so lowest price usually wins. Competitive differentiation is harder for A-credits.
AI changes the risk engagement playing field by converting data – past and present, internal and external into predictions of borrower behavior: “Will they pay or not pay?” Payment behavior is a valuable prediction because it has a range of consequences on the operating margin of the deal. Minor delinquency with paid late-fees can increase the revenue from the deal. Persistent delinquency, even when eventually paid, creates additional collections activity costs that decreases operating margin. Default is the extreme outcome creating no margin at all or even a loss.
AI prediction incorporated in underwriting agents enable a business to engage riskier market segments with higher NIM (net interest margins) but with insights into the outcomes to avoid downside deals and the associated operating margin leakage. AI creates a customer engagement precision that protects and increases operating margins through better customer selection.
Spend less time wondering and more time working on the right deals
“Time is money” is a maxim in any industry, but nowhere more so than in the time-based pricing offerings of equipment finance. As a result, any change in workflow that accelerates daily decisions not only creates competitive advantage with time-sensitive customers, it also makes money by deploying capital faster.
As the table shows, mid-sized and small lenders start with lower gross margins due to higher cost of funds. Smaller firms also suffer efficiency costs due to the economies of scale in underwriting and documentation reducing both operating margins and competitive advantage against bigger lenders.
This is an area where AI will again change the rules. The daily decisions of small-ticket lending generate large amounts of data that is the fuel for AI-based automation. AI automation breaks the “economies of scale” advantage of larger operations by essentially eliminating the fixed costs, i.e., labor, of the process. The cloud makes the cost of an AI-agent sales or underwriting decision for a small firm basically the same as for a large firm. Speed also increases customer satisfaction and the pace of capital deployment which in turn generate more revenue. Even if revenue is only maintained, the cost of creating that revenue decreases with speed so margins improve.
AI agents can decide: “Call or Don’t Call”, “Approve or Not Approve”, “Retain or Syndicate” quickly and safely. These daily decisions generate data rapidly, so machine learning models have the data they need for accuracy and improve quickly via learning. AI can level the playing field for small business operators by reducing both the costs and getting to actions more quickly.
Reduce the cost of capital by giving funders exactly what they want
Regarding equipment finance Grok says:“Gross margin is less suitable as a technology measure as it focuses on funding spreads, unchanged by most tech implementations.”
Not true. A first principle of design thinking is that “buyers pay more for exactly what they want from a brand that they trust.” The same is true for capital partners, those funding either individual or pools of deals generating more capital for the enterprise. Funders all have their favorite “credit box” parameters and define their interests with literally dozens of variables. Compass Capital CEO John Gauger says, “Money has rules” and that he can characterize buyer rules with about forty terms. Forty terms, not the usual 5-8 used in lender scorecards.
“Credit Box” seems like a poor term for funder requirements – boxes have three dimensions – Gauger has found that matching a funder’s preference is much more like a very complex combination lock. But even forty variables are a cake walk for both digital models and AI predictors. A lease is a product that has characteristics that AI can match to a set of lenders, i.e., predict lender acceptance. Better yet, a pool of leases is a “product” that can be designed to exactly match what funders want. Gauger says that the money will share their preferences so that they don’t have to wade through data themselves to find it. Those preferences combined with data from ongoing purchases enable models to identify what a funding partner wants, match a product to those wants with full transparency, and then consistently deliver that product via a focused origination strategy. A better product consistently delivered commands a higher price thereby lowering the cost of capital and increasing both gross and operating margins throughout the business.
An unfortunate reality and call to action
Margin analysis is great tool for designing and guiding strategic initiatives to leverage AI and improve profitability, make more money. The unfortunate reality is that most equipment finance companies do not spend the effort to capture the data necessary to do this analysis. Since cost of funds is viewed as a macro-economic constant unavailable to influence, very few entities measure it over time, by industry, by asset type, or market segment. Similarly, very few companies are measuring “time in stage” or allocating labor by deal, asset or industry types, or market segment needed for operating margin calculations. Most equipment finance companies do not measure and capture the data necessary to use margin analysis in their AI deployments.
Gross and Operating Margins are two of the most strategic and important KPIs in any business. They measure customer demand, competitive differentiation, and the ability to produce at scale. This is not new, but it is often not part of enterprise discussions on how to leverage new technologies, particularly disruptive technologies like AI. Margin improvements, whether from cost savings or customer value improvements, are the right guide for deploying AI. Risk engagement, speed, and customer satisfaction are good first targets for AI to significantly impact equipment finance business performance.
If you have the right data.