How Generative AI Must Transform Equipment Finance

The time is coming when the equipment finance industry will be transformed by generative AI applications, whose deep-learning models turn enterprise data into rich, original content.

This transformation will have seismic, positive impacts on all industry processes and operations, but getting there from here will require a lot more deliberate thinking about how to do it than almost anybody in the industry has done to this point.

Future-focused problems like these are the reason The Alta Group and Reimagine Advisors formed a strategic partnership in 2020 to bring transformative technology thought leadership to clients seeking a competitive advantage in the digital-first economy. In the January/February 2024 issue of the Monitor, The Alta Group co-CEO Valerie L. Gerard and Reimagine Advisors Founder Denis Stypulkoski provided a call to action for the industry to think big about AI’s future role in what we do. That discussion continues here.

Generative AI examples lacking

Given the nascent nature of AI, we don’t have any fixed models for this transformation. Making it happen will require a large-scale reconsideration of how people who perform our most critical tasks can use AI-based insights produced by intuitive data analysis to make effective, high-level decisions that create win-win-win situations for themselves, EF businesses, and their customers. In such a vision, generative AI moves well past incremental changes in typical EF business processes to empowering an EF workforce that leaves non-value-added work behind and instead performs creative, relationship-building work so that their companies realize their full growth and service potential.

This can be done, but it demands the fusion of technology and expertise where generative AI engines that interpret data partner with the people who use those informed analyses to build and nurture more productive customer relationships.

“AI is moving so fast that equipment finance companies shouldn’t take a wait-and-see approach. By tapping into the transformative power of AI, early adopters will gain a significant competitive advantage from organic growth to operational efficiencies and beyond. That’s why we’re advising clients to invest today in a formal AI Office responsible to creating a data and AI infrastructure that’s efficient and scalable while cultivating trust in AI-driven solutions.”

Valerie L. Gerard, The Alta Group

Differentiating Among AI Types

There are a few less sophisticated data application processes – all of them interconnected within AI – that preceded the emergence of generative AI. Predictive analytics forecasts future events based on historical patterns, data, and statistics. Machine learning (ML), which sometimes is applied to predictive learning, has algorithms that learn from data and past events to identify patterns and make forecasts. Predictive AI – occasionally, and erroneously, marketed as predictive analytics – takes ML algorithms derived from historical data to continuously recognize patterns and emerging trends and make predictions.

For some time now, these older AI-associated technologies have been deployed to the benefit of equipment finance businesses in areas such as managed services. Indeed, machine-learning algorithms are automating core functions across the leasing cycle, including asset valuation, credit underwriting, billing, invoicing, and predictive maintenance. But what really separates generative AI from this pack – and, specifically, from its most advanced forerunner, predictive AI – is that predictive AI uses machine learning and statistical algorithms to evaluate data and predict future developments, while generative AI creates powerful new content with the data it gets from advanced algorithms and deep learning methods, while learning from its own prior results.

That difference contains the seeds of a great industry future, where we can build a constellation of generative AI applications to power every phase of the equipment finance lifecycle.

A Potential Generative AI Use Case

The pace of progress in automating operational equipment finance processes has been glacial, and the results have been modest. Contrast that with the warp-speed leaps forward that could happen if generative AI becomes a practical reality instead of a conceptual exercise. But that possible future reality is a present mystery because apparently nobody has conceived the seemingly inconceivable: a system that perfectly integrates people, process and technology to create a peak-functioning workforce and business cycle. Nobody has thought up a generative AI use case to bring that vision into a sharp, tangible focus.

Not so fast. For purposes of discussion, we actually can chew on something that has some meat on its bones: a suggested use case for small-ticket, late-stage collections. It’s a logical extension of what began many years ago, when small-ticket leaders applied predictive analytics to credit scoring, then evolved the collections experience through machine-learning-driven predictive analytics and predictive AI. Models were created to distinguish between good and bad deals, segregate customers into categories to achieve optimal collection success rates and significantly accelerate accurate real-time predictions about customer delinquency and ultimate loss performance.

Now, we have taken the next logical step and devised a generative-AI upgrade involving a hypothetical use case retiree, Harry, who was a hypothetical company’s best post-60-day collector. This would permit an AI bot to monitor all of Harry’s post-60 calls and e-mails to capture every delinquent customer data attribute and discover how Harry convinces the customer to pay. With all the data that Harry would harvest at the 60-day window, it could develop a persuasive game plan to elicit payment. It’s almost impossible for people to do this, but our auto-Harry could give Harry’s successor collectors the script to behave like Harry, with access to the same data and insights that Harry got, and to see how Harry is thinking about those insights. The upshot is much more effective human-to-human engagement and much greater success in late-stage collections.

“For decades, industries have looked to automation and enhanced training in a quest to manage knowledge transfer during times of transition and downsizing, with limited success.  AI applied properly promises to soften the blow from the ‘Baby Boomer Brain Drain’ to become that expert, trusted, virtual mentor for the next generation of industry workers.”

Denis Stypulkoski, Reimagine Advisors

Generative AI requires thinking big

This scenario might seem highly speculative and improbable, but it reveals the kind of bigger-picture strategic thinking we must do if we’re going to get to the next level with generative AI. The trouble is, our industry tends to adopt change in baby steps. Instead, we need to get everybody in the industry together to brainstorm generative-AI solutions that learn from the major disruptions experienced by parallel industries. That’s how we’ll be able to predict and shape our industry’s future with opportunistic business agility.

Our top priority, then, is building and embracing a foundational digital ecosystem containing a single version of the truth in every deal, so that AI can dependably make informed decisions that reflect the deal’s real attributes.

Read more in the Monitor.