AI boosts lending efficiencies by structuring data, managing assets
Market for AI in asset management could hit $38B by 2034
Asset-based lenders are unlocking efficiencies by using AI for structuring data and asset lifecycle management.
CHG-Meridian, a global leasing specialist that finances IT hardware, industrial equipment and medical equipment, is turning unstructured data into actionable insights with AI, Executive Vice President for North America Simon Harrsen told Equipment Finance News’ sister publication FinAi News.
“We’re trying to use AI to make more sense of all the data,” he said. “I think that’s especially important for such a data-heavy organization.”
The company operates in more than 30 countries.
The data points that CHG is structuring include:
- Aftermarket value;
- Condition of assets upon return;
- Historical performance; and
- Industries that customers serve.
Structuring this data provides a deeper understanding of “the actual lifetime of assets,” enhancing underwriting and risk management, Harrsen said.
CHG hopes to reach a point where AI can automatically make suggestions on loan terms and pricing based on its data, “versus someone looking at these manual data points,” he said.
Asset valuation
The global market for AI in asset management is projected to reach $38.9 billion by 2034, up from $5.8 million in 2025, according to research and consulting firm Precedence Research.
Lenders are benefiting from deploying AI for asset management, particularly asset valuation, John Gougeon, president and chief executive at Ann Arbor, Mich.-based UniFi Equipment Finance, told FinAi News. UniFi finances construction equipment, specialty vehicles and fitness equipment.
“It’s a bit mind boggling that you can write a prompt and get a five-year [fair market value] and [orderly liquidation value] curve, and ask whatever AI you’re using to state its sources,” he said. “And they’re generally the same sources I would have used manually.”
Lenders can even train AI to provide asset valuations that are compliant with the Uniform Standards of Professional Appraisal Practice, Gougeon said.
“What would have taken an asset manager a day or two to complete is now being accomplished in a matter of minutes.”
— John Gougeon, president, UniFi Equipment Finance
Fintechs respond
Fintechs are developing AI solutions to address issues tied to unstructured data and asset management. For example, EnFi’s agentic AI platform enables commercial lenders to turn decades of static data into “structured, query-able institutional memory,” Chief Technology Officer Scott Weller told FinAi News.
This is beneficial for asset-based lending because it is “fundamentally a data problem disguised as a credit problem,” he said.
“You’ve got borrowing base reports, [accounts receivable] aging, inventory schedules, field exams, loan agreements, all coming in different formats, at different times, with a lot of variability,” he said.
EnFi structures this data while its AI agents “continuously evaluate it against the credit structure,” allowing lenders to identify dilution trends, concentration risk, ineligible collateral or covenant breaches in near real time, Weller said.
Weller said that asset lenders are showing interest in EnFi’s platform because it can address challenges including:
- Talent shortages, especially roles that require deep document analysis like ABL monitoring and field exams;
- Document-heavy workflows; and
- Delayed risk identification due to periodic review cycles.
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