Advances in AI are allowing lenders to more accurately predict residual values, a boon for the equipment finance industry as machines become increasingly high-tech.
The global market for AI in financial services is expected to grow by 34.3% annually to $249.5 billion from 2025 to 2032. Verified market research. The global predictive AI market is expected to reach $88.6 billion by 2032, more than four times more than in 2025, according to the research firm. Market.us.
The potential benefits of AI in residual forecasting are particularly important for equipment lenders. autonomous solutiontelematics systems; GPS system others mechanical technology Enter the market. Until now, lenders reluctant to lend Uncertainty in residual value makes it difficult to purchase new technology-intensive machinery. Uncertainty is caused by:
- Limited historical performance data.
- Rapid obsolescence. and
- Lack of resale market.
nearest neighbor
Fintechs and financial institutions can overcome these hurdles by implementing “nearest neighbor technology” powered by machine learning. timothy applegetDirector of Technology Services tamarack technologyof AI and data solutions provider, said. FinAi News” sister publications equipment finance news.
Nearest neighbor methods use proximity to make predictions or classifications about groupings of individual data points, as follows: IBM. The technology will help “fill data gaps that don't exist,” Appleget said.
For example, lenders and fintechs will need to not only collect scarce residual value data for autonomous devices, but also look for the technologies that enable them, or data for other asset types with similar systems.
President Tamarack, data integrity is very important in this process. scott nelson said EFN.
“If you can find an asset type that falls within the definition of this more technical thing, it's like your closest neighbor,” he said.
Borrower's behavior
Borrower behavior is also an important factor to consider when developing AI tools to predict residuals, Nelson said.
“One of the biggest influences on residuals is usage, so the interesting question is: Is anyone looking to aggregate data about operators to predict the behavior of the people moving this equipment?”
— Scott Nelson, President, Tamarack Technology
To accomplish this, fintech lending partners can leverage the data collection and transmission capabilities of emerging device technologies, including: telematicssaid Nelson. Simple technologies such as shock and vibration sensors can also aid in this process, he said.
“You get two things right away. One is run time, because whenever the object is vibrating, it's running,” he said. “If you have uptime, you can run the engine for hours on end. That's one of the big factors. The shock sensor tells you if the engine has been in an accident or if it's been abused.”
“That runtime data can also be converted into revenue generation. How often is this generating revenue?”
— Scott Nelson, President, Tamarack Technology
Appleget said many financial institutions take a conservative approach when lending to relatively new assets, so integrating operator behavioral data and predictive AI could give them a competitive edge.
“For me, this additional asset behavior data opens up the possibility of having more flexibility in setting the residual values you set on certain assets,” he said. “Having that level of knowledge gives you a significant advantage.”
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