Facing excess inventory, elevated equipment prices and tightening credit, leaders in the equipment finance industry say collaboration among dealers, lenders and OEMs is key to finding stability amid economic uncertainty. 

As dealer and finance operations expand nationwide, lenders must collaborate with trusted dealers to inspect and deliver quality equipment that meets customer needs, Jody Ray, vice president and relationship manager at BMO Bank North America, said during Equipment Finance News’ webinar today. 

“That collaboration piece is crucial and critical,” he said. “In my world, we trust our dealers and we depend on them, almost solely, to be the eyes and ears, sometimes, of the lending team and the asset management team.”

Watch the webinar here

Dealers are the critical link between lenders and customers, providing visibility into real-world equipment conditions that influence credit and valuation decisions that would otherwise need third-party assistance for equipment monitoring, John Gougeon, president and chief executive of UniFi Equipment Finance, said during EFN’s High-priced used equipment inventory: The no-man’s land of equipment finance webinar. 

“Nothing beats having eyes on the equipment,” he said. “We have some internal hurdles that may be deal size, cost of equipment or age of equipment that require us to seek third-party support or an inspection.” 

Lenders, in turn, are rethinking financing structures, using flexible floorplans and tailored repayment schedules to protect dealer liquidity and accelerate inventory turnover. 

“At the same time, we also have to look at the retail side of that when they sell the piece of equipment, and we’re looking at the customer of the dealership’s ability to purchase the piece of equipment,” he said. “As the lender, we’ll need to make sure that we’re on top of every piece of the transaction, so that we can make that a smooth and seamless transaction for our customer, the dealer and their customer purchasing the equipment.” 

Excess inventory collaborations 

Collaboration can also enable dealers, lenders and OEMs to address excess inventory, especially with strategic programs, Kevin Pate, director of fleet and heavy-duty equipment at Shoppa’s Material Handling, said during the webinar. More than 70% of ag equipment dealers and nearly 30% of truck dealers reported excess late-model inventory in the second quarter, according to IronAdvisor Insights. 

The collaboration “would be something in the vein of a shared remarketing program, whether it’s a joint site where dealers that work with that vendor are able to load their assets, maybe not just what’s with that company,” he said. “You’d be looking for OEM-supported programs, similar to a certified pre-owned program, where they come with finance options, extended warranty options and things like that.” 

Check out our exclusive industry data here. 

Technology – especially AI – is disrupting the world of finance (see overviews in Duffie et al. 2022, Foucault et al. 2025, and Vives 2019). Lending is no exception: machine learning and large datasets are successfully used for credit assessment. Fintech has enabled efficiency gains, such as improved loan screening, monitoring, and processing, and has fostered financial inclusion among underserved populations and in less developed countries.

At the same time, it raises concerns about financial stability, privacy, and discrimination. Digital technologies enable improved customer segmentation, which not only facilitates personalised services but also allows for finer price discrimination. The empirical evidence on fintech’s impact is mixed regarding loan pricing, substitutability or complementarity of fintech and bank credit, loan default, and data sharing.

Empirical studies differ on whether default or delinquency rates are higher for fintech-originated loans than for bank-originated loans. While some report higher default rates (Di Maggio and Yao 2021), others report lower (Fuster et al. 2019), and still others find no significant difference (Buchak et al. 2018). Similarly, open banking initiatives increase the likelihood that SMEs form new lending relationships with non-bank lenders and reduce their interest payments. Still, they do not necessarily improve financial inclusion (Babina et al. 2024). However, in Germany (Nam 2023) and India (Alok et al. 2024), open banking has improved credit access on both extensive and intensive margins without increasing risk. In the US, California’s Consumer Privacy Act strengthened fintechs’ screening capabilities relative to banks and enabled more personalised mortgage pricing, ultimately reducing loan rates and improving financial inclusion (Doerr et al. 2023).

An analytical framework

In Vives and Ye (2025a, 2025b), my co-author and I present an analytical framework that incorporates key differences between fintech firms and incumbent banks, explains the mixed empirical findings in the literature, and delivers a welfare analysis. The framework introduces a taxonomy of how fintech affects frictions in the lending market. We find that fintech’s impact on competition and welfare hinges on its effect on the differentiation between financial intermediaries and the efficiency gap between them. Primary factors influencing market performance include the level of bank concentration, the intensity of competition among fintechs, the potential for price discrimination, the size of the unbanked population, and the convenience offered by fintechs.

We consider a spatial oligopolistic competition model in which lenders (banks and fintechs) compete to provide loans to entrepreneurs. The framework captures key differences between fintechs and banks. For example, banks have more financial data and soft information (with relationship lending) than fintechs, but the latter have better information-processing technology and conversion of soft into hard information (with the digital footprint) and lower distance friction with borrowers. This distance can be physical or in terms of expertise; greater distance between a lender and borrower increases the cost of monitoring (or screening).

Furthermore, banks have lower funding costs, and fintechs have higher convenience benefits. Fintechs also have greater price flexibility for technological and regulatory reasons, which gives them a competitive advantage. In the extreme, banks are differentiated by expertise (location), but fintechs are not; fintechs can price discriminate, whereas banks cannot. In our model, endogenous entrepreneur participation occurs at each location, and entrepreneurial projects require monitoring (screening) to enhance project returns (Vives and Ye 2025b) or to mitigate a moral hazard problem faced by entrepreneurs (Vives and Ye 2025a).

The type of fintech advancement matters

A key insight from Vives and Ye (2025a) is that we should distinguish between general advances in fintech that reduce the distance between lenders and borrowers and those that do not. General improvements in information collection and processing, such as enhanced data storage, computing power, or desktop software, do not necessarily reduce distance friction. Technologies that lower the effective distance between lenders and borrowers include improved internet connectivity, video conferencing, remote learning tools, AI, and advanced search engines, which enable lenders to expand their domain expertise and serve distant borrowers more effectively. Big data, together with machine learning, can improve both types of capabilities.

If fintech does reduce the distance friction, lenders’ differentiation will decrease and competition intensity will increase, decreasing their profits and monitoring incentives. The effect is more pronounced when the entrepreneurs’ moral hazard problem is more severe. The impact on entrepreneurs’ investment and total welfare is hump-shaped. Those effects are not present when fintech progress does not affect the distance between lenders and borrowers.

Bank and fintech competition

In Vives and Ye (2025b), we assume that banks are differentiated by expertise (located in a circle) but fintechs are not (located in the virtual middle). We find that (1) fintech entry can be blockaded, remain as a potential threat, or materialise depending on fintechs’ monitoring efficiency, (2) fintech lending can substitute or complement bank lending depending on whether pre-entry banks competed or not, and (3) fintech entry and loan volume is higher when bank concentration is higher.

Furthermore, if banks cannot price discriminate, a fintech with no advantage in terms of monitoring efficiency or funding costs can enter the lending market. If banks and fintechs have similar funding costs, for entrepreneurs with similar characteristics, banks’ loan rates and monitoring are higher than those of fintechs (and fintech borrowers are more likely to default). The latter result will change if fintechs have significantly higher funding costs than banks. If fintechs have a significant advantage in convenience, they will likely charge higher prices, while banks will conduct more thorough monitoring. Therefore, differences in funding costs, convenience benefits, and abilities to price discriminate may explain the variety of empirical results on loan defaults by banks and fintechs.

Fintech entry may decrease entrepreneurs’ investment if competition within fintechs is not sufficiently intense. An intermediate level of competition intensity among fintechs is needed to ensure a welfare increase following fintech entry, to balance the incentives of borrowers and lenders.

However, if banks can also discriminate, fintechs need an advantage in monitoring (or funding costs, although this is less probable) to penetrate the market. Finally, the threat or actual entry of fintechs can induce bank exit or restructuring, potentially reducing the intensity of lending competition and investment, but generating a welfare-improving option value effect.

Policy implications

We can derive some policy implications from the analysis. We know that price discrimination is a competitive weapon, but it will not necessarily be welfare optimal unless it extends the market. This is so also in our modelling. Socially optimal loan rates strike a balance between the incentives of entrepreneurs and intermediaries to exert effort, thereby mitigating moral hazard, encouraging entrepreneur participation in the market, and enhancing lenders’ monitoring or screening effort.

However, this balance typically cannot be obtained from lender competition with location-based discrimination. For example, with endogenous entrepreneur participation at any location, a bank should charge (from a welfare perspective) higher rates for distant locations (since monitoring is more costly and distant locations generate less surplus). In contrast, price-discriminating banks will do the opposite in equilibrium to meet the competition. However, allowing banks to discriminate when fintechs price discriminate improves welfare when there is little inter-fintech competition.

Regarding data sharing, we find that a policy (e.g. open banking) that benefits fintechs must be complemented by an appropriate degree of inter-fintech competition. Otherwise, the policy may backfire, and a leading fintech may gain a monopoly position in a market segment. Differences in the degree of competition may explain the differences in the empirical results in the impact of open banking.

In summary, levelling the playing field (in terms of lenders’ ability to price discriminate and access to information) is a good policy aimed at achieving a degree of competition that induces a division of rents, thereby balancing the incentives of different market participants to maximise welfare. This degree of competition must be sufficient to prevent monopoly positions in market segments, while also ensuring that both lenders and borrowers have enough stake in the game.

References

Alok, S, P Ghosh, N Kulkarni, and M Puri (2024), “Open banking and digital payments: Implications for credit access”, working paper.

Babina, T, S A Bahaj, G Buchak, F De Marco, A K Foulis, W Gornall, F Mazzola, and T Yu (2024), “Customer data access and fintech entry: Early evidence from open banking”, working paper.

Buchak, G, G Matvos, T Piskorski, and A Seru (2018), “Fintech, regulatory arbitrage, and the rise of shadow banks”, Journal of Financial Economics 130: 453–83.

Di Maggio, M, and V Yao (2021), “FinTech borrowers: Lax screening or cream skimming?”, The Review of Financial Studies 34: 4565–618.

Doerr, S, L Gambacorta, L Guiso, and M Sanchez del Villar (2023), “Privacy regulation and fintech lending”, working paper.

Duffie, D, T Foucault, L Veldkamp, and X Vives (2022), Technology and finance, The Future of Banking 4, CEPR Press.

Foucault, T, L Gambacorta, W Jiang and X Vives (2025), Artificial intelligence in finance, The Future of Banking 7, CEPR Press.

Fuster, A, M Plosser, P Schnabl, and J Vickery (2019), “The role of technology in mortgage lending”, The Review of Financial Studies 32: 1854–99.

Nam, R J (2023), “Open Banking and Customer Data Sharing: Implications for Fintech Borrowers”, SAFE Working Paper No. 364.

Vives, X (2019), “Digital disruption in banking”, Annual Review of Financial Economics 11: 243–72.

Vives, X, and Z Ye (2025a), “Information technology and lender competition”, Journal of Financial Economics 163: 103957.

Vives, X, and Z Ye (2025b), “Fintech entry, lending market competition, and welfare”, Journal of Financial Economics 168: 104040.

Interim Vice President for Finance Reka Wrynn announced to the Undergraduate Student Government (USG) on Wednesday that the University of Connecticut had reduced its budget deficit from $37.9 million to $12.6 million, adding that tuition and fees were likely to increase. 

“We don’t have those exact amounts yet, but yeah, it’s inflation,” Wrynn said. “The cost of things goes up. But I will say that the university is committed to any time we increase tuition and fees that there is an increase in the financial aid bucket.” 

The university is in the process of implementing what Wrynn called a financial sustainability plan consisting of three key pillars: growing enrollment, resource reallocation and personnel optimization and reduction, adding that the “guiding principle” of these initiatives was to keep students from being “negatively impacted.” 

“We want to hear back from you,” Wrynn said. “If you know you’re being negatively impacted, we want to hear that, so you know certainly we’ll look into them.”  

Wrynn announced that UConn plans to increase enrollment by 4,000 students over the next five years. Multiple senators questioned this initiative, citing a housing crisis on the Storrs campus along with shrinking access to amenities like areas for students to study. 

Wrynn said that there was no housing crisis at UConn, saying there were plenty of empty beds available across UConn’s multiple campuses. 

“Don’t believe everything you read. We have plenty of empty beds this year,” Wrynn said. “At the Storrs campus, in Stamford, in Hartford, we have empty beds right now. So, we would like to grow enrollment to fill those beds.”  

When pressed by one senator regarding the number of available beds on the Storrs campus, Wrynn said she believed there were roughly 550 available beds on the campus. Wrynn added that a private development just outside of campus was also underway to provide more housing for students, in addition to housing expansions at the Hartford and Stamford campuses. 

Other senators questioned Wrynn regarding student access to study spaces on campus, which Wrynn claims there are plenty of, according to data given to her by the Dean of the UConn Library. 

Senators challenged Wrynn’s claim and said they frequently had to search every floor of the library to find space to study. One senator brought forward concerns about the accessibility of spaces known as study pods, which are inaccessible to some students due to their raised nature. 

Wrynn added that resources would be reallocated to “our high priority areas,” to ensure they experience as little cuts as possible. 

A senator representing the African American Cultural Center (AACC) asked Wrynn why so many black organizations had experienced such large cuts to their budgets. Wrynn said it was because those organizations were not a part of those high priority areas. 

“Primarily non-academic funds were the funds that were swept,” Wrynn said. “I would have to dig into the specific case to see, you know, maybe to step back a little bit in that there’s only so much money, right, to spread around and things get more expensive every year, whether it’s the travel or whatever the event is that you’re looking to participate in.” 

Wrynn also spoke on UConn’s loss of federal research grants since the change in administration. The university had 63 of its grants terminated, which provided $41.3 million in funding for ongoing and future research. 

Wrynn said that UConn is in the process of shifting its research priorities to be in line with the priorities of the Trump administration, something she says is usually procedure. 

“We are pivoting as we do every four years when there’s a new administration,” Wrynn said. “We tend to pivot and realign our research priorities with the priorities of that administration and seek out research awards that are along uh those lines.” 

Those research priorities include national security, quantum information science, biosciences, artificial intelligence and health, wellness and quality of life, according to Wrynn. 

Senators asked Wrynn what UConn’s research priorities were during the Biden administration to make the shift in priorities clearer to students. Wrynn said she was not familiar with those priorities herself but said they were published somewhere online. 

A UConn Today article from September 2021 lists genomics and neuroscience, climate studies, cybersecurity, energy, personalized medicine, cancer detection and care, manufacturing innovations among others as previous research priorities. 

In addition to federal cuts, Wrynn mentioned the constant funding risk from the state government, which can reduce UConn’s funding by up to five percent without the approval of the legislature, according to her. 

“That’s always a risk in our budget,” Wrynn said. “If they should choose to do that throughout the year for any given reason, then that would create an additional reduction in our budget. And so, we just continue to remind people of that risk.” 

State funding dropped to $268.2 million in Fiscal Year 26 from $319.5 in Fiscal Year 25. $95.7 million, or 18% of FY 25 funds were denoted as temporary support. 

Wrynn said the university expected another $15 million decrease in state funding for FY 27 but had submitted a request for $12 million to be put back into permanent funding. 

While the university is operating under a deficit this financial year, Wrynn said that UConn still had the funds to cover current expenses for the time being. 

According to Wrynn, the university has a balance of cash funds set aside for circumstances like this, similar to a savings account. But those funds are limited according to Wrynn.  

“As you all know, when you spend that money down from your savings account, that’s one time funding,” Wrynn said. “It can Band-Aid the problem for one year, but once you spend it, it’s gone. And so, it doesn’t solve any problems.”