Out with old: A modernization playbook for lending in AI era

Lenders operating on older legacy systems face an uphill battle to adopt AI, necessitating a meticulous modernization strategy. Nearly 75% of financial institutions devote roughly 60% of their development resources to maintaining systems rather than innovating, Tim Yalich , vice president of business development at Carleton , told FinAi News , citing a survey conducted by the company. Carleton provides software solutions, compliance support and documentation tools for lenders. “You can't sprint toward AI when most of your technology team is busy keeping the lights on." — Tim Yalich, VP of business development, Carleton One of the biggest barriers to integrating AI into older systems is that they “simply do not speak to other systems effectively,” Scott Weller , chief technology officer at EnFi , an agentic AI fintech for commercial lending, told FinAi News . “Even when those APIs are available, those legacy systems have very limited API access,” he said. “That presents a challenge when you want to enable a lot of interoperability among systems to drive more automation.” Additionally, many legacy systems rely on “keyed-in data” and an “error rate that we have to consider,” Weller said. “A lot of the data that is keyed in has a drift to it that isn't current,” he said. Weller said he has seen several clients use systems from the late 1990s or early 2000s, which he considers especially old. Slow and steady Modernizing a technology stack costs as much as $150,000 for a small, three- to six-month project, compared with at least $500,000 for a larger, 12-month project, according to engineering and AI consulting firm FullStack . While lenders often think of modernization as a multiyear project involving massive expenses, they should prioritize shorter timelines and gradual upgrades, Carleton’s Yalich said. “I am always going to be spending money keeping up and upgrading, no matter what. A budget related to that should be constant, not a one-time approval. You have to have it as continuous and then you just work within that framework and constantly chip away.” — Tim Yalich, VP of business development, Carleton What’s the use? Modernization r equire s careful selection of short- and long-term infrastructure upgrades. FIs with outdated systems must first pinpoint AI use cases to determine which upgrades to prioritize, EnFi’s Weller said. For example, if a lender wants to deploy an AI tool for credit memo evaluation to expedite credit decisions, “it probably touches a few systems,” he said. This “allows you to kind of look at the standard operating procedure for the former flow and ask yourself, ‘What systems does that touch?’” he said. “’What specific information do we need?’ "And then you kind of build a strategy on how to essentially climb the rungs of the ladder to the most complex version of that.” Weller said this strategy has three primary levels, or rungs: First rung: A basic “minimum viable product” that runs the whole process end to end with all the data in place; Second rung: Making the data more accurate; and Third rung: Speeding up the part of the process that is still slow. “That ... strategy is going to allow you to kind of avoid boiling the ocean and building too many dependencies or hooks into legacy things that might need to be upgraded,” Weller said. This approach also helps employees better understand AI, he said. “They can get reps with AI, they can have a win with a targeted use case and then they can decide how they want to make that more complex as they expand that use case,” he said. Yalich agreed. “Focus on what your problems are, tackle those and then look at the bigger picture because you definitely want to get some immediate efficiency gains,” he said. FinAi Lending Summit, set for Oct. 7-8 in Las Vegas, will include speakers from Fifth Third and Capital One as well as a fireside chat with U.S. Bank Senior Vice President – Lending Operations Group Manager Suzanne Rathbun . To learn more about the 2026 event and register, visit here .

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Out with old: A modernization playbook for lending in AI era

75% of FIs spend most of R&D resources on maintaining legacy systems

Feedzai launches ‘always-on’ AI agent for fraud prevention

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