The move comes as lenders grapple with chronic staffing shortages and mounting pressure to speed up lending decisions. The Boston-based startup’s raise highlights how agentic AI is moving deeper into regulated banking workflows, particularly at regional and community institutions that lack the scale of national players.
The funding round was led by Fintop, with participation from Patriot Financial Partners, Commerce Ventures, Unusual Ventures and Boston Seed Capital. Collectively, those firms are connected to more than 150 financial institutions, primarily smaller banks. The raise brings EnFi’s total funding to $22.5 million and is earmarked for broader deployment of AI agents that assist with credit analysis and lending decisions.
“The use of the artificial intelligence agents makes the smaller banks much more competitive in credit,” EnFi Co-Founder and CEO Joshua Summers told Reuters.
Summers noted that regional and community banks routinely leave thousands of credit analyst roles unfilled, effectively capping how much business they can underwrite. EnFi’s AI agents are designed to shoulder much of the analytical workload, reviewing borrower leverage, collateral and credit histories while flagging inconsistencies in documentation. Banks tailor the agents to their own credit portfolios, and as the systems absorb more data, they are intended to increase lending capacity without adding headcount.
The raise comes as banks are reassessing how AI fits into their core operations. Industry leaders at Davos recently emphasized that AI in banking must prioritize reliability, transparency and governance over speed alone.
“The power of AI is only as good as the data that it resides on,” Snowflake Global Head of Financial Services Rinesh Patel said to PYMNTS CEO Karen Webster in a November interview. He explained that banks, insurers and asset managers have “a huge treasure trove of data.” However, in the past, it has been tough to use the data efficiently due to fragmentation and compliance requirements.
The technology is becoming more sophisticated. Providers such as Amazon Web Services and Microsoft have been making the case that the architectures of today’s banking AI agents outperform earlier generative models in accuracy and auditability, especially when embedded directly into workflows. At the same time, bank executives and regulators have stressed the importance of human oversight, clear accountability and controls that treat AI systems more like supervised digital employees than autonomous black boxes.
