Q&A: How Agentic AI Is Changing Loan Processing at Banks and Credit Unions

Rahul Jain spoke with Naveen Gupta, Practice Director for Banking and Financial Services at Xebia, about where loan applications stall and how Xebia Agentic Loan Processing uses agentic AI to take each application from intake to a decision-ready file. For the wider picture, read why loan applications stall before they reach a credit decision.

Borrowers haven’t stopped borrowing, but more of them are going elsewhere. The Federal Reserve Banks’ Small Business Credit Survey found that 29% of small business credit applicants applied at online lenders in 2025, up from 17% in 2020. At most banks and credit unions, the delay isn’t the credit decision. It’s the work that comes before it, as our article Why Do Loan Applications Stall Before They Reach a Credit Decision? explains.

Naveen Gupta on agentic loan processing

What is agentic loan processing, and why does it matter for banks and credit unions now?

Naveen Gupta: Agentic loan processing means a team of AI agents does the assembly work on a loan application: collecting documents, verifying data at source, cross-checking figures and building the analysis. People still make the credit decision. It matters now because borrowers compare banks with online lenders that answer quickly, and most of a bank’s elapsed time goes to work that happens before any decision. The capability to automate that work safely is finally here.

Where do loan applications actually stall?

Naveen Gupta: In the same few places, retail or commercial. Figures get re-keyed into the loan origination system, and the errors show up at underwriting. Verifications of employment and IRS 4506-C requests wait on other organizations. On commercial files, analysts categorize bank statements line by line before they can start the cash-flow analysis. And exceptions, like a missing W-2 or a name that doesn’t match, are found when the underwriter opens the file, weeks in.

How does Xebia Agentic Loan Processing work on a single application?

Naveen Gupta: An orchestrator agent plans the file: which product, which checklist, which agents to run. Six specialist agents then work on one shared credit file. They verify the applicant, collect and classify documents, extract and cross-check every value, verify income and assets at source, build the analysis, and draft the recommendation or the credit memo. They run in parallel where they can, and they check each other’s work. A person sees only the exceptions, with the evidence attached.

How do the agents verify income and assets?

Naveen Gupta: They go to the source. Xebia Agentic Loan Processing pulls IRS transcripts through 4506-C, payroll and employer records, bank data through the institution’s open banking provider, and credit bureau files. Then it matches what comes back against what the borrower uploaded. Verified sources take precedence. If the agents can’t settle a conflict, say an employer name that differs between a pay stub and a payroll record, the file goes to a person with both values side by side.

What does it change for commercial and small business lending?

Naveen Gupta: Commercial is where the assembly work is heaviest, so it’s where the change is most visible. The agents turn three months of bank statements into a categorized monthly cash-flow view, separate owner draws and transfers from operating flows, compute DSCR, and flag seasonality instead of scoring it down. The credit team receives verified data, the cash-flow view and the exceptions in one draft loan credit memo, with every figure traceable to its source.

What about mortgage, auto, personal and HELOC lending?

Naveen Gupta: Mortgage lending is in use today. The agents verify income and assets at source and clear routine conditions before the underwriter opens the file, so the underwriter reviews exceptions rather than the whole file. Auto, personal and HELOC lending use the same agents and the same six stages, configured to each product’s documents, rules and checklists. That configuration is part of every build.

Does Xebia Agentic Loan Processing make credit decisions?

Naveen Gupta: Only if the institution configures it to. By default, the agents prepare a recommendation with plain-language reasons, or a draft credit memo, and the underwriter, the credit team or the bank’s decision engine decides. Every automated step can be set to require a person’s review, and the institution decides which. It also sets the thresholds that route files to an analyst, an underwriter or the credit committee.

How does it support fair lending and adverse-action requirements?

Naveen Gupta: Every recommendation carries plain-language reasons mapped to ECOA and Regulation B adverse-action requirements. Every action, source, reviewer and timestamp is logged in an audit trail, so a fair-lending or model risk review can see exactly why a file was routed, approved or declined. We support the institution’s model risk process; we don’t replace it. Compliance and credit teams keep the final review.

Does it replace our loan origination system?

Naveen Gupta: No. Xebia Agentic Loan Processing sits in front of the loan origination system you already run and works with your decision engine, core, CRM and data providers. It writes its results to those systems. It runs in your own cloud, on AWS, Microsoft Azure or Google Cloud, and personal data is masked and access is set by role.

Why is this a solution and not a product?

Naveen Gupta: Because no two lenders work the same way. Credit policies, product rules, checklists and systems differ from one institution to the next, and the agents have to follow yours, not a generic version. Every build is configured to the institution’s policy, products and stack. That’s also why we start with one loan product and expand from there.

What results should a bank expect, and how do you measure them?

Naveen Gupta: Independent research points to large gains in credit work. In a proof of concept at a retail bank, McKinsey reports that AI agents drafting credit-risk memos showed a potential 20–60% increase in productivity, including a 30% improvement in credit turnaround. But every institution starts from a different baseline, so we measure your own: time to a decision-ready file, conditions at underwriting and analyst hours per file, before and after, on the product you choose.

Where should a lending team start?

Naveen Gupta: With one loan product that hurts today. For many banks and credit unions that’s small business term loans, and for others it’s a single retail product. Pick it, agree on the baseline, configure the agents to your credit policy, and measure. Once the first product works, the same agents and stages carry over to the next one.

Want to see what your loan files could look like? Book a demo of Xebia Agentic Loan Processing to see an application go from upload to a decision-ready file.

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