Why Do Loan Applications Stall Before They Reach a Credit Decision?

Most loan applications at US banks and credit unions don’t stall because of the credit decision. They stall before it. The time goes to re-keying figures into the loan origination system, waiting on mailed verifications, and building cash-flow analysis by hand. Agentic loan processing, the approach behind Xebia Agentic Loan Processing, changes that work. AI agents collect, verify and analyze each application and hand underwriters and credit teams a decision-ready file. Your people keep the credit decision.

It’s 8:40 on a Monday morning, and a credit analyst at a regional bank opens a small business term loan request that came in on Friday. The borrower is a machine shop, organized as an S corporation, asking for $425,000 to replace two machines. The file holds two years of business tax returns, interim financials, three months of bank statements as PDFs, and the formation documents. Before she can start the analysis, she has to re-key the figures into the loan origination system, check that the business name and EIN match across every document, and categorize several hundred bank transactions so she can build a monthly cash-flow view. A deposit in August doesn’t match any invoice, so she emails the relationship manager, who emails the borrower. By Thursday the file is ready for credit. By then, the owner has a term sheet from an online lender.

This scene is illustrative. It describes a common pattern, not a specific bank or borrower.

What is agentic loan processing?

Agentic loan processing uses a team of AI agents to do the assembly work on a loan application: collecting documents, verifying data at source, cross-checking figures and building the analysis. Each file comes out decision-ready for a person to decide.

It differs from adding point AI to a loan origination system, and it is one of the most practical uses of agentic AI in banking today. OCR that reads a pay stub, or a scorecard that rates a file, speeds up one step, and people still move the file from queue to queue. In an agentic setup, an orchestrator plans each application and runs specialist agents in parallel on one shared credit file. The agents check each other’s work, resolve data conflicts against verified sources, and bring in a person when judgment is needed.

Xebia Agentic Loan Processing works this way for US banks and credit unions, across retail and commercial lending. It sits in front of the loan origination system you already run, rather than replacing it. For a practitioner’s view, read our Q&A with Xebia’s Naveen Gupta on how agentic AI is changing loan processing.

The problem: the bottleneck is the application, not the demand

Borrowers haven’t stopped borrowing. The Mortgage Bankers Association forecasts $2.1 trillion in US single-family mortgage originations for 2026. On the commercial side, 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.

That shift has less to do with rates than with effort. Simple loans are already fast: the FDIC’s Small Business Lending Survey found that three in four banks approve their typical small business loan within ten business days. The time goes on the files that need documents, verification and analysis, which are most commercial files and many retail ones.

Across retail and commercial lending, the time goes in the same places:

None of these steps is the credit decision. They’re the work that has to happen before anyone can make one.

Why do verifications still take days?

Verifications take days because many of them still depend on requests sent to other organizations and answered on those organizations’ timelines. As one lending leader at a Xebia client put it: “A written VOE or a 4506-C can sit for three to ten days, and the underwriter still ends up reading the whole file.”

The data to verify income and assets usually exists at source. IRS transcripts, payroll and employer records, bank data through an open banking provider, and credit bureau files can all be reached electronically. What’s missing is the work of calling each source, matching what comes back against what the borrower uploaded, and deciding which value wins when they disagree.

That’s the work agents can take on. In Xebia Agentic Loan Processing, a data agent pulls income and asset data at source while a verification agent extracts and cross-checks every uploaded document. Verified sources take precedence over uploads. When the agents can’t settle a conflict, for example an employer name that differs between a pay stub and a payroll record, the file goes to a person with both values and the evidence attached.

Where does analyst time actually go on a commercial file?

On a commercial file, most analyst time goes to assembling the analysis, not to judging it. Spreading bank statements into a monthly cash-flow view, separating owner draws and transfers from operating flows, computing DSCR, and writing the credit memo can take days before a credit officer reads a word.

Research on agentic AI in credit points to large gains here. 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. In a separate December 2025 article, McKinsey describes one financial institution that cut the time to perform financial-risk analyses by 50% with a multiagent system, with credit officers still reviewing and editing every draft. Both are McKinsey’s findings, not Xebia client results.

The pattern matters more than the exact figure: the gain comes from removing assembly work, while the judgment stays with people.

What changes when agents assemble the file?

When agents assemble the file, each application moves through six stages in parallel where it can, and people see the exceptions rather than every line. In Xebia Agentic Loan Processing, those stages are:

The difference shows up in four outcomes: