Across four compliance workflows, the bank saves an estimated 24,300 employee hours a year, Bretton AI said. That equals the annual hours of roughly 12 full-time employees, based on a 2,080-hour work year. The workflows are SBA origination, enhanced due diligence, fraud reviews and periodic customer reviews. The figure measures freed capacity, not payroll savings.
The agents do the research that analysts once did by hand. They retrieve corporate records, identify beneficial owners and check sanctions lists and fraud indicators. They then consolidate the findings into a structured report. Analysts review the report and take the cases that need judgment.
When the bank’s SBA manager asked whether the gains meant fewer people, CEO David Becker said no. “I want you to do twice as much business,” Becker said in the Bretton case study.
The bank was already growing. It funded $610 million in loans under the SBA’s 7(a) program in 2024, up 44% from a year earlier, the bank said in early 2025. Becker told Pulse 2.0 in February that the bank has become a top 10 SBA 7(a) lender. More volume meant more files for the same analysts.
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Customers Bank Closes Commercial Loans in 7 Days
First Internet is not alone in aiming AI at the slowest part of the loan file. Customers Bank in Pennsylvania used AI in a pilot to close select commercial loans in seven days, PYMNTS reported. The typical period is 30 to 60 days. The bank’s parent, Customers Bancorp, also estimated more than $15 million in annualized staffing capacity gains as it scales the process. Employees estimated cumulative savings of 76,000 hours across the bank’s AI initiatives.
The compliance work is the same at both banks. Lenders must verify ownership, confirm identities and screen for risk before a loan moves forward. Even when a borrower meets credit requirements, delays in that work slow the file. Both banks are shortening that step instead of adding staff to it.
63% of Enterprise Leaders Name Data Quality a Barrier
That speed depends on clean data, and most large companies are not there yet. PYMNTS Intelligence’s April Enterprise AI Benchmark Report, “The Enterprise AI Readiness Gap,” found that 63% of enterprise leaders cite data quality as a barrier to AI. The report surveyed 65 senior technology executives at U.S. firms with at least $1 billion in revenue. Nearly all, 99%, said they are very or extremely confident their data governance supports enterprise AI. Yet 85% said their data is fragmented or only moderately integrated. Only 15% said it is mostly integrated.
Financial services is no exception. In the May edition of the benchmark, 30% of financial services firms named data quality as their top barrier, PYMNTS Intelligence reported. A lender that closes in seven days competes against one that takes 30 to 60. Staying that fast takes data its agents can read.
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