Financial ServicesAI Engineers On Demand12 weeks
A mid-market lending firm processed 800+ loan applications every month, each one taking 5 business days to review by hand. We built AI agents and an automated underwriting pipeline that scores, validates, and routes applications in under 90 seconds.
Under 90 Seconds
Approval Processing Time
7 in 8 Applications
Automated Decision Rate
25+ Per Week
Analyst Hours Recovered

“Delivered ahead of schedule and the system paid for itself within months.”
Jane Fraser
Client Lead, AI-Driven Loan Approval Transformation
Before
5-day manual review cycles, fragmented data across legacy systems, no senior technical leadership
After
90-second automated approvals, unified real-time analytics layer, embedded fractional CTO driving strategy
Narrative
The Challenge
The client's underwriting team reviewed every loan application manually, averaging 5 business days per decision across 800+ monthly submissions. On top of that, their senior technical leader had recently left, which stalled an in-progress team migration, opened compliance gaps, and exposed a data infrastructure that could not handle the volume.
What We Built
We embedded a fractional CTO who built the operational roadmap and directed the buildout of AI agents that evaluate creditworthiness using behavioral patterns, income signals, and repayment history. One agent consolidates fragmented data into a unified analytics layer, a second runs risk scoring models against each application, and a third routes edge cases to human reviewers while auto-approving qualified borrowers. End-to-end processing now completes in under 90 seconds per application.
Results
7 out of every 8 loan applications now receive a fully automated decision without a human touching them. Processing time dropped from 5 days to under 90 seconds for standard approvals. Compliance escalations fell by more than half, freeing up 25+ analyst hours every week. The engagement paid for itself within 3 months through recovered loan volume that the firm had been losing to applicant drop-off during long wait times.
Story gallery
Technical proof
Implementation details and the stack behind the delivery.
Implementation highlights
Key engineering decisions from this project.
Stack snapshot
Tools and platforms used in the delivery.
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