AI-Powered Loan Decisioning Engine
Custom ML pipeline that cut loan approval time from 5 days to 4 hours while improving accuracy.
Where Nexaflow was stuck.
Nexaflow's manual underwriting process was the single biggest bottleneck in their growth. Approval took 5 business days on average, 40 % of applications required manual follow-up for missing documents, and underwriters were spending 70 % of their time on cases that a model could handle with confidence. The compliance team needed every decision to be fully auditable.
How we solved it.
We built a three-stage decisioning pipeline: an OCR-powered document extraction layer, a gradient-boosted risk model trained on 4 years of historical loan performance, and an explainability layer that generates a plain-language audit trail for every decision. Edge cases, flagged by confidence score, are routed to a human queue with a pre-populated summary. The system integrates directly with their core banking platform via a REST API with under 200 ms latency.
Results that matter.
The engine went live in June 2024 and processed over 8,000 applications in its first month. Approval time dropped from 5 days to 4 hours for straight-through cases (78 % of volume). Manual review cases arrive with a full document summary, cutting underwriter time per case from 45 minutes to 12. The model holds a 94 % accuracy rate on a held-out validation set and has been re-trained twice since launch.
down from 5 days
on validation set
at go-live
per case
"The AI decisioning engine has become our biggest competitive differentiator. What impressed me most was how quickly Synapse got up to speed on our domain, asked the right questions, and pushed back when our initial brief was flawed. Three years in, they still feel like an extension of our own leadership team."
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