Workflow Automation for a Series B Fintech

Project Overview

A fast-growing fintech startup was scaling its lending operations but hitting a wall: their compliance review process was almost entirely manual. Every loan application required a human analyst to cross-reference documents, verify data points, and flag inconsistencies. It was accurate but painfully slow, and they were hiring faster than they could train.

We mapped the entire compliance workflow end-to-end, identifying which steps were rule-based (and automatable) versus judgment-heavy (and better left to humans). We then built a multi-stage automation pipeline using document parsing, entity extraction, and rule engines, with an LLM layer for edge-case classification and natural language summarization of findings.

The system integrated directly into their existing loan origination platform via API. Analysts now receive pre-processed applications with flagged risk areas and auto-generated compliance summaries. What used to take 45 minutes per application now takes under 10.

Within the first quarter, the team automated 80% of routine compliance checks, freed up over 200 analyst-hours per month, and reduced error rates by 35%. The fintech was able to scale loan volume 3x without adding headcount to the compliance team.

Key Takeaways

• Rule-based steps are prime automation targets in compliance workflows
• LLMs excel at edge-case classification and summarization
• API-first integration minimizes disruption to existing tools
• Automation enables scale without proportional headcount growth

Challenge

Every loan application required manual cross-referencing of documents, data verification, and inconsistency flagging. The process was accurate but took 45 minutes per application, creating a bottleneck as loan volume grew.

Strategy

We mapped the full compliance workflow to separate rule-based steps from judgment-heavy decisions. This allowed us to target 80% of the process for automation while keeping human analysts focused on high-complexity cases.

Solution

We built a multi-stage pipeline using document parsing, entity extraction, rule engines, and an LLM layer for edge cases. The system integrates via API into the existing loan origination platform and delivers pre-processed applications with auto-generated compliance summaries.