TTabula

Financial Services

How a Fintech Startup Cut Compliance Review Time by 60%

A 40-person fintech company was drowning in manual compliance reviews. Tabula built an AI workflow that cut review time from 45 minutes to 18 — and caught more issues.

60% faster reviews12% more issues caughtZero backlog in 3 weeks

When ComplianceTech (name changed for confidentiality) reached out to Tabula, they were processing 300+ compliance reviews per week. Each review took an average of 45 minutes. The team of 8 analysts was working overtime, and the backlog was growing.

The Baseline

We spent two weeks embedded with the compliance team, mapping their exact workflow. What we found was surprising: only about 15 minutes of the 45-minute review was actual analysis. The rest was data gathering — pulling reports from 4 different systems, cross-referencing customer profiles, checking against regulatory databases.

This is the pattern we see everywhere: the work isn't the work. The work is finding the work.

The Build

Tabula built a 3-stage AI workflow:

  1. Ingestion layer: Automated data pull from all 4 source systems, normalized into a single review workspace
  2. Analysis layer: An LLM-powered pre-review that flagged high-risk items, cross-referenced regulations, and surfaced relevant precedent
  3. Decision layer: A structured review interface where analysts made final calls, with every decision logged for audit

The Results

After 6 weeks of iteration and refinement:

  • Average review time: 45 minutes → 18 minutes (60% reduction)
  • Issues caught: improved by 12% (the AI spotted patterns humans were missing)
  • Team morale: analysts reported spending time on "actual analysis, not copy-paste"
  • Backlog: eliminated within 3 weeks of deployment

What Made It Work

Three things:

  1. We didn't replace the analysts. The AI did the grunt work. Humans made the decisions. This was critical for regulatory acceptance.
  2. We built for observability. Every AI decision was traced, timestamped, and auditable. The compliance lead could see exactly what the system did and why.
  3. We iterated in production. Week 1 was rough. Week 3 was solid. Week 6 was excellent. You can't design a workflow like this upfront — you have to refine it with real data.

Six months later, the team is handling 30% more volume with the same headcount, and audit findings are at an all-time low.