BFSI · Use Case
SAR/AML Narrative Generation: 3-Hour Task to 15-Minute Review.
Every flagged transaction still needs a written Suspicious Activity Report narrative before it can be filed — today, an analyst writes that narrative from scratch. A model fine-tuned on your institution's own SAR filing history drafts it instead; a compliance officer reviews, edits, and signs every one before it goes anywhere.
The Problem
Manual narratives don't scale with case volume.
Every SAR filing needs a written narrative — what happened, why it's suspicious, what evidence supports that judgment — and today an analyst writes that narrative by hand for every single flagged case. As transaction-monitoring volume grows, so does the backlog behind it, and narrative quality varies analyst to analyst even when the underlying judgment is sound.
None of this is a decisioning problem. The decision to flag a case already happened upstream — this is a writing bottleneck sitting on top of a decision that's already been made.
Drafting, not deciding
The model never decides whether a case is suspicious or whether to file. It only drafts the narrative for a case a human has already flagged — the highest-leverage place to apply automation without touching the judgment call itself.
The Approach
A full fine-tune on your own filing history.
We fine-tune a model directly on your institution's own historical SAR filings and the case data behind each one — not a generic template, and not simple retrieval. That's a deliberate choice: narrative quality here depends on matching your institution's house style and the regulatory phrasing conventions your compliance team already uses, which a full fine-tune captures far more reliably than RAG alone.
Given a new flagged case, the model drafts a complete narrative in the same style. A compliance officer reviews it against the underlying evidence, edits where needed, and signs it — turning a 3-hour writing task into a 15-minute review.
Why fine-tune over RAG here
Regulatory Q&A benefits from retrieval — pulling the right rule at the right moment. Narrative writing benefits from a model that's internalized your house style. This use case is fine-tuned for that reason; see RAG & LLM Fine-Tuning for where each approach fits.
Guardrails
The same delivery standard as every engagement.
Deployed in your cloud tenant, on model weights you own. Every narrative is reviewed and signed by a compliance officer before filing — never autonomous, never a rubber stamp. Case data used for fine-tuning never leaves your environment, and the model is red-teamed before it touches production.
Built for examiner scrutiny
This is the same boundary published sitewide for BFSI: full delivery on analytics, drafting, and decision-support — human sign-off on anything that touches money movement or a regulatory filing.