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 writes the draft instead, and 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. It has to say what happened, why it looks suspicious, and what evidence supports that judgment. Today an analyst writes that narrative by hand for every flagged case. As transaction-monitoring volume grows, the backlog behind it grows too, and narrative quality varies from 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 has 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, which is 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 historical SAR filings and the case data behind each one. That’s a deliberate choice over a generic template or plain retrieval. Narrative quality here depends on matching your institution’s house style and the regulatory phrasing your compliance team already uses, and a full fine-tune captures that 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. A 3-hour writing task becomes 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 has internalised your house style. This use case is fine-tuned for that reason; RAG & LLM Fine-Tuning covers 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, so it’s never autonomous and 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, with human sign-off on anything that touches money movement or a regulatory filing.
FAQ
Common questions about SAR/AML narrative generation.
Does the model decide which cases to file?
No. It drafts the narrative for a case a human has already flagged. The decision to flag the case, and the decision to file the SAR, both stay with compliance.
Why fine-tune a model instead of using RAG?
Narrative writing depends on matching your institution’s house style and the regulatory phrasing your compliance team already uses. A full fine-tune on your own filing history captures that more reliably than retrieval. RAG fits regulatory Q&A, not house-style drafting.
How much time does this save per case?
It turns a roughly 3-hour drafting task into a 15-minute review.
Where does the case data live?
In your cloud tenant. The data used for fine-tuning never leaves your environment, and the model is red-teamed before it reaches production.
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