# AML case evidence assembly

Source: https://altivusops.ai/use-cases/aml-case-evidence-assembly/
Last modified: 2026-10-13

> Assembles transaction, account and counterparty data when a case opens and ranks activity against known laundering typologies. An investigator reviews high-risk cases and decides on filing.

An AI workflow Blueprint — a documented use case for Compliance in Financial Services. Complexity: Advanced. Deployment: HeyQuinn recommended.

## The problem

Anti money laundering investigations are among the most labor intensive workflows in banking. Investigators manually assemble evidence across fragmented core systems before they can evaluate a single case, which limits how many cases a team can review without adding headcount.

- Evidence sits scattered across transaction records, account history and counterparty data
- Investigators spend hours assembling a case before any judgment can begin
- Typology matching depends on individual investigator experience and memory
- Case volume is capped by how many investigators are available, not by risk

## The solution

A financial crimes workflow assembles the full evidence package the moment a case opens and evaluates it against established typologies, while investigators keep authority over every filing decision.

1. Opens the case and pulls transaction records, account history and counterparty data from core banking systems
2. Evaluates the assembled activity against known money laundering typologies
3. Ranks cases by risk and queues the highest risk cases for investigator review
4. Attaches the full evidence package with every conclusion linked back to source data
5. Routes the case to an investigator for the final decision on filing or closing

## Where people stay in control

- An investigator retains final decision authority on every case
- The agent does not file suspicious activity reports or close cases on its own
- Every conclusion links traceably back to source data within the governance layer

## Systems and data

The workflow reads transaction records, account history and counterparty data from a bank's core systems, whether vendor managed or bank owned, and writes ranked case evidence packages into the investigation queue for review.

- core banking system
- transaction records

## Why HeyQuinn is recommended

The workflow touches regulated financial data across several core banking systems and requires a governance and audit layer that traces every conclusion to source data, so HeyQuinn is recommended to manage permissions and monitoring.

[What HeyQuinn is](https://altivusops.ai/heyquinn/)

## Frequently asked questions

### Does AI file suspicious activity reports on its own?

No. The agent assembles evidence and ranks risk, but an investigator makes every filing or case closing decision.

### What systems does the workflow connect to?

It connects to a bank's core banking systems, whether managed by the vendor or owned by the bank, to pull transaction, account and counterparty data.

### Can every conclusion be traced back to its source?

Yes. Every agent conclusion links to the underlying source data within the governance layer.

## Related Blueprints

- [Support conversation resolution and escalation](https://altivusops.ai/use-cases/support-conversation-resolution-and-escalation/)
- [Chargeback claim translation and analysis](https://altivusops.ai/use-cases/chargeback-claim-translation-and-analysis/)
- [Vendor contract review assistant](https://altivusops.ai/use-cases/vendor-contract-review-assistant/)

[Discuss this workflow](https://altivusops.ai/strategy-call/?workflow=aml-case-evidence-assembly)

[Back to the Blueprint library](https://altivusops.ai/use-cases/)

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