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Multi-Agent AML Case Triage and Investigation System with AutoGen

Anti-Money Laundering (AML) investigators face overwhelming backlogs of false-positive transaction alerts from legacy monitoring systems. Build a multi-agent conversational investigation team using AutoGen to synthesize transaction histories, cross-reference KYC records, and generate audit-ready SAR reports. Reduce investigator manual triage time by 60% while maintaining 100% regulatory audit compliance.

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What you are building

Build a multi-agent AML case triage system using AutoGen to coordinate specialized investigator agents and automate Suspicious Activity Report drafting.

How work is evaluated

Evaluation

Evaluates multi-agent conversation flow, SAR narrative generation quality, and false-positive dismissal accuracy.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

structuring_detection_test

Ensures sub-$10k repeat deposits are flagged as SAR_REQUIRED

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

investigation_accuracy

Accuracy of case dispositions across ground truth test set • target: 0.98 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Construct multi-agent AutoGen conversations featuring Data Retriever, KYC Reviewer, and Lead Investigator

  • Synthesize structured bank transaction logs with historical account behavior

  • Automate generation of compliant Suspicious Activity Reports (SAR) narratives

  • Incorporate Human-in-the-Loop review gates for final SAR sign-off

Resources

Dataset notes

Dataset of 200 transaction alert cases with associated KYC history, including false positives and structuring patterns.

Challenge setup

Evaluates multi-agent conversation flow, SAR narrative generation quality, and false-positive dismissal accuracy.

Challenge input

JSON transaction alert record including account history and counterparty details

AutoGen

Microsoft conversational multi-agent framework

Evaluated output

JSON containing case_disposition (SAR_REQUIRED or FALSE_POSITIVE), narrative_text, and audit_trail

Evaluation checks
  • Ensures sub-$10k repeat deposits are flagged as SAR_REQUIRED
  • Accuracy of case dispositions across ground truth test set • target: 0.98 • range: 0-1
Configured evaluation evidence
  • Benchmark: AMLCase-Eval
  • Investigation Accuracy target: 0.98
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • AutoGen · Required
  • Microsoft AutoGen · Optional

Evaluation contract

  • structuring_detection_test · Weight 1
  • investigation_accuracy · Weight 1

Metrics

  • investigation_accuracy · Target 0.98

Benchmarks

  • AMLCase-Eval
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON transaction alert record including account history and counterparty details

    Output: JSON containing case_disposition (SAR_REQUIRED or FALSE_POSITIVE), narrative_text, and audit_trail

    Execution harness: Python

Evaluation tools

  • AutoGen · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Multi-Agent AML Case Triage and Investigation System with AutoGen