---
name: al-182-prep-an-anti-money-laundering-alert-review-with-every-sou
description: "Have an agent pull together the evidence for a suspicious-activity alert, compare it to what is normal for that customer, and draft the case summary so the analyst only has to decide. FIS made its AML Investigations Agent, built with Anthropic, available to US banks; at design partner Amalgamated Bank it is projected to cut first-level alert review time by more than 90% and SAR narrative prep by more than 80%, and no case can be closed without an analyst confirming it. Use when the user wants help with aml investigations (legal & compliance) for a small business."
---

# Prep an anti-money-laundering alert review with every source linked

Have an agent pull together the evidence for a suspicious-activity alert, compare it to what is normal for that customer, and draft the case summary so the analyst only has to decide. FIS made its AML Investigations Agent, built with Anthropic, available to US banks; at design partner Amalgamated Bank it is projected to cut first-level alert review time by more than 90% and SAR narrative prep by more than 80%, and no case can be closed without an analyst confirming it.

## Inputs to collect first
Ask the user for anything below that they haven't already given you. Don't guess missing facts.

- Customer type, business or occupation, how long they've banked with…
- Alert type, date, rule that fired
- The flagged transactions plus the last [3/6

## Instructions

Do the task below with the user's inputs in place of each [bracket].

You are helping me, a compliance analyst, prepare a review of one transaction-monitoring alert. Customer profile: [customer type, business or occupation, how long they've banked with us, normal monthly activity]. Alert: [alert type, date, rule that fired]. Transactions: [paste the flagged transactions plus the last [3/6] months for comparison]. Step 1: describe what is normal for this customer and how the flagged activity differs, e.g. a seasonal cash spike versus deposits kept just under reporting thresholds. Step 2: list every fact you rely on with the exact transaction or document it came from. Step 3: list the red flags and the innocent explanations side by side. Step 4: suggest one of: close with rationale, escalate for deeper review, or consider a SAR, and draft a short neutral narrative. Use only the data I gave you, mark anything missing as 'needs info', and remember the final decision is mine.

## Safety checks (share these with the user)

- Check every figure, price and total against your own records before anything is sent, posted or booked. A person approves refunds, discounts, payments and price changes.
- Anything legal, tax or compliance-related is a first draft for a qualified person to check. It is not professional advice, and clause references and deadlines must be checked against the source.
- Remove names, contact details, account numbers and other personal data the task doesn't need before you paste, and check your AI tool's data and training settings.
- Check names, dates, numbers and any claim against your own source material before you use the answer. AI can misread or invent details.
- Say clearly which parts need a person to review or approve before anything is sent, paid, posted or filed.

## About this skill

- Works with: Claude, ChatGPT, FIS AML Investigations Agent
- Based on this real-world example: https://www.investor.fisglobal.com/news-releases/news-release-details/fis-aml-investigations-agent-now-commercially-available-help/
- Free prompt from AgentPlaybook Real Estate: https://agentic-library.netlify.app/prompt/al-182-prep-an-anti-money-laundering-alert-review-with-every-source/
- To install in an agent that supports Agent Skills, save this file as SKILL.md in a folder with the same name as `name` above (for example in your skills directory). Install steps for Claude.ai, Claude Code, Cursor and Codex: https://agentic-library.netlify.app/skills/