AML Investigation

AML Investigation Engine Overview

The AML (Anti-Money Laundering) Investigation Engine is an agentic workflow that investigates transaction data for potential money laundering patterns. It analyzes alerts against your defined Standard Operating Procedures (SOPs), performs web research, and queries data warehouses to gather evidence and produce a structured investigation result.

Key Capabilities

Policy-Driven Analysis: Investigates transactions against your AML SOP guidelines
Automated Evidence Collection: Uses web research and SQL queries to gather evidence
Category-by-Category Analysis: Evaluates red flags and green flags per SOP category
Structured Verdicts: Produces investigation results with clear verdicts from your policy dispositions

AML Investigation Engine Inputs

The AML Investigation Engine Configuration has four parameters:

Parameter	Required	Description
policy_version_id	Yes	The Policy version ID containing your AML investigation guidelines (SOP)
context_sources	No	Data sources (e.g., Snowflake) for the agent to query transaction data during investigation
alert_data	Yes	Alert data that triggered this investigation (JSON string with alert details)
transaction_data	No	Pre-loaded transaction data (JSON string). If empty, the agent will fetch from context sources

See Template Strings for dynamic parameter configuration.

AML Investigation Output

The output is a structured JSON object containing:

Policy Applied

Information about the policy used for investigation:

name: Name of the policy applied
version: Version ID of the policy applied

AML Analysis

Complete investigation result including:

Field	Description
`verdict`	Final verdict from policy dispositions (e.g., “Clear - False Positive”, “File STR”)
`summary`	Executive summary explaining the conclusion and key findings
`category_analysis`	Detailed analysis for each category in the policy guidelines
`out_of_scope_flags`	Additional findings not covered by the defined guidelines
`reason_for_output`	Brief explanation of the thought process behind the verdict

Category Analysis Structure

For each SOP category, the output includes:

category_title: Title of the category
summary: 2-3 sentence summary of findings
red_flag_analysis: Array of RED_FLAG rules with hit status and evidence
green_flag_analysis: Array of GREEN_FLAG rules with hit status and evidence

Evidence Structure

Each piece of evidence includes:

evidence_type: “web_link”, “screenshot”, or “artifact”
evidence_data: URL or artifact reference
evidence_name: Short descriptive name
evidence_description: Why this evidence is relevant

Policy (SOP) Structure

Your AML policy should contain:

Instructions: High-level data exploration and investigation guidance
Guidelines: Organized as Categories > Rules
- Each Category (e.g., “BLACKLISTED_BARCODE_CHECK”, “DAILY_TRANSACTION_COUNT_CHECK”) contains multiple Rules
- Each Rule has a flag type (GREEN_FLAG or RED_FLAG), title, and description
Dispositions: Classification options for final verdicts

Supported Context Sources

The AML Investigation Engine supports the following data source connectors:

Connector	Type	Description
Snowflake	SQL	Query your Snowflake data warehouse for transaction history, customer data, etc.
Roe Tables	SQL	Query tables stored in VolansDB for internal data analysis
Zendesk	API	Fetch support tickets and customer communication history

Configure context sources to allow the agent to automatically fetch transaction data during investigation. The agent will use natural language to generate appropriate SQL queries or API calls.

Creating an AML Policy

The AML Investigation Engine requires a policy containing your Standard Operating Procedures (SOPs). You can create policies using the Policies feature.

A pre-built AML Investigation Workflow policy template is available in the platform. This template provides a comprehensive framework for alert review, typology assessment, evidence synthesis, and regulatory reporting decisions.

Policy Template Structure

The AML policy template includes:

Alert Intake and Classification: Initial steps for processing alerts
Customer Due Diligence Review: KYC verification and customer profile analysis
Transaction Pattern Analysis: Suspicious activity indicators
Web Research Guidance: Entity verification and adverse media screening
Disposition Classifications: Clear verdict options (e.g., “Clear - False Positive”, “File STR”, “Escalate for Review”)

Example Alert Data

{
  "alert_id": "ALT-2024-001234",
  "merchant_id": "MER-789456",
  "merchant_name": "ABC Trading Co.",
  "alert_type": "HIGH_VOLUME_TRANSACTIONS",
  "alert_date": "2024-01-15",
  "triggered_by": "Daily transaction count exceeded threshold",
  "transaction_summary": {
    "total_transactions": 450,
    "total_amount": 125000.00,
    "average_amount": 277.78
  }
}

Example Output

{
  "policy_applied": {
    "name": "AML Investigation SOP v2",
    "version": "pol_ver_abc123"
  },
  "aml_analysis": {
    "verdict": "Clear - Continue Monitoring",
    "summary": "Investigation found high transaction volume consistent with seasonal business patterns. No indicators of structuring or suspicious counterparties identified.",
    "category_analysis": [
      {
        "category_title": "TRANSACTION_PATTERN_CHECK",
        "summary": "Transaction patterns show legitimate business activity with seasonal variation.",
        "red_flag_analysis": [
          {
            "rule_title": "Round Amount Detection",
            "hit": false,
            "evidences": []
          }
        ],
        "green_flag_analysis": [
          {
            "rule_title": "Consistent Business Hours",
            "hit": true,
            "evidences": [
              {
                "evidence_type": "artifact",
                "evidence_data": "AMLInvestigationEngine-123/sql_query_1234567.json",
                "evidence_name": "Transaction timing analysis",
                "evidence_description": "95% of transactions occur during business hours 9am-6pm"
              }
            ]
          }
        ]
      }
    ],
    "out_of_scope_flags": [],
    "reason_for_output": "I analyzed the merchant's transaction patterns and found them consistent with their stated business type. The high volume is explained by seasonal factors and no structuring patterns were detected."
  }
}

Common Verdict Classifications

Define disposition classifications in your policy that match your compliance requirements. Common examples include:

Verdict	Description
Clear - False Positive	Alert reviewed, no suspicious activity identified
Clear - Continue Monitoring	No immediate concern, but enhanced monitoring recommended
Hold - Pending Documentation	Additional documentation needed before final decision
Escalate for Review	Requires senior analyst or compliance officer review
File STR	Suspicious Transaction Report should be filed
Temporary Suspension + Escalation	Account suspended pending further investigation

Use Cases

The AML Investigation Engine is designed for:

Transaction Monitoring Alerts: Investigate alerts from TMS systems for unusual transaction patterns
Sanctions Screening: Verify potential matches against sanctions lists
PEP Identification: Investigate politically exposed person alerts
Adverse Media Monitoring: Assess negative news coverage about customers or counterparties
Periodic Customer Reviews: Conduct scheduled reviews of high-risk customers
Referrals from Other Teams: Investigate escalations from frontline staff

Best Practices

Define Clear SOPs

Create comprehensive policies with specific red and green flag rules for each category you want to investigate

Connect Data Sources

Configure SQL context sources to allow the agent to query historical transaction data automatically

Structure Alert Data

Provide well-structured alert data with all relevant identifiers for accurate investigation

Review Dispositions

Define clear disposition classifications in your policy for consistent verdict assignment

Policies

Learn how to create and manage AML investigation policies

Data Connectors

Set up Snowflake and other data source connections

Get Started

Agents

VolansDB

Data Resources

Policies

Webhooks

Enterprise

Use Cases

AML Investigation Engine Overview

Key Capabilities

AML Investigation Engine Inputs

AML Investigation Output

Policy Applied

AML Analysis

Category Analysis Structure

Evidence Structure

Policy (SOP) Structure

Supported Context Sources

Creating an AML Policy

Policy Template Structure

Example Alert Data

Example Output

Common Verdict Classifications

Use Cases

Best Practices

Define Clear SOPs

Connect Data Sources

Structure Alert Data

Review Dispositions

Policies

Data Connectors

Get Started

Agents

VolansDB

Data Resources

Policies

Webhooks

Enterprise

Use Cases

​AML Investigation Engine Overview

​Key Capabilities

​AML Investigation Engine Inputs

​AML Investigation Output

​Policy Applied

​AML Analysis

​Category Analysis Structure

​Evidence Structure

​Policy (SOP) Structure

​Supported Context Sources

​Creating an AML Policy

​Policy Template Structure

​Example Alert Data

​Example Output

​Common Verdict Classifications

​Use Cases

​Best Practices

Define Clear SOPs

Connect Data Sources

Structure Alert Data

Review Dispositions

​Related Resources

Policies

Data Connectors

AML Investigation Engine Overview

Key Capabilities

AML Investigation Engine Inputs

AML Investigation Output

Policy Applied

AML Analysis

Category Analysis Structure

Evidence Structure

Policy (SOP) Structure

Supported Context Sources

Creating an AML Policy

Policy Template Structure

Example Alert Data

Example Output

Common Verdict Classifications

Use Cases

Best Practices

Related Resources