AML Transaction Pattern Analyzer
Identify suspicious transaction patterns and build evidence-backed AML reports
What You Can Do
You can analyze transaction clusters to identify layering, placement, and integration schemes using proven AML methodologies. Claude applies typology frameworks to suspicious fund flows, cross-border movements, and beneficiary ownership chains—transforming raw transaction flags into regulatory-grade narratives that justify SAR escalation and withstand compliance audits.
Features
Categorize transactions by money laundering methodology (structuring, trade-based laundering, trade finance abuse, circular flows, shell company networks)
Apply statistical and behavioral indicators (velocity, round-tripping, threshold avoidance, unusual party combinations) to flag high-risk sequences
Build evidence-backed Suspicious Activity Report narratives with documented reasoning, timeline analysis, and regulatory-grade justification
Trace complex fund flows and beneficial ownership chains to identify shell company networks and layering schemes
Reconstruct fund movement patterns across accounts, parties, and geographies to establish placement, layering, and integration components
Generate structured audit-ready analysis with citation of transaction evidence, regulatory frameworks, and analytical methodology
Apply multi-indicator confirmation (behavioral, structural, contextual) before escalating alerts to reduce false positives
Example Output
Example 1: Structuring Detection
Input: 45 days of transactions from a single account totaling $987,500 in deposits
Output:
- Pattern identified: 23 deposits between $40,000–$49,500, strategically timed to avoid $50,000 threshold
- Typology: Classic structuring (smurfing) consistent with placement phase
- Quantitative indicator: 94% of deposits cluster in $40K–$49.5K band (probability of random occurrence: <0.1%)
- SAR narrative: "Account demonstrates deliberate structuring behavior with deposits consistently maintained below currency reporting thresholds despite account holder's stated business averaging $150K monthly revenue."
Example 2: Trade-Based Laundering
Input: 12 import invoices and corresponding fund transfers over 8 months
Output:
- Pattern identified: Over-invoicing on commodity imports from high-risk jurisdiction with rapid re-export at 40% loss
- Typology: Trade-based laundering (over-invoicing + phantom shipment indicators)
- Evidence chain: Invoice markup 380% above market rate → Goods shipped to unrelated third party → Proceeds transferred to offshore entity
- Quantitative risk score: 8.7/10 based on jurisdiction risk, pricing anomalies, and beneficiary opacity
- SAR recommendation: Escalate with supply chain documentation and comparative market pricing analysis
What's Included
- SKILL.md: Complete instruction file with AML methodologies, typology classification framework, and SAR documentation standards
- Transaction Pattern Checklist: 40-point assessment tool for classifying layering, placement, and integration indicators
- SAR Narrative Template: Structured format for building compliance-grade Suspicious Activity Reports with evidence sections
- AML Typology Matrix: Reference guide mapping money laundering schemes (structuring, trade-based laundering, trade finance abuse, circular flows, shell company networks) to detection indicators
- Quantitative Indicator Benchmarks: Statistical thresholds for transaction velocity, threshold avoidance, round-tripping, and unusual party combinations by transaction type
Who It's For
- Forensic Accountants — Investigating transaction anomalies and building evidence for regulatory escalation
- AML Compliance Officers — Validating transaction monitoring alerts and constructing defensible SAR documentation
- Financial Crime Investigators — Analyzing suspicious fund flows and mapping beneficiary ownership networks
- Compliance Managers — Training teams on typology recognition and pattern analysis methodologies
- Internal Audit Teams — Reviewing transaction sequences during regulatory examinations and compliance assessments
Best For
- Analyzing multi-transaction clusters (10+ transactions) for layering, placement, and integration schemes
- Building Suspicious Activity Report narratives with quantitative and structural evidence
- Detecting structuring, trade-based laundering, and trade finance abuse patterns
- Mapping cross-border fund flows and beneficiary ownership chains across complex networks
- Validating and escalating transaction monitoring system alerts before regulatory submission
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