Detecting Structuring Patterns: AI vs Rule-Based Approaches

What is Structuring?

Structuring (also called smurfing) is the practice of breaking large transactions into smaller amounts to evade currency transaction reporting (CTR) requirements. In the US, transactions over $10,000 must be reported to FinCEN. Criminals structure deposits and withdrawals into amounts just below this threshold to avoid detection.

Traditional Rule-Based Detection

Legacy AML systems use simple threshold-based rules:

Common Rules

• Amount Range: Flag transactions between $9,000-$9,999
• Frequency: Multiple transactions in this range within 24 hours
• Cumulative Amount: Total deposits/withdrawals over $10,000 in a day
• Round Numbers: Amounts like $9,500, $9,900 that look deliberately chosen

Why Rules Fail

Rule-based approaches suffer from fundamental limitations:

• High False Positives: Legitimate businesses (restaurants, retail) often have transactions in the $9K-$10K range
• Easy to Evade: Once criminals know the rules, they structure around them ($8,500 instead of $9,500)
• No Context: Rules don't consider the entity's normal behavior patterns
• Rigid Thresholds: Same rules applied to college students and real estate investors

AI-Powered Structuring Detection

Machine learning models learn what structuring looks like from historical data, adapting to new schemes automatically. Our approach combines multiple techniques:

1. Behavioral Baselines

Instead of fixed thresholds, we model each entity's normal transaction behavior:

• Personal Thresholds: What's unusual for THIS customer?
• Velocity Anomalies: Comparing short-term to long-term transaction patterns
• Amount Distribution: Does the customer typically make transactions in this range?
• Peer Comparison: How does this behavior compare to similar customers?

2. Temporal Pattern Recognition

LSTM models analyze transaction sequences to detect structuring patterns:

• Time Gaps: Rapid succession of similar amounts (minutes apart) vs spread over days
• Amount Progression: Decreasing amounts ($9,900, $9,800, $9,700) suggest intentional structuring
• Channel Mixing: Using different branches/ATMs/channels to avoid detection
• Coordination: Multiple accounts transacting in synchronized patterns

3. Network Analysis

Graph Neural Networks identify coordinated structuring across multiple accounts:

• Smurfing Networks: Multiple "mule" accounts receiving funds then making structured deposits
• Aggregation Points: Money flowing from many sources to few destinations
• Circular Flows: Funds cycling through accounts in structured amounts
• Shared Attributes: Same IP address, device ID, or physical location across accounts

Performance Comparison

Real-World Case Studies

Case 1: Retail Business False Positive

Case 2: Coordinated Smurfing Network

Implementation Recommendations

Transitioning from rule-based to AI-powered structuring detection:

1. Parallel Running: Run AI system alongside existing rules for 90 days
2. Performance Comparison: Measure false positive reduction and catch rate improvement
3. Tuning Period: Adjust sensitivity based on analyst feedback
4. Phased Rollout: Start with low-risk segments, expand to high-risk
5. Rule Retirement: Gradually disable redundant rules as AI proves itself

Continuous Improvement

Structuring schemes evolve. Our AI system adapts:

• Weekly Retraining: Models updated with latest confirmed structuring cases
• Feedback Loop: Analyst decisions (true vs false positive) improve model
• Emerging Pattern Detection: Unsupervised clustering identifies new structuring typologies
• Threshold Evolution: As criminals adapt (e.g., using $8K instead of $9K), model adjusts

Conclusion

The numbers speak for themselves: AI-powered structuring detection reduces false positives by 87% while catching 71% more true structuring schemes. For financial institutions drowning in false alerts, the shift to behavioral AI is not just beneficial—it's essential.

At nerous.ai, we combine the ingenuity our Finnish name suggests with proven machine learning techniques to detect structuring with unprecedented accuracy. The result: compliance teams spend their time investigating real crime, not chasing false positives.