Transaction patterns describe the recurring behaviours, flows, or characteristics of financial transactions. In anti-money laundering (AML) compliance, recognising patterns is critical to identifying suspicious activity, such as structuring deposits, unusual cross-border transfers, or repeated payments just below reporting thresholds.

By monitoring transaction patterns, financial institutions can detect red flags that may indicate money laundering, terrorist financing, or sanctions evasion. Regulators expect firms to incorporate behavioural analysis into their AML frameworks, making transaction patterns a cornerstone of compliance.

Definition Of Transaction Patterns

A transaction pattern is a consistent or recognisable set of behaviours in payment or account activity. Patterns may reflect:

• Normal customer behaviour (e.g., monthly salary deposits followed by routine bill payments).

• High-risk behaviour (e.g., rapid transfers through multiple accounts to obscure origins).

• Suspicious structuring (e.g., multiple deposits just below reporting thresholds).

Regulators such as the FATF highlight the importance of detecting unusual transaction flows as part of the risk-based approach to AML

How Transaction Patterns Are Used In AML Detection

Customer Risk Profiling

Baseline transaction patterns establish what “normal” looks like for a given customer. Deviations from this baseline can trigger enhanced monitoring.

Suspicious Activity Monitoring

Patterns such as rapid fund movement, sudden increases in transaction size, or frequent transfers to high-risk jurisdictions often indicate potential money laundering.

Sanctions Risk Identification

Patterns involving payments linked to sanctioned entities or flagged jurisdictions must be detected and blocked. FacctShield, Payment Screening provides this real-time protection.

Behavioural Analytics

Advanced monitoring tools apply statistical and machine learning models to identify patterns across accounts, highlighting anomalies that may evade rule-based detection.

Transaction Patterns And Facctum Solutions

Facctum products integrate transaction pattern analysis into AML workflows:

• FacctGuard, Transaction Monitoring – applies configurable rules and behavioural analytics to detect suspicious payment flows and unusual customer activity.

• FacctShield, Payment Screening – screens individual payments in real time, blocking transactions that match sanctions or prohibited activity patterns.

• Alert Adjudication – ensures alerts triggered by suspicious patterns are reviewed consistently, with clear audit trails.

These tools ensure institutions can both identify risky transaction patterns and manage alerts efficiently.

Challenges In Monitoring Transaction Patterns

Data Quality

Poor data integrity can obscure genuine patterns, leading to missed risks or false positives.

False Positives

Overly rigid rules can generate alerts for benign behaviours, overwhelming compliance teams. Studies suggest 90–95% of alerts in AML systems are false positives

Cross-Border Complexity

Global transactions often follow different norms in different jurisdictions, making it harder to distinguish normal from suspicious activity.

Evolving Criminal Techniques

Criminals adapt quickly, creating new layering and structuring strategies to evade detection.

Best Practices For Analysing Transaction Patterns

• Use Risk-Based Rules: Focus on patterns most associated with laundering typologies.

• Integrate Behavioural Analytics: Combine statistical analysis with configurable rules.

• Leverage High-Quality Data: Maintain accurate and standardised transaction data.

• Review And Calibrate Regularly: Update thresholds and scenarios as risks evolve.

• Align With Governance: Use platforms like Alert Adjudication to ensure all alerts are consistently reviewed and documented.

The Future Of Transaction Pattern Analysis

• AI and Machine Learning: Advanced models will detect hidden, non-obvious patterns across large datasets.

• Explainability Requirements: Regulators will require firms to justify why certain patterns trigger alerts, not just rely on black-box models.

• Real-Time Monitoring: Instant analysis of transaction flows will become standard in both banking and payments.

• Integration With Cybersecurity: Criminal patterns increasingly overlap with cyber-enabled fraud, requiring joined-up monitoring.

Firms that prioritise explainable, real-time detection of transaction patterns will be best positioned to meet regulatory expectations.