Segmentation is the process of dividing customers, accounts, or transactions into groups based on shared characteristics. In anti-money laundering (AML) compliance, segmentation allows institutions to apply targeted monitoring, risk assessment, and controls. For example, a retail banking customer with regular salary deposits may require less intensive monitoring than a corporate account with frequent cross-border payments.

Regulators encourage segmentation because it supports a risk-based approach. By applying differentiated monitoring to different customer or transaction groups, firms can allocate resources more effectively, reduce false positives, and focus on high-risk activity.

Definition Of Segmentation

Segmentation in AML refers to categorising data into meaningful groups for risk assessment, monitoring, and detection of suspicious behaviour. Common segmentation categories include:

• Customer type (retail, corporate, correspondent banking, VASP).

• Transaction behaviour (high-volume, cross-border, unusual frequency).

• Geography (domestic vs. high-risk jurisdictions).

• Product type (loans, securities, remittances, crypto-fiat on-ramps).

By segmenting customers and transactions, institutions create more accurate baselines of expected behaviour. This makes it easier to identify anomalies and apply enhanced due diligence where needed.

The FATF’s guidance for the banking sector clarifies that applying a risk-based approach allows institutions to focus enhanced measures on higher-risk areas and apply simpler controls where risk is lower, enabling practical segmentation across different customer and transaction categories.

Why Segmentation Matters In AML

Segmentation is more than just data grouping; it is a fundamental tool for aligning AML monitoring with regulatory expectations. Without segmentation, firms risk applying generic rules that either overwhelm investigators with false positives or miss genuinely suspicious activity.

Risk-Based Monitoring

Segmentation supports risk-based monitoring by ensuring high-risk groups (e.g., politically exposed persons, offshore structures) receive enhanced oversight.

Improved Detection Accuracy

By tailoring detection rules to customer or transaction segments, firms reduce noise and improve the accuracy of alerts.

Regulatory Compliance

Supervisors such as the FCA expect firms to apply differentiated monitoring that aligns with the varying risk profiles of customers and products. Firms are required to implement a risk-based approach, where monitoring intensity is proportional to the assessed risk.

Operational Efficiency

Segmentation allows compliance teams to prioritise resources on areas with the greatest financial crime risk.

Segmentation And Facctum Solutions

Facctum’s products support segmentation by enabling configurable, transparent monitoring across different risk groups:

• FacctGuard, Transaction Monitoring – applies rules and behavioural analytics by customer or transaction segment, enabling differentiated risk detection.

• FacctView, Customer Screening – screens customers with segment-specific thresholds and controls, reflecting varying risk levels.

• Alert Adjudication – ensures that alerts from different segments are escalated and investigated in consistent, auditable ways.

By embedding segmentation principles, Facctum ensures firms can align AML controls with regulator expectations for a risk-based approach.

Challenges In Implementing Segmentation

Segmentation offers strong benefits but also presents practical challenges for compliance teams.

Data Quality Issues

If customer or transaction data is incomplete or inaccurate, segmentation becomes unreliable.

Over-Segmentation

Creating too many micro-segments can dilute focus and overwhelm monitoring systems.

Regulatory Misalignment

If segmentation does not match regulator expectations, firms may still face compliance deficiencies.

Dynamic Risks

Criminal behaviour evolves, meaning segmentation models must be recalibrated frequently.

Best Practices For Segmentation In AML

To maximise its value, segmentation should be applied with discipline and transparency.

• Use A Risk-Based Framework: Group customers and transactions according to inherent financial crime risks.

• Calibrate Regularly: Review and adjust segmentation logic as risks and behaviours change.

• Ensure Data Accuracy: Maintain clean, reliable customer and transaction data.

• Integrate Governance: Align segmentation changes with governance and audit trails.

• Link To Monitoring: Apply differentiated rules and scenarios through platforms like FacctGuard, Transaction Monitoring.

The Future Of Segmentation In AML

Segmentation is evolving alongside advances in data and AI-driven analytics. Rather than relying only on static categories, future segmentation will combine dynamic and behavioural elements.

• AI-Enhanced Segmentation: Machine learning will identify new, hidden risk clusters.

• Dynamic Recalibration: Segments will adjust automatically as customer behaviours shift.

• Explainable Segmentation: Regulators will require transparency in how customers and transactions are grouped.

• Global Alignment: International regulators are converging around risk-based segmentation frameworks.

Institutions that embed advanced segmentation into AML monitoring will be better placed to reduce false positives and satisfy regulatory scrutiny.