Financial institutions in Africa and beyond face growing expectations from regulators, correspondent banks and the Financial Action Task Force that their anti-money laundering and counter-terrorist financing controls are risk-based and evidenced with data. Legacy monitoring systems often generate thousands of low-value alerts, hide genuine red flags and leave compliance teams unable to explain why a rule exists or whether it works.

Over five days, participants learn how to prepare and profile transaction, customer and screening data, translate typologies into scenarios, and measure and tune detection performance. The programme covers customer risk segmentation, threshold setting, sampling and validation, network and link analysis, and the careful use of machine learning for anomaly detection. It then follows an alert through triage, investigation, case documentation and reporting to the financial intelligence unit, and closes with management information and governance of the analytics function.

The teaching is led by practitioners and built on anonymised datasets that participants query and visualise themselves. Short briefings introduce each method, after which small groups apply it to a realistic case. Participants finish with a tested monitoring scenario, an alert-tuning plan and a reporting dashboard design that can be adapted to their own institution.

By the end of the programme, participants will be able to:

• Translate money laundering, terrorist financing and fraud typologies into measurable data-driven red flags.
• Prepare and profile transaction and customer data so that monitoring results can be trusted.
• Design, calibrate and document rule-based monitoring scenarios and thresholds.
• Evaluate alert quality using false-positive rates, below-the-line testing and sampling.
• Apply link and network analysis to identify related parties and beneficial ownership chains.
• Assess where machine learning adds value in detection and what governance it requires.
• Investigate alerts and draft clear suspicious transaction reports.
• Build dashboards that report monitoring performance to senior management and supervisors.

The programme is aimed at professionals who detect, investigate or oversee financial crime risk, including:

• AML and CFT compliance officers
• Money laundering reporting officers and their deputies
• Financial intelligence unit and law enforcement analysts
• Transaction monitoring and sanctions screening analysts
• Internal and external auditors reviewing compliance programmes
• Fraud and forensic investigators
• Risk managers in banks, insurers and mobile money providers
• Supervisors and regulators of financial institutions
• Data analysts supporting compliance teams

Participants leave with:

• A tested transaction monitoring scenario with documented logic, thresholds and rationale.
• An alert-tuning method they can repeat on their own institution's data.
• A typology-to-red-flag library covering common laundering and financing patterns.
• A template for investigation notes and suspicious transaction report narratives.
• A draft management information dashboard for the board and compliance committee.
• A prioritised plan for improving data quality and analytics in their compliance function.

Delivery relies on hands-on analysis rather than slides. The programme includes:

• Short practitioner briefings introducing each detection method
• Labs in Excel, SQL and Python on anonymised transaction datasets
• Small-group investigations of realistic financial crime cases
• Review of participants' own monitoring challenges in facilitated clinics
• A closing presentation of a scenario and dashboard design

Participants who attend the sessions and complete the practical labs are awarded a CPD-accredited Certificate of Completion from Vision Reach Global Consultancy.