Transaction Monitoring and Suspicious Activity Detection Training Course

Transaction Monitoring and Suspicious Activity Detection Training Course

Introduction

The global financial system faces a relentless challenge from Money Laundering and Terrorism Financing, with criminals continuously developing new and sophisticated Financial Crime Typologies. Financial Institutions (FIs) and Designated Non-Financial Businesses and Professions are on the frontline, mandated by strict Global Regulatory Standards to act as gatekeepers. The sheer volume and complexity of digital transactions, especially in the era of FinTech and Virtual Assets, have rendered traditional manual processes obsolete. An effective Anti-Money Laundering (AML) program hinges critically on a robust Transaction Monitoring (TM) system.

Transaction Monitoring and Suspicious Activity Detection Training Course addresses the urgent industry demand for skilled compliance professionals who can navigate this complexity. It provides an in-depth understanding of the Risk-Based Approach (RBA) to TM, moving beyond basic rule-sets to explore advanced techniques like Behavioral Analytics and Machine Learning (ML) to significantly reduce False Positives and enhance the detection of truly Suspicious Activity (SA). Mastery of TM and Suspicious Activity Report (SAR) filing is no longer just a regulatory requirement; it is an essential component of Reputational Risk management and maintaining the integrity of the financial ecosystem.

Course Duration

5 days

Course Objectives

Upon completion of this course, participants will be able to:

1. Grasp the fundamental role of Transaction Monitoring (TM) within the broader Anti-Money Laundering (AML) and Financial Crime Compliance (FCC) framework.
2. Define and differentiate key Money Laundering Typologies and Terrorism Financing methods relevant to modern financial services.
3. Apply the Risk-Based Approach (RBA) to establish appropriate TM parameters based on customer and geographic risk.
4. Analyze the components of an effective Know Your Customer (KYC) profile and its direct correlation to setting Monitoring Scenarios.
5. Design and Optimize effective TM Rulesets to minimize operational risk and reduce the volume of False Positives.
6. Evaluate the utility of Advanced Analytics in enhancing Suspicious Activity Detection.
7. Identify and interpret complex Red Flags across various financial products, channels, and High-Risk Jurisdictions.
8. Conduct a thorough and defensible Alert Investigation using a structured methodology and evidence-based decision-making.
9. Differentiate between unusual activity and a genuinely Suspicious Transaction warranting internal escalation.
10. Master the legal and procedural requirements for documenting and filing a high-quality Suspicious Activity Report (SAR) or Suspicious Transaction Report (STR).
11. Understand the importance of Model Governance and regular Scenario Tuning to ensure the TM system remains current and effective.
12. Comply with Global Regulatory Expectations and the consequences of compliance failure, including major Enforcement Actions.
13. Communicate effectively with Regulators and Law Enforcement regarding SAR submissions and subsequent requests for information.

Target Audience

1. AML/CFT Analysts and TM Specialists.
2. Compliance Officers and Managers.
3. Financial Crime Investigators and Case Managers.
4. Financial Intelligence Unit (FIU) Personnel.
5. Internal Audit and Risk Management Professionals.
6. Operations Managers overseeing transaction processing.
7. Data Scientists and IT Teams supporting AML Systems.
8. Front-Line Staff and Relationship Managers

Course Modules

Module 1: The Foundation of AML and Transaction Monitoring (TM)

• AML Global Framework.
• Money Laundering Stages.
• The Risk-Based Approach (RBA).
• Core TM Components.
• Case Study: Analysis of a major Regulatory Fine due to TM System Failures

Module 2: Designing and Optimizing Monitoring Scenarios

• Scenario Categories.
• Developing Effective Rules.
• Behavioral Analytics.
• Data Quality and Governance.
• Case Study: A Structuring scheme case illustrating how to fine-tune a simple velocity rule to catch subtle patterns.

Module 3: Leveraging Advanced Technology in Suspicious Activity Detection

• Introduction to AI/ML in AML
• Predictive and Reactive Monitoring.
• Graph Analytics.
• Model Validation and Tuning.
• Case Study: Using a Graph Analysis to uncover a complex Trade-Based Money Laundering (TBML) network hidden in a high volume of small transactions.

Module 4: Alert Management and Investigative Techniques

• Alert Triage and Prioritization.
• Investigative Due Diligence
• Creating a Robust Audit Trail.
• Escalation Criteria.
• Case Study: Investigating a Money Mule scenario across multiple alerts that initially appeared unrelated, focusing on the quality of the investigation narrative.

Module 5: Identifying and Analyzing High-Risk Typologies

• Terrorism Financing Red Flags.
• Corruption and Bribery Indicators.
• Cybercrime and Fraud.
• High-Risk Sectors.
• Case Study: A case involving a customer making large, round-sum payments inconsistent with their declared Source of Wealth (SoW), indicative of Corruption.

Module 6: Suspicious Activity Reporting (SAR/STR)

• The Decision to File.
• SAR/STR Filing Process.
• Constructing the Narrative.
• Legal Protections.
• Case Study: Reviewing a redacted, high-quality SAR to identify best practices for narrative, evidence summary, and ultimate conclusion.

Module 7: TM Governance, Audits, and Regulatory Interaction

• TM Program Governance.
• Independent Review and Audit.
• System Testing and Validation.
• Responding to Regulator Inquiries.
• Case Study: Reviewing an Audit Finding focused on the lack of proper Model Validation and the corrective actions required.

Module 8: Emerging Risks and The Future of TM

• FinTech and Payments Landscape.
• Virtual Assets and Blockchain.
• Global Sanctions Evasion.
• The Role of the Human Analyst.
• Case Study: A scenario involving an account showing unusual transfers to a newly-created wallet on an Unregulated Exchange, potentially linked to Sanctions Evasion.

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.