Introduction

The traditional methods of transaction monitoring, heavily reliant on rule-based systems, are proving increasingly inadequate in the face of sophisticated financial crime. They often generate high volumes of false positives and can be slow to adapt to new criminal behaviors. AI-powered transaction monitoring represents a significant leap forward, using machine learning and behavioral analytics to identify suspicious activities with greater accuracy and efficiency. This Intelligent Oversight: Training Course on AI-Powered Transaction Monitoring is a 10-day comprehensive program designed for professionals seeking to lead this technological transformation within their organizations.

This course will guide you through the fundamental principles of AI and its practical application in combating financial crime, including money laundering and terrorism financing. You will learn how to design, build, and deploy AI models that can detect anomalies and patterns that traditional systems miss. By the end of this training, you will be equipped to not only enhance your organization's compliance posture but also to optimize its operational efficiency, freeing up valuable resources to focus on true threats.

Duration: 10 Days

Target Audience

• Financial compliance officers
• Anti-Money Laundering (AML) investigators
• Fraud prevention specialists
• Data scientists and machine learning engineers
• Risk management professionals
• Auditors and internal control staff
• FinTech product managers
• Banking operations managers
• Regulatory affairs specialists
• Anyone involved in financial crime prevention

Objectives

• Understand the limitations of traditional rule-based systems.
• Explore the different types of AI and their use in monitoring.
• Learn how to prepare and handle large-scale financial data.
• Build and train machine learning models for anomaly detection.
• Apply behavioral analytics to identify unusual transaction patterns.
• Understand the ethical and regulatory challenges of using AI.
• Interpret and explain AI model outputs for regulatory reporting.
• Implement a robust governance framework for AI in compliance.
• Stay updated on the latest trends in AI and financial crime.
• Develop a strategic roadmap for AI adoption in your organization.

Course Modules

Module 1: The Evolution of Transaction Monitoring

• The history of financial crime and regulation
• The shortcomings of traditional rule-based systems
• The rise of data science in compliance
• Introduction to the challenges of false positives and negatives
• The business case for an AI-driven approach

Module 2: Fundamentals of AI and Machine Learning

• A non-technical introduction to AI concepts
• Supervised vs. unsupervised learning
• Key machine learning algorithms for anomaly detection
• The importance of data and feature engineering
• Understanding model performance metrics

Module 3: Data Preparation for AI Models

• Sourcing and gathering relevant financial data
• Data cleaning, normalization, and transformation
• Handling imbalanced datasets in financial crime
• Feature engineering from raw transaction data
• The role of unstructured data (e.g., text)

Module 4: Rule-Based vs. AI-Driven Systems

• The architecture of traditional monitoring systems
• How to migrate from rules to models
• Combining rules and AI for a hybrid approach
• The benefits of a dynamic, adaptive system
• Measuring the performance uplift of an AI model

Module 5: Anomaly Detection Techniques

• Statistical methods for outlier detection
• Clustering algorithms (e.g., K-Means)
• Using Isolation Forests to find anomalies
• The role of autoencoders for anomaly detection
• Developing a comprehensive anomaly scoring system

Module 6: Behavioral Analytics and Profiling

• Building customer risk profiles
• Identifying deviations from normal behavior
• Using time-series analysis for transaction patterns
• Network analysis to uncover hidden relationships
• Applying graph databases for relationship mapping

Module 7: Explainable AI (XAI) for Compliance

• Why model interpretability is critical in finance
• Techniques for explaining model predictions
• Generating clear and concise reports for auditors
• The importance of model transparency and fairness
• The future of explainable AI

Module 8: Ethical and Regulatory Considerations

• The risk of bias in AI models
• Ensuring data privacy and security
• Regulatory guidance on AI in financial services
• The role of governance and model validation
• Designing a framework for ethical AI deployment

Module 9: Implementation and Deployment

• The technology stack for AI-powered monitoring
• Building a scalable and reliable architecture
• Deploying models in a production environment
• Continuous monitoring and model maintenance
• Integrating with existing compliance systems

Module 10: Case Studies and Scenarios

• Analysis of a real-world money laundering case
• Using AI to detect terrorist financing
• A case study on trade-based money laundering
• Detecting fraud in real-time
• The impact of AI on investigative workflows

Module 11: Future of Financial Crime Fighting

• The role of federated learning in compliance
• The use of AI in combating crypto crime
• The challenge of synthetic data generation
• The evolving role of the compliance officer
• The convergence of AI and digital identity

Module 12: Capstone Project Part 1: Design

• Defining a financial crime detection problem
• Designing a data collection and preparation pipeline
• Selecting and justifying the AI model
• Planning the implementation and deployment
• Outlining the project's success metrics

Module 13: Capstone Project Part 2: Build

• Building a prototype of the AI model
• Training the model on a simulated dataset
• Fine-tuning hyperparameters for optimal performance
• Evaluating the model's accuracy and efficiency
• Preparing the final model for presentation

Module 14: Capstone Project Part 3: Present & Discuss

• Presenting the final AI model and its findings
• Explaining the model's decisions to a non-technical audience
• Discussing the challenges and limitations faced
• Q&A and peer feedback session
• Receiving expert recommendations for improvement

Module 15: Course Certification

• A final comprehensive assessment
• A final review of key concepts and objectives
• Issuance of a certificate of completion
• Post-course career guidance and networking
• A final Q&A with instructors

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.