AI-Powered AML Compliance, Transaction Monitoring and Financial Crime Analytics Workshop

Course overview

The AI-Powered AML Compliance, Transaction Monitoring and Financial Crime Analytics Workshop equips banking professionals, compliance officers and financial crime investigators with practical skills to leverage artificial intelligence, machine learning and data analytics to detect suspicious transactions, identify money laundering patterns and strengthen regulatory compliance. The workshop covers Anti-Money Laundering (AML) frameworks, Know Your Customer (KYC), customer risk scoring, AI-driven transaction monitoring, anomaly detection, sanctions screening, fraud analytics, suspicious activity investigation and regulatory reporting. Participants explore how machine learning models, network analytics and automated monitoring systems can improve detection accuracy, reduce false positives and support risk-based compliance decisions while maintaining explainability, data privacy and human oversight.

The AI-Powered AML Compliance, Transaction Monitoring and Financial Crime Analytics Workshop equips banking and compliance professionals with practical skills in AI-driven anti-money laundering, suspicious transaction detection, customer risk scoring, fraud analytics and regulatory reporting. Participants learn to apply machine learning, anomaly detection, network analysis and Power BI dashboards to strengthen financial crime investigations, improve transaction monitoring and support risk-based AML compliance.

Course objectives

• Apply AML, KYC and customer due diligence principles to identify money laundering risks and strengthen financial crime compliance.
• Evaluate AI-driven AML solutions, explain model outputs, strengthen governance and produce evidence-based investigation findings aligned with applicable regulatory requirements.
• Apply financial crime analytics, link analysis and network visualisation to investigate suspicious transactions, relationships and complex money laundering patterns.
• Develop risk-based transaction monitoring and customer profiling approaches to prioritise alerts and reduce false-positive investigations.
• Analyse transaction data using AI, machine learning and statistical techniques to identify suspicious patterns, anomalies and potential financial crime indicators.

Who should attend

Course content

• Module 1: Foundations of AML Compliance and Financial Crime Prevention Money laundering concepts and stages, terrorist financing risks, proliferation financing awareness, predicate offences, financial crime typologies, risk-based AML compliance, internati
• Module 2: Customer Due Diligence, KYC and AI-Powered Customer Risk Assessment Customer identification and verification, simplified and enhanced due diligence, beneficial ownership, politically exposed persons (PEPs), sanctions and adverse media screening,
• Module 3: Transaction Monitoring Systems and AML Alert Management Rules-based transaction monitoring, transaction thresholds and typologies, monitoring of deposits, withdrawals and transfers, velocity and frequency analysis, unusual transaction patterns,
• Module 4: Financial Data Analytics and Preparation for AML Modelling Sources of transaction and customer data, transaction identifiers and timestamps, customer and account attributes, data integration, data quality assessment, missing-value treatment, dup
• Module 5: AI and Machine Learning for AML Transaction Monitoring Introduction to machine learning in financial crime prevention, supervised and unsupervised learning, classification and anomaly detection, feature engineering for suspicious transaction ide
• Module 6: Anomaly Detection and Suspicious Transaction Pattern Recognition Statistical anomaly detection, unusual transaction amounts and frequencies, deviations from customer behaviour, outlier detection, isolation-based methods, clustering for unusual c
• Module 7: Advanced Money Laundering Typologies and AI Detection Strategies Structuring and smurfing, rapid movement of funds, layering patterns, unusual cash deposits, circular transactions, funnel accounts, mule account indicators, trade-based money laun
• Module 8: Network Analytics, Link Analysis and Relationship Mapping Customer-account-transaction relationships, graph-based financial crime analysis, identifying connected accounts, common identifiers and shared beneficiaries, transaction networks, centra
• Module 9: AI-Powered Sanctions Screening, PEP Detection and Entity Resolution Sanctions screening principles, PEP identification, adverse media analysis, name matching and transliteration, fuzzy matching, false-positive reduction, entity resolution, dupli
• Module 10: Fraud Analytics and the Relationship Between Fraud and AML Common banking and digital payment fraud patterns, account takeover, identity fraud, payment fraud, card and mobile money fraud, mule accounts, suspicious beneficiary relationships, fra
• Module 11: Investigations, Case Management and Suspicious Transaction Reporting AML alert investigation, transaction tracing, reconstruction of fund flows, evidence collection, customer and counterparty profiling, investigation narratives, case prioritisa
• Module 12: Model Evaluation, Explainable AI and False-Positive Reduction Precision, recall, F1-score and confusion matrices, class imbalance, threshold optimisation, alert conversion rates, false-positive analysis, investigation workload, model explainabi
• Module 13: AML Governance, Data Protection and Regulatory Technology AML governance frameworks, AI model governance, data quality and lineage, privacy and confidentiality, access controls, cybersecurity, regulatory record keeping, auditability, third-part
• Module 14: AML Dashboards, Reporting and Compliance Performance Analytics AML key performance indicators, transaction monitoring coverage, alert volumes, false-positive rates, case ageing, investigation turnaround times, suspicious activity escalation tre
• Module 15: Integrated AI-Powered AML Analytics Capstone Project Preparation of an anonymised or synthetic transaction dataset, customer and transaction risk profiling, development of rules-based monitoring scenarios, implementation of anomaly detection, i

General notes

• Tools and Software Python for financial crime analytics and machine learning, Jupyter Notebook for practical model development, Pandas and NumPy for transaction data cleaning and feature engineering, Scikit-learn for classification, clustering, anomaly detection and model evaluation, XGBoost for advanced predictive modelling, NetworkX for transaction network analysis, Matplotlib, Seaborn and Plotly for financial crime visualisation, Microsoft Excel for transaction review and initial analysis, Power BI for AML dashboards and compliance reporting, SQL for querying customer and transaction databases, and synthetic or appropriately anonymised datasets for practical exercises. Enterprise AML platforms may also be reviewed conceptually to demonstrate alert management, sanctions screening and investigation workflows.
• Practical Exercises and Outputs Analyse a synthetic banking transaction dataset to identify unusual customer behaviour, design rules-based monitoring scenarios for suspicious transaction patterns, build an anomaly detection model to flag potentially unusual transactions, develop customer risk scoring using selected KYC and transaction indicators, evaluate a classification model using precision, recall and false-positive analysis, investigate potential structuring and rapid fund movement patterns, visualise relationships between connected accounts using network analytics, demonstrate name-matching and entity-resolution techniques for sanctions screening, prepare an investigation case summary and sample suspicious transaction report, create a Power BI dashboard showing AML alerts and compliance KPIs, and produce a final financial crime analytics report documenting findings, limitations, model governance considerations and recommended next steps.

Frequently asked questions

Schedule & fees