AI for Fraud Detection and Financial Crime

Course overview

AI for Fraud Detection and Financial Crime equips professionals with the knowledge and practical skills to use Artificial Intelligence, Machine Learning, advanced analytics and emerging Generative AI technologies to detect, investigate, prevent and manage financial crime. The course covers the complete intelligence cycle—from fraud typology and data preparation through transaction monitoring, anomaly detection, predictive modelling, graph/network analytics, case investigation, model governance and deployment.

AI for Fraud Detection and Financial Crime is a comprehensive professional training course designed to equip banking, fintech, AML/CFT, compliance, risk, cybersecurity, audit and financial-crime professionals with practical knowledge of Artificial Intelligence, Machine Learning and advanced analytics for fraud detection and financial crime prevention. The course covers AI-powered transaction monitoring, anomaly and behavioural analytics, fraud risk scoring, machine learning, graph and network analytics, KYC and customer risk assessment, NLP, Generative AI, AI agents, real-time fraud detection, financial crime investigations, model explainability, AI governance, cybersecurity, data protection and regulatory risk. Participants learn how to identify suspicious patterns, reduce false positives, improve investigation efficiency, strengthen AML/CFT and fraud controls, and develop effective AI-driven financial crime strategies for modern digital banking, payments and financial services.

Course objectives

• Understand the evolving financial-crime landscape and the role of AI in fraud, AML/CFT and financial-crime prevention.
• Identify high-value opportunities for applying AI and machine learning across the financial-crime detection and investigation lifecycle.
• Understand and apply fraud analytics techniques including anomaly detection, behavioural analytics, risk scoring and predictive modelling.
• Use AI concepts to strengthen transaction monitoring and identify suspicious financial activities and emerging fraud patterns.
• Apply graph and network analytics to identify fraud rings, mule accounts, collusion, layering and complex financial relationships.
• Develop and evaluate AI-based fraud and financial-crime detection models using appropriate performance measures such as precision, recall, F1-score and false-positive rates.
• Apply AI and NLP to customer risk assessment, KYC, adverse-media analysis, entity resolution and financial-crime investigations.
• Explore Generative AI and AI agents for investigation support, case analysis, intelligence gathering, reporting and investigator productivity.
• Understand real-time AI fraud detection architectures and how AI systems can integrate with banking, payment, AML, KYC and case-management systems.
• Identify and manage AI-related risks including bias, privacy, cybersecurity, model risk, data leakage, hallucination, adversarial attacks and regulatory risks.
• Develop appropriate AI governance and model-management practices for financial-crime applications.
• Measure AI effectiveness using fraud-loss reduction, detection rates, false-positive reduction, investigation efficiency, ROI and other operational KPIs.
• Develop an AI-powered financial-crime strategy incorporating fraud detection, AML/CFT, investigation, governance, workforce requirements and implementation priorities.
• Design an implementation roadmap for deploying and scaling AI-enabled fraud and financial-crime solutions within a financial institution or other organization.

Who should attend

Course content

• Module 1: Financial Crime in the Digital Economy Topics Understanding financial crime Fraud versus financial crime Fraud typologies Money laundering Terrorist financing Proliferation financing Cyber-enabled financial crime Digital payment fraud Identity t
• Module 2: Fraud Detection and AML/CFT Foundations Fraud detection lifecycle Prevent → Detect → Investigate → Respond → Recover → Learn AML/CFT lifecycle Customer identification Customer due diligence Enhanced due diligence Transaction monitori
• Module 3: Data for Fraud and Financial Crime Analytics This is a critical module because AI is only as effective as the data and controls supporting it. Data sources Transaction data Customer profiles Account data Payment data Device data IP addresses G
• Module 4: Fraud Analytics and Risk Scoring Traditional analytics Rules-based detection Threshold systems Statistical analysis Exception reporting Trend analysis Outlier analysis Risk scoring Customer risk score Transaction risk score Merchant risk score A
• Module 5: Machine Learning for Fraud Detection Supervised learning Logistic regression Decision trees Random forests Gradient boosting XGBoost LightGBM Neural networks Unsupervised learning Clustering Isolation Forest Autoencoders Outlier detection Behavi
• Module 6: Transaction Monitoring with AI Traditional transaction monitoring Rule-based scenarios Thresholds Velocity rules Geographic rules Behavioural rules AI-enhanced monitoring Behavioural baselines Dynamic risk scoring Context-aware detection Real-ti
• Module 7: Anomaly and Behavioural Analytics Topics What is an anomaly? Point anomalies Contextual anomalies Collective anomalies Behavioural baselines Customer behaviour modelling Normal transaction behaviour Normal location Normal transaction size Normal
• Module 8: Graph Analytics and Network-Based Financial Crime Detection This should be a major advanced module. Financial crime is frequently relational rather than isolated. Graph concepts Nodes Edges Relationships Networks Communities Centrality Paths
• Module 9: AI for KYC, Customer Risk and Identity Fraud AI applications Identity verification Document analysis Face verification Behavioural biometrics Customer risk scoring Synthetic identity detection Account takeover detection Customer risk factors Cus
• Module 10: AI for AML and Suspicious Activity Detection AI applications Suspicious transaction detection Transaction pattern analysis Customer risk modelling Alert prioritization Case triage Network analysis Investigative intelligence Common patterns Stru
• Module 11: Natural Language Processing for Financial Crime NLP allows organizations to analyse large volumes of unstructured information. Data sources Investigation notes Customer communications Emails Reports News Adverse media Regulatory documents Cas
• Module 12: Generative AI for Financial Crime Investigations Applications Investigation assistants Case summarization Suspicious activity report drafting Regulatory research Policy interpretation Investigative document analysis Natural-language querying Ev
• Module 13: AI-Powered Fraud Investigation Investigation workflow Alert → Triage → Evidence collection → Relationship analysis → Investigation → Decision → Action → Documentation AI support Alert prioritization Evidence discovery Entity res
• Module 14: Real-Time Fraud Detection Real-time architecture Transaction → Data ingestion → Feature engineering → AI model → Risk score → Decision engine → Approve / Decline / Challenge / Investigate Technologies Streaming analytics APIs Even
• Module 15: Fraud Model Development and Feature Engineering Fraud features Examples: Transaction amount Transaction frequency Time since previous transaction Number of devices Geographic distance Beneficiary age Account age Login frequency Failed authent
• Module 16: Model Explainability and Financial Crime Decisions A financial institution cannot simply say: "The AI says the transaction is suspicious." Explainability Why was the transaction flagged? Which factors influenced the decision? How confident i
• Module 17: AI Governance, Model Risk and Responsible AI This is essential for a professional financial-crime course. NIST's AI RMF organizes AI risk management around Govern, Map, Measure and Manage, with governance treated as a cross-cutting function t
• Module 18: Adversarial Fraud and AI Arms Race A particularly valuable advanced topic. Criminal use of AI Deepfakes Synthetic identities Automated social engineering AI-generated documents Automated phishing Voice impersonation Fraud chatbots Credential
• Module 19: Measuring Fraud and AML AI Performance Business KPIs Fraud losses prevented Fraud losses detected Detection rate False-positive rate Investigation productivity Alert reduction Cost per investigation Case conversion rate Customer friction Respon
• Module 20: AI Deployment and Enterprise Fraud Architecture
• Module 21: AI Strategy for Financial Crime Operations Topics Building an AI fraud strategy AI maturity assessment Build vs buy Vendor evaluation AI operating model Fraud analytics teams Data science teams AML investigators Model-risk teams Technology team

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Schedule & fees