Detect, Prevent, and Contain Financial Fraud—Before It Lands on the Front Page
A practitioner-built program that equips risk and compliance professionals with AI-driven detection models, regulatory-grade frameworks, and forensic investigation skills—applied end-to-end across your organization's real threat landscape.
"The professionals this field needs can translate between the model, the regulation, and the boardroom — and that is exactly what I built this program to produce."— Dr. J Raymond ABK
What you'll learn
What you'll be able to do
- Build and deploy AI-driven anomaly detection models to flag suspicious transactions in real time
- Design an enterprise fraud prevention framework aligned with global regulatory standards (AML, SOX, GDPR)
- Conduct structured risk assessments using quantitative scoring and machine-learning triage
- Investigate and document fraud incidents with forensic-grade evidence chains ready for legal or regulatory review
- Implement continuous monitoring dashboards that surface emerging risk signals across financial systems
- Lead cross-functional incident response plans that contain fraud exposure and minimize organizational losses
How it works
A school that adapts to you
This isn't a set of static videos. Every lesson is generated live and tuned to where you actually are.
We learn your level
A quick placement check tailors your starting point so you're never bored or lost.
Lessons adapt as you go
Each lesson is written for your pace and your goal, adjusting as your skills grow.
Your AI coach keeps you moving
Checkpoints, feedback, and gentle nudges turn progress into a real result.
The curriculum
What's inside your school
10 modules · 46 lessons
Foundations of Financial Fraud and Enterprise Risk
Establishes the conceptual bedrock for the entire program. Students learn how fraud manifests, how enterprises frame risk, and where AI fits before touching any data or code. Every subsequent module assumes fluency with the vocabulary and mental models built here.
- 1.1Anatomy of Financial Fraud: Typologies and Loss MechanicsIncluded
- 1.2Enterprise Risk Frameworks: COSO, ISO 31000, and Basel III in PracticeIncluded
- 1.3Regulatory Landscape: AML, SOX, GDPR, and Emerging AI RegulationsIncluded
- 1.4Quantitative Risk Scoring: Building Your First Risk RegisterIncluded
- 1.5The AI Opportunity in Fraud and Risk: Capabilities, Limits, and EthicsIncluded
Data Infrastructure for AI-Driven Fraud Detection
Builds the data engineering foundation that every AI fraud model depends on. Students learn how financial transaction data is structured, how to overcome the severe class imbalance inherent in fraud datasets, how to craft predictive features, how to model fraud rings as graphs, and how to serve features at inference speed. This module is a prerequisite to all modeling modules.
- 2.1Financial Data Structures: Transactions, Entities, and Behavioral SignalsIncluded
- 2.2Handling Imbalanced Fraud Datasets: SMOTE, Undersampling, and Cost-Sensitive LearningIncluded
- 2.3Feature Engineering for Fraud SignalsIncluded
- 2.4Graph Data and Network Analytics for Fraud Ring DetectionIncluded
- 2.5Data Pipelines and Feature Stores for Real-Time Fraud ScoringIncluded
Machine Learning Models for Fraud Detection
Develops the full spectrum of ML techniques applicable to fraud — from interpretable supervised classifiers through deep unsupervised anomaly detectors to sequential behavioral models — while embedding explainability and model governance throughout. Students emerge able to select, train, validate, and justify any model to a regulator.
- 3.1Supervised Fraud Classification: Gradient Boosting and Ensemble MethodsIncluded
- 3.2Unsupervised Anomaly Detection: Isolation Forest, Autoencoders, and LOFIncluded
- 3.3Sequence and Time-Series Models: LSTMs and Transformers for Behavioral FraudIncluded
- 3.4Model Explainability and Regulatory Justification: SHAP, LIME, and CounterfactualsIncluded
- 3.5Model Validation, Backtesting, and Model Risk GovernanceIncluded
Real-Time Anomaly Detection and Continuous Monitoring
Transitions from model-building to live production operations. Students learn to embed models in streaming pipelines, build dashboards that surface risk signals to analysts, manage alert queues efficiently, and keep models performant as fraud patterns drift — delivering the continuous monitoring outcome.
- 4.1Streaming Fraud Detection: Apache Kafka and Real-Time InferenceIncluded
- 4.2Alert Triage and Case Management WorkflowsIncluded
- 4.3Continuous Monitoring Dashboards: Design and BuildIncluded
- 4.4Model Drift Detection and Automated Retraining PipelinesIncluded
- 4.5Threat Intelligence Integration and External Signal FeedsIncluded
AML, Transaction Monitoring, and Regulatory Compliance Automation
Focuses specifically on the anti-money laundering domain, where AI must satisfy both detection performance and strict regulatory documentation requirements. Students build AML-specific detection capabilities, automate customer risk rating and KYC, and learn to produce SAR filings and audit-ready documentation that survive regulatory examination.
- 5.1AML Typologies and the AI-Augmented Transaction Monitoring SystemIncluded
- 5.2Customer Risk Rating and AI-Enhanced KYCIncluded
- 5.3Suspicious Activity Report Automation and Quality ControlIncluded
- 5.4Regulatory Examination Readiness and Audit Trail DesignIncluded
Enterprise Fraud Prevention Framework Design
Elevates students from individual model builders to enterprise architects. The module teaches how to design layered fraud control architectures, govern AI vendor relationships, establish risk appetite and policy structures, manage third-party fraud risk, and build the human and cultural defenses that no algorithm can replace.
- 6.1Fraud Control Architecture: Layered Defenses and Control MappingIncluded
- 6.2AI Vendor Assessment and Model Procurement GovernanceIncluded
- 6.3Fraud Risk Appetite, Policies, and Governance StructuresIncluded
- 6.4Third-Party and Supply Chain Fraud RiskIncluded
- 6.5Culture, Training, and Human Factors in Fraud PreventionIncluded
Fraud Investigation and Digital Forensics
Trains students to conduct rigorous, legally defensible fraud investigations from evidence preservation through final report — leveraging AI tools to accelerate forensic data analysis while maintaining chain-of-custody standards that satisfy prosecutors and regulators. Sequenced after prevention to reflect the operational lifecycle.
- 7.1Fraud Investigation Planning and Evidence PreservationIncluded
- 7.2AI-Assisted Forensic Data AnalysisIncluded
- 7.3Interview Techniques and Behavioral Indicators of DeceptionIncluded
- 7.4Investigation Reporting and Regulatory DisclosureIncluded
Fraud Incident Response and Crisis Containment
Shifts from investigation (understanding what happened) to response (stopping the bleeding and recovering). Students design and exercise incident response plans, practice containment and recovery tactics, manage multi-stakeholder communications under pressure, and conduct the post-incident reviews that drive lasting control improvements.
- 8.1Incident Response Planning: Roles, Playbooks, and Escalation PathsIncluded
- 8.2Containment, Remediation, and Financial Recovery TacticsIncluded
- 8.3Stakeholder Communication and Regulatory NotificationIncluded
- 8.4Post-Incident Review and Control EnhancementIncluded
Advanced AI Techniques and Emerging Fraud Threats
Pushes to the frontier — the threats that are reshaping fraud today and the AI techniques being developed to counter them. Sequenced late in the program so students have the foundational skills to critically evaluate new approaches rather than accepting them uncritically. Covers generative AI fraud, crypto-AML, real-time payment risks, and adversarial ML.
- 9.1Generative AI as a Fraud Vector: Deepfakes, Synthetic Identities, and AI-Written ScamsIncluded
- 9.2Cryptocurrency Fraud, Blockchain Analytics, and Crypto-AMLIncluded
- 9.3Real-Time Payment Fraud and APP Scam DetectionIncluded
- 9.4Adversarial Machine Learning and Model Evasion by Fraud ActorsIncluded
- 9.5The Future of AI in Fraud and Risk: Large Language Models, Agentic AI, and Federated LearningIncluded
Capstone: AI-Powered Fraud and Risk Program Build
Integrates every module into a single end-to-end deliverable that mirrors the real-world challenge of building or transforming an enterprise fraud and risk program. Students work in teams to produce a complete, board-ready program package — from risk assessment through detection architecture to incident response — and defend it before a panel.
- 10.1Capstone Kickoff: Organizational Profile and Risk Landscape AssessmentIncluded
- 10.2Capstone Build: Detection Models, Monitoring Architecture, and Compliance FrameworkIncluded
- 10.3Capstone Build: Incident Response Plan, Investigation Protocol, and Governance StructureIncluded
- 10.4Capstone Presentation: Executive Defense and Peer ReviewIncluded
Who it's for
Is this you?
Risk Managers
Ready to replace rule-based alert systems with AI-driven detection architectures they can govern, justify, and defend to the C-suite.
Compliance Officers
Need to align AML, SOX, and GDPR obligations with AI-augmented workflows and produce audit trails that hold up under regulatory examination.
Internal Auditors
Tasked with evaluating AI-driven fraud controls they didn't build—and need the technical fluency to assess model risk governance and control design rigorously.
Financial Crime Analysts
Drowning in false positives and manual case queues—looking to build and deploy smarter detection models and automate SAR workflows without losing evidentiary quality.
Enterprise Security Leaders
Responsible for cross-functional fraud incident response and need structured playbooks, containment tactics, and stakeholder communication frameworks that hold under pressure.
Fraud Strategy Consultants
Advising organizations on fraud program modernization and need a rigorous, end-to-end methodology—from risk scoring to capstone-ready governance deliverables—to back their recommendations.
Questions
Frequently asked
Your teacher
A note from your teacher
Dr. J Raymond ABK
If you are reading this, you are probably already responsible for something that keeps you up at night—a transaction monitoring system that generates too many false positives to action, an AML program that a regulator could pick apart, an audit committee that wants to see an AI strategy you are not sure how to articulate, or a fraud loss that happened faster than your controls could respond.
I built this program because the skills that close those gaps are not taught together anywhere. Risk and compliance training tends to stay in the regulatory lane. Data science training stays in the model lane. Neither produces the professional who can sit in a room with a CRO, a regulator, and an engineering team and speak fluently to all three. That professional is who this curriculum builds.
Every lesson in this program is structured around decisions you will have to make in practice: Which model architecture fits a real-time payment fraud use case? How do you justify a machine learning model's output to an examiner when the regulation was written before AI existed? What goes in the evidence package when a fraud investigation crosses a regulatory disclosure threshold? What does a defensible model risk governance process look like when the model is yours? These are the questions we answer—with specificity, with the relevant regulatory standards named, and with the tools you will actually use.
I want to be direct about what this program is not. It is not a survey of fraud concepts for general awareness. It is not a certification prep course that teaches you to pass a multiple-choice exam. It is a practitioner-grade build—ten modules that move from foundations through a full capstone program build, designed so that the work you produce during the course is work you can defend in front of a board, a regulator, or a peer review panel.
The professionals who will get the most from this are the ones who are already in the field and ready to operate at the next level of technical and strategic depth. If that is where you are, I am glad you are here. Let's build something your organization can actually rely on.
— Dr. J Raymond ABK
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- 10 modules, 46 lessons
- AI-adaptive lessons tuned to your level
- Quizzes & checkpoints to lock in progress
- Your own AI learning coach
- Learn on any device, at your pace
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