GenAI for Fraud Detection Analytics Course

Lesson 1 • The Fraud Detection Lifecycle

Maps the end-to-end process from alert generation to case closure. Grounds students in operational context before introducing AI tooling.

Lesson 2 • Introduction to AI and Machine Learning

Defines supervised, unsupervised, and reinforcement learning at a conceptual level. Connects each paradigm to a specific fraud detection use case.

Lesson 3 • Regulatory and Ethical Baseline

Outlines compliance obligations, fairness requirements, and explainability mandates relevant to AI fraud systems. Sets the ethical guardrails applied throughout the course.

Lesson 4 • Fraud Typologies and Threat Landscape

Covers identity fraud, payment fraud, account takeover, and insider threats. Provides the taxonomy needed to frame all subsequent AI modeling decisions.

Lesson 1 • Feature Stores and Data Pipelines

Designs reusable feature stores that serve both training and real-time inference. Consistent feature serving eliminates training-serving skew in production.

Lesson 2 • Data Quality and Labeling

Addresses missing values, class imbalance, and label noise specific to fraud datasets. Clean labels are the prerequisite for reliable supervised learning.

Lesson 3 • Feature Engineering for Fraud

Constructs velocity features, aggregation windows, and graph-based signals from raw data. Strong features reduce model complexity and improve detection rates.

Lesson 4 • Synthetic Data Generation for Fraud

Uses generative models to augment rare fraud classes and protect data privacy. Synthetic data enables model training where real labeled samples are scarce.

Lesson 1 • Hyperparameter Tuning and Validation

Applies cross-validation, time-aware splits, and Bayesian optimization to fraud model tuning. Temporal splits prevent data leakage from future transactions into training.

Lesson 2 • Model Interpretability and Explainability

Uses SHAP values and LIME to explain individual fraud decisions to investigators. Explainability supports regulatory compliance and analyst trust in model outputs.

Lesson 3 • Supervised Classification Algorithms

Covers logistic regression, decision trees, random forests, and gradient boosting for fraud scoring. Establishes the performance baseline all advanced models must beat.

Lesson 4 • Anomaly Detection Techniques

Applies isolation forests, one-class SVMs, and autoencoders to detect unknown fraud patterns. Unsupervised methods catch novel attacks that labeled models miss.

Lesson 1 • Recurrent Models for Transaction Sequences

Trains LSTMs and GRUs on ordered transaction histories to detect behavioral drift. Sequential modeling captures temporal fraud patterns invisible to tabular classifiers.

Lesson 2 • Graph Neural Networks for Fraud Rings

Models account relationships and transaction networks as graphs to expose fraud rings. GNNs propagate fraud signals across connected entities that individual models miss.

Lesson 3 • Neural Network Fundamentals

Reviews feedforward networks, activation functions, and backpropagation in the fraud context. Provides the architectural vocabulary needed for all subsequent deep learning sections.

Lesson 4 • Multimodal Fraud Detection

Fuses tabular, text, image, and behavioral modalities into a unified fraud scoring model. Multimodal fusion raises detection accuracy on complex, cross-channel fraud schemes.

Lesson 1 • Adversarial Attack Simulation with GenAI

Generates synthetic adversarial transactions to probe and harden existing detection models. Red-teaming with generative AI reveals blind spots before real attackers exploit them.

Lesson 2 • Continual Learning Against Evolving Fraud

Implements online and continual learning loops that adapt models as fraud tactics shift. Continuous adaptation prevents model decay caused by concept drift in fraud patterns.

Lesson 3 • LLMs for Fraud Investigation Assistance

Prompts and fine-tunes LLMs to summarize cases, extract entities, and draft SAR narratives. LLM assistance reduces analyst workload and improves investigation consistency.

Lesson 4 • Deepfake and Synthetic Identity Detection

Trains classifiers to distinguish AI-generated identity documents, faces, and voice samples. Countering generative fraud requires models trained on generative attack outputs.

Lesson 1 • Stream Processing for Fraud Signals

Processes high-velocity event streams to compute real-time features for fraud models. Stream processing enables velocity checks and behavioral features at inference time.

Lesson 2 • System Resilience and Failover

Designs fallback scoring logic, circuit breakers, and redundancy for fraud system outages. Resilient systems maintain fraud protection even when primary models are unavailable.

Lesson 3 • Real-Time Inference Architecture

Designs model serving layers that return fraud scores within millisecond SLA constraints. Architecture choices directly determine whether fraud is stopped before transaction completion.

Lesson 4 • Model Deployment and Versioning

Applies CI/CD pipelines, canary deployments, and shadow scoring to fraud model releases. Safe deployment practices prevent production incidents during model updates.

Lesson 1 • Production Model Monitoring

Tracks score distributions, feature drift, and prediction quality in live fraud systems. Continuous monitoring is the first line of defense against silent model degradation.

Lesson 2 • AI Governance Frameworks

Implements model cards, governance committees, and accountability structures for fraud AI. Governance frameworks align AI operations with organizational risk appetite and regulation.

Lesson 3 • Model Risk Management

Applies model risk management principles to validate, document, and audit fraud AI systems. Formal validation satisfies regulatory expectations for high-risk AI model governance.

Lesson 4 • Fairness Auditing and Bias Mitigation

Measures disparate impact across demographic groups and applies debiasing techniques. Fairness audits protect customers and reduce legal exposure from discriminatory scoring.

Lesson 1 • Operating Model and Team Design

Defines roles, responsibilities, and collaboration models for data science, fraud ops, and IT. A clear operating model prevents ownership gaps that slow fraud AI delivery.

Lesson 2 • Fraud Analytics Maturity Assessment

Evaluates current detection capabilities against a structured maturity model to identify gaps. Maturity assessment anchors the strategic roadmap in an honest baseline.

Lesson 3 • Vendor and Technology Evaluation

Applies a structured framework to evaluate fraud AI vendors, platforms, and open-source tools. Objective evaluation prevents costly vendor lock-in and misaligned capability purchases.

Lesson 4 • Building the Business Case for GenAI

Quantifies fraud loss reduction, operational savings, and risk mitigation to justify AI investment. A rigorous business case secures executive sponsorship and budget allocation.