Fraud Analytics Course

Lesson 1 • Defining Fraud and Its Variants

Covers the legal and operational definitions of fraud alongside major scheme categories. Establishes shared vocabulary used throughout the course.

Lesson 2 • The Fraud Triangle and Diamond

Examines motivational models explaining why individuals commit fraud. Connects behavioral theory to detection signal design.

Lesson 3 • Regulatory and Ethical Context

Introduces compliance obligations and ethical boundaries governing fraud analytics programs. Grounds technical work in accountability frameworks.

Lesson 4 • Fraud Data Landscape

Maps the internal and external data sources relevant to fraud detection. Students understand data availability constraints before modeling begins.

Lesson 1 • Feature Engineering for Fraud Signals

Transforms raw transactional fields into predictive features that capture fraud behavior. Strong features are the primary driver of model accuracy.

Lesson 2 • Data Splitting and Leakage Prevention

Establishes correct train-validation-test splits that respect temporal ordering in fraud data. Prevents data leakage that inflates model performance estimates.

Lesson 3 • Data Cleaning and Deduplication

Identifies common data quality issues in fraud datasets and applies systematic remediation. Clean data reduces false positives in detection models.

Lesson 4 • Data Acquisition and Ingestion

Covers methods for extracting data from transactional systems, logs, and third-party feeds. Establishes pipelines that feed downstream analytics.

Lesson 1 • Building Fraud Profiles and Typologies

Synthesizes exploratory findings into documented fraud typologies for operational use. Profiles guide rule design and model feature selection.

Lesson 2 • Descriptive Statistics for Fraud Data

Uses distributional summaries to characterize legitimate vs. fraudulent transactions. Reveals baseline differences that inform feature selection.

Lesson 3 • Visual Exploration Techniques

Applies charts and plots to surface anomalies and behavioral clusters in fraud data. Visualization accelerates hypothesis generation for analysts.

Lesson 4 • Benford's Law and Digit Analysis

Applies the expected digit frequency law to detect fabricated or manipulated figures. Widely used in financial statement and expense fraud investigations.

Lesson 1 • Rule Engine Architecture

Explains how rule engines evaluate transactions against defined conditions in real time. Provides the structural foundation for building detection logic.

Lesson 2 • Measuring Rule Performance

Applies precision, recall, and lift metrics to evaluate rule effectiveness. Performance measurement drives iterative rule improvement.

Lesson 3 • Designing Effective Detection Rules

Translates fraud typologies into precise, testable rule conditions. Well-designed rules minimize false positives while maintaining high recall.

Lesson 4 • Fraud Scoring and Risk Ranking

Combines multiple signals into a composite fraud score for prioritizing alerts. Scoring enables analysts to focus effort on highest-risk cases.

Lesson 1 • Fraud-Specific Evaluation Metrics

Selects and interprets metrics suited to imbalanced fraud classification tasks. Metric choice directly affects business decisions about model deployment.

Lesson 2 • Classification Algorithm Fundamentals

Introduces logistic regression, decision trees, and ensemble methods in the fraud context. Builds intuition for algorithm selection before hyperparameter tuning.

Lesson 3 • Feature Importance and Model Explainability

Extracts and communicates which features drive model predictions using interpretability tools. Explainability supports investigator trust and regulatory review.

Lesson 4 • Model Training and Validation

Applies cross-validation and holdout testing to estimate real-world model performance. Correct validation prevents overfit models from reaching production.

Lesson 1 • Clustering for Behavioral Segmentation

Groups transactions or entities by behavioral similarity to isolate anomalous clusters. Clustering surfaces fraud rings and unusual behavioral cohorts.

Lesson 2 • Isolation Forest and Tree-Based Methods

Uses tree-based isolation to score anomalies without distributional assumptions. Scales efficiently to large transactional datasets.

Lesson 3 • Combining Unsupervised and Supervised Signals

Integrates anomaly scores as features in supervised models to boost detection coverage. Hybrid approaches capture both known and novel fraud patterns.

Lesson 4 • Statistical Anomaly Detection

Applies statistical distance and distribution tests to flag outlier transactions. Provides interpretable, auditable anomaly scores for investigators.

Lesson 1 • Graph Machine Learning Applications

Extends classical graph analytics with graph neural networks for node classification. Enables automated fraud scoring at the entity level using relational context.

Lesson 2 • Centrality and Influence Metrics

Computes degree, betweenness, and PageRank centrality to identify key fraud actors. High-centrality nodes often represent orchestrators or mule accounts.

Lesson 3 • Building Fraud Entity Graphs

Constructs entity relationship graphs from transactional and identity data. Graph construction quality determines the accuracy of downstream analytics.

Lesson 4 • Community Detection for Fraud Rings

Applies community detection algorithms to isolate tightly connected fraud clusters. Ring detection enables coordinated investigation and takedown strategies.

Lesson 1 • Model Risk and Governance

Applies model risk management principles to fraud detection models throughout their lifecycle. Governance ensures models remain accurate, fair, and compliant over time.

Lesson 2 • Adaptive Strategy Against Evolving Fraud

Builds organizational capability to detect and respond to new fraud tactics as they emerge. Adaptive programs reduce the lag between fraud evolution and detection response.

Lesson 3 • Measuring Fraud Program Effectiveness

Establishes KPIs and reporting frameworks to quantify fraud detection and prevention outcomes. Measurement enables data-driven investment decisions for the program.

Lesson 4 • Alert Management and Triage Workflows

Designs end-to-end workflows for routing, prioritizing, and resolving fraud alerts. Efficient triage maximizes investigator throughput and minimizes customer friction.