Analyze Financial Fraud Using Machine Learning Analytics

Overview

By the end of this course, learners will be able to analyze banking and credit systems, apply machine learning techniques for fraud detection, evaluate financial risk using efficiency models, and interpret profitability reports to support data-driven decisions. Learners will gain the ability to assess credit risk, detect fraudulent payment patterns, and evaluate operational efficiency using industry-relevant analytical frameworks. This course provides a practical, end-to-end exploration of financial fraud analytics across banking, credit, and payment systems. Learners progress from foundational banking concepts and credit risk classification to advanced fraud detection, efficiency modeling, and profit-and-loss analysis. The course integrates logistic regression, risk analytics, and Data Envelopment Analysis (DEA) to bridge predictive modeling with operational and financial performance evaluation. What makes this course unique is its combined focus on machine learning, financial efficiency, and real-world fraud decision-making. Instead of treating fraud detection as a standalone modeling task, the course emphasizes interpretability, regulatory relevance, and business impact. Through applied examples and structured analytics workflows, learners develop job-ready skills aligned with roles in financial risk analytics, fraud prevention, and data-driven decision support.

Syllabus

Foundations of Banking, Credit, and Fraud Analytics

This module introduces learners to the structure of the banking system, core credit evaluation concepts, and foundational machine learning techniques used in fraud detection. Learners explore how financial institutions assess borrower risk, apply logistic regression for credit classification, and evaluate fraud prediction models using performance metrics critical to regulated financial environments.

Credit Fraud Detection and Risk Modeling

This module focuses on applied fraud detection within credit payment systems, emphasizing real-time risk evaluation, analytics setup, and market-driven risk considerations. Learners examine fraud model evaluation metrics, analytics infrastructure, and efficiency benchmarking techniques used to assess trading and financial market operations.

Advanced Efficiency, Profitability, and Fraud Insights

This module advances learners into efficiency modeling, profitability analysis, and constraint-based decision frameworks used in financial fraud analytics. Learners apply DEA models, interpret profit and loss reports, and compare Variable and Constant Returns to Scale assumptions to support data-driven fraud and operational decisions.