Analyze Financial Fraud Using Machine Learning Analytics

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

• Analyze banking, credit, and payment systems to identify fraud and risk patterns
• Apply machine learning and efficiency models to detect fraud and assess performance
• Interpret risk, profitability, and efficiency outputs for data-driven financial decisions

• Basic computer and internet skills
• Ability to read course instructions in English and complete short practice activities