Analyze Fraud Using Data Analytics and R

Overview

Learners will analyze fraud patterns, evaluate fraud detection techniques, and apply data-driven analytical approaches to identify and mitigate fraudulent activities. This course builds a strong foundation in fraud concepts while progressively introducing modern fraud analytics methods, including Big Data approaches and machine learning techniques such as supervised and unsupervised learning. Learners will gain a structured understanding of the fraud lifecycle, high-level fraud analytics strategies, and the measurable business benefits of analytics-driven fraud prevention. By completing this course, learners will be able to interpret real-world fraud scenarios, assess risk using analytical reasoning, and support informed decision-making in fraud detection environments. The course emphasizes practical insight through detailed credit card fraud examples, enabling learners to connect theory with real operational challenges. What makes this course unique is its end-to-end perspective on fraud analytics—from foundational concepts to strategic implementation—combined with a project-oriented approach using R for analytical thinking. Rather than focusing solely on tools, the course develops analytical judgment, pattern recognition skills, and strategic awareness essential for roles in fraud risk, data analytics, and financial crime prevention.

Syllabus

Foundations of Fraud and Analytics

This module introduces learners to the fundamental concepts of fraud and the analytical techniques used to detect and prevent it. Learners explore different types of fraud, understand how fraud occurs, and examine the limitations of traditional fraud detection methods. The module then transitions into modern, data-driven approaches, highlighting the role of Big Data and machine learning techniques in identifying fraudulent behavior. By the end of this module, learners will have a strong conceptual foundation in fraud analytics and be prepared to apply analytical thinking to fraud detection scenarios.

Fraud Lifecycle, Strategy, and Real-World Application

This module focuses on the end-to-end fraud lifecycle and the strategic role of analytics in managing fraud risk. Learners examine how fraud evolves over time, why continuous monitoring is essential, and how organizations design high-level fraud analytics strategies aligned with business objectives. The module concludes with real-world credit card fraud scenarios, demonstrating how analytics is applied in practice to detect suspicious behavior, reduce losses, and improve decision-making in high-volume transaction environments.