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.
Analyze Fraud Using Data Analytics and R
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
6 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Analyze fraud patterns and evaluate common fraud detection techniques.
Apply data-driven and machine learning approaches to identify fraudulent behavior.
Interpret real-world fraud scenarios to support informed risk and prevention decisions.
Skills you'll gain
- Threat Detection
- Unsupervised Learning
- Predictive Analytics
- R Programming
- Analytical Skills
- Big Data
- Machine Learning Methods
- Fraud detection
- Data-Driven Decision-Making
- Credit Risk
- Applied Machine Learning
- Advanced Analytics
- Anomaly Detection
- Supervised Learning
- Risk Analysis
- Analytics
- Skills section collapsed. Showing 10 of 16 skills.
Details to know
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Assessments
8 assignments
Taught in English
Recently updated!
February 2026
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There are 2 modules in this course
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.
What's included
8 videos4 assignments
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.
What's included
8 videos4 assignments
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