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Analyze Fraud Using Data Analytics and R

This course is part of Apply R for Business Analytics Projects Specialization

Instructor: EDUCBA

Included with

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2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

6 hours to complete

Flexible schedule

Learn at your own pace

2 modules

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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Learn more about Coursera for Business

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Build your subject-matter expertise

This course is part of the Apply R for Business Analytics Projects Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

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.

Module details

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

8 videosTotal 69 minutes

Introduction to Fraud12 minutes
Types of Fraud8 minutes
Fraud Analytics7 minutes
Details of Fraud9 minutes
Traditional Fraud Detection Method9 minutes
Fraud Detection - BIG DATA Approach6 minutes
Supervised Learning11 minutes
Unsupervised Learning7 minutes

4 assignmentsTotal 60 minutes

Understanding Fraud Fundamentals10 minutes
Core Concepts in Fraud Analytics10 minutes
Modern Data-Driven Fraud Detection10 minutes
Foundations of Fraud and Analytics30 minutes

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.