Fraud Analytics: Fraud Detection With Big Data And Analytics

Unlock the power of Big Data and analytics to detect and prevent fraud in real-time.

What you'll learn

The different types of fraud and their impact on businesses.

The fundamentals of fraud analytics and the traditional methods used to detect fraud.

How Big Data and machine learning are transforming fraud detection.

Techniques like supervised and unsupervised learning applied to fraud detection.

Real-world application of fraud analytics, especially in credit card fraud.

Requirements

Basic understanding of data analytics and fraud concepts. Familiarity with data science tools is helpful but not required.

Description

Course IntroductionThis course is designed to equip you with the knowledge and skills needed to detect and prevent fraud using modern analytics techniques and Big Data approaches. You will dive deep into the types of fraud, traditional detection methods, and the latest tools in fraud analytics, including supervised and unsupervised learning. A special emphasis is placed on real-world applications, such as credit card fraud, to help you implement effective fraud prevention strategies in your organization.Section-Wise WriteupSection 1: Introduction to FraudThe course kicks off with an introduction to fraud, exploring its various forms and the impact it has on businesses and individuals. You'll understand the fundamentals of fraud and why it’s crucial to detect and prevent it in a timely manner.Section 2: Types of Fraud and Detailed Fraud AnalyticsIn this section, you will learn about different types of fraud, such as identity theft, financial fraud, and cyber fraud. You will explore the methodologies used in fraud analytics, including how to identify and analyze fraudulent activities. We’ll also examine traditional fraud detection methods, providing a historical context for how fraud detection has evolved over time.Section 3: Big Data Approach to Fraud DetectionHere, we shift focus to modern fraud detection techniques powered by Big Data. You will explore the power of supervised and unsupervised learning to uncover patterns in massive datasets. Additionally, we will dive into the fraud cycle—how fraud emerges, evolves, and can be disrupted. You'll also learn about high-level strategies to tackle fraud using data-driven approaches, and the various benefits that fraud analytics can offer.Section 4: Case Study – Credit Card FraudThis section brings theory to life with a case study on credit card fraud. Through real-world examples, you'll examine how fraud detection models are applied to detect and prevent fraudulent transactions. We will go through detailed examples of credit card fraud, helping you understand the nuances and strategies needed to combat this growing threat.Section 5: ConclusionIn the final lecture, we summarize the key concepts learned throughout the course. You’ll gain a clear understanding of how to implement fraud detection systems using Big Data and analytics in any industry, and be prepared to apply these insights to real-world challenges.ConclusionBy the end of this course, you will have a comprehensive understanding of how fraud works and how to leverage Big Data and advanced analytics techniques to detect and prevent it effectively. Whether you're in finance, cybersecurity, or any other field, this course will help you build a solid foundation in modern fraud detection strategies.

Overview

Section 1: Introduction

Lecture 1 Introduction to Fraud

Section 2: Types of Fraud and Detail Fraud Analytics

Lecture 2 Types of Fraud

Lecture 3 Fraud Analytics

Lecture 4 Details of Fraud

Lecture 5 Traditional Fraud Detection Method

Section 3: BIG DATA Approach

Lecture 6 Fraud Detection - BIG DATA Approach

Lecture 7 Supervised Learning

Lecture 8 Unsupervised Learning

Lecture 9 Fraud Cycle

Lecture 10 Fraud Cycle Continues

Lecture 11 High Level Strategy

Lecture 12 Fraud Analytics Benefits

Section 4: Case Study

Lecture 13 Credit Card Fraud

Lecture 14 Example of Credit Card Fraud

Lecture 15 Example of Credit Card Fraud Continues

Section 5: Conclusion

Lecture 16 Conclusion

Data analysts and professionals who want to specialize in fraud detection. Security professionals interested in learning how to use data to detect fraud. Business managers looking to understand how analytics can reduce fraud risks. Anyone interested in learning about Big Data's role in modern fraud prevention strategies.