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Course Description
A typical organization loses an estimated 5% of its yearly revenue to fraud. In this course, you will learn how to fight fraud by using data. For example, you'll learn how to apply supervised learning algorithms to detect fraudulent behavior similar to past ones, as well as unsupervised learning methods to discover new types of fraud activities. Moreover, in fraud analytics you often deal with highly imbalanced datasets when classifying fraud versus non-fraud, and during this course you will pick up some techniques on how to deal with that. The course provides a mix of technical and theoretical insights and shows you hands-on how to practically implement fraud detection models. In addition, you will get tips and advice from real-life experience to help you prevent making common mistakes in fraud analytics.
Prerequisites
Curriculum
Course outline
1
Introduction and preparing your data
In this chapter, you'll learn about the typical challenges associated with fraud detection, and will learn how to resample your data in a smart way, to tackle problems with imbalanced data.
- Introduction to fraud detection50 XP
- Checking the fraud to non-fraud ratio100 XP
- Plotting your data100 XP
- Increasing successful detections using data resampling50 XP
- Resampling methods for imbalanced data50 XP
- Applying SMOTE100 XP
- Compare SMOTE to original data100 XP
- Fraud detection algorithms in action50 XP
- Exploring the traditional way to catch fraud100 XP
- Using ML classification to catch fraud100 XP
- Logistic regression combined with SMOTE100 XP
- Using a pipeline100 XP
2
Fraud detection using labeled data
Now that you're familiar with the main challenges of fraud detection, you're about to learn how to flag fraudulent transactions with supervised learning. You will use classifiers, adjust them, and compare them to find the most efficient fraud detection model.
3
Fraud detection using unlabeled data
This chapter focuses on using unsupervised learning techniques to detect fraud. You will segment customers, use K-means clustering and other clustering algorithms to find suspicious occurrences in your data.
4
Fraud detection using text
In this final chapter, you will use text data, text mining, and topic modeling to detect fraudulent behavior.
Fraud Detection in Python
Course
Complete
