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Course

Fraud Detection in Python

IntermediateSkill Level

4.7+

Updated 08/2024

Learn how to detect fraud using Python.

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PythonMachine Learning4 hr16 videos57 Exercises4,800 XP21,775Statement of Accomplishment

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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

Unsupervised Learning in Python Supervised Learning with scikit-learn

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 detection

Checking the fraud to non-fraud ratio

Plotting your data

Increasing successful detections using data resampling

Resampling methods for imbalanced data

Applying SMOTE

Compare SMOTE to original data

Fraud detection algorithms in action

Exploring the traditional way to catch fraud

Using ML classification to catch fraud

Logistic regression combined with SMOTE

Using a pipeline

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.

Review of classification methods

Natural hit rate

Random Forest Classifier - part 1

Random Forest Classifier - part 2

Performance evaluation

Performance metrics for the RF model

Plotting the Precision Recall Curve

Adjusting your algorithm weights

Model adjustments

Adjusting your Random Forest to fraud detection

GridSearchCV to find optimal parameters

Model results using GridSearchCV

Ensemble methods

Logistic Regression

Voting Classifier

Adjust weights within the Voting Classifier

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.

Normal versus abnormal behavior

Exploring your data

Customer segmentation

Using statistics to define normal behavior

Clustering methods to detect fraud

Scaling the data

K-means clustering

Elbow method

Assigning fraud versus non-fraud

Detecting outliers

Checking model results

Other clustering fraud detection methods

Assessing smallest clusters

Checking results

4

Fraud detection using text

In this final chapter, you will use text data, text mining, and topic modeling to detect fraudulent behavior.

Using text data

Word search with dataframes

Using list of terms

Creating a flag

Text mining to detect fraud

Removing stopwords

Cleaning text data

Topic modeling on fraud

Create dictionary and corpus

Flagging fraud based on topics

Interpreting the topic model

Finding fraudsters based on topic

Fraud Detection in Python

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FAQs

What Python and machine learning background is expected for this course?

You should know pandas, scikit-learn for supervised learning, and unsupervised learning basics. Prior exposure to statistics in Python is also recommended.

How does the course address the class imbalance problem common in fraud data?

You will learn resampling and other techniques specifically designed to handle highly imbalanced datasets where fraudulent cases are far outnumbered by legitimate ones.

Are both supervised and unsupervised methods used for detecting fraud?

Yes. You will apply supervised algorithms to catch fraud patterns similar to known cases, and unsupervised methods to discover entirely new types of fraudulent activity.

What industries or roles benefit most from fraud detection skills?

Financial services, insurance, e-commerce, and healthcare all rely on fraud analytics. Roles include fraud analyst, data scientist, and risk management specialist.

Does the course include practical tips from real fraud analytics experience?

Yes. Beyond technical methods, the course shares tips and advice drawn from real-life fraud analytics work to help you avoid common mistakes in production settings.

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