Machine Learning Applications in Risk Management: Fraud Detection Using Machine Learning

From the series: Machine Learning Applications in Risk Management

Kawee Numpacharoen, MathWorks

Credit card fraud may be one of the most common fraudulent activities in many countries. However, the number of fraudulent activities is very small (less than 1%). Common performance metrics, such as accuracy, may not be that useful for determining model performance. In this demo, you will learn how to use machine learning to detect fraudulent activities as well as how to use built-in functions in MATLAB^® to calculate the area under the precision-recall curve (AUPRC), a custom performance metric.

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Series: Machine Learning Applications in Risk Management

4:42

Fraud Detection Using Machine Learning Learn how to use machine learning to detect fraudulent activities like credit card fraud.

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Classifying Credit Card Default Using the Classification Learner App Use the Classification Learner app and simplified datasets to classify and predict credit card default.

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Forecasting Bitcoin Volatility Using the Regression Learner App See how to apply machine learning techniques to forecast continuous response variables like volatility.

Machine Learning Applications in Risk Management: Fraud Detection Using Machine Learning

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Series: Machine Learning Applications in Risk Management

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