Detection of Credit Card Fraud with an Autoencoder
Do you want to know how to create an anomaly detector using Python and TensorFlow? Then this article is for you. Credit card companies use anomaly detectors to detect fraudulent transactions. It is important to identify fraudulent transactions so that customers do not have to pay for something they did not buy.
Many credit card transactions take place every day, but very few transactions are fraudulent. The fraudulent transactions are anomalies. The article presents an implementation of an autoencoder model to detect these fraudulent transactions.
We’ll discuss the following points:
Anomaly detection in general
Anomaly definition
Types of anomalies
Anomaly detection
Autoencoder concept
Credit Card Fraud Detection Implementation
Data Preparation
Modeling
Evaluation
Conclusion
To read this post you'll need to become a member. Members help us fund our work to ensure we can stick around long-term.
See our plans (Opens in a new window)
Already a member? Log in (Opens in a new window)
