Machine Learning Use Case
Credit Card Fraud Detection
Fraud is a risk for all organizations but is hard to detect as patterns frequently change and fraud datasets are often highly imbalanced. Machine learning has emerged as a powerful technique for identifying fraudulent patterns when properly applied. Learn how to build an end-to-end ML pipeline to automatically detect credit card fraud.
The Predibase Solution
Detect fraud to protect your customers and reduce financial risk
- Rapidly preprocess a large corpus of tabular transaction data
- Train and compare a series of fraud models including GBMs and Neural Networks
- Learn how to handle data imbalance to improve your fraud detection model
Train your first fraud detection model in < 20 lines of a config file
At the core of Predibase is Ludwig, an open-source declarative ML framework, that automates complex model development with a simple configuration file. Predibase builds on these capabilities with a collaborative, easy-to-use, and fully managed ML platform in the cloud.
Resources to Get Started
Read the Tutorial
Check out our deep dive tutorial to learn how to build an end-to-end ML pipeline for fraud detection.
Run the Notebook
Get started with credit card fraud detection using this open-source Ludwig notebook and sample code.
Visit the Resource Center
Visit the Predibase resource center to see our collection of ebooks and webinars on topics from multimodal ML to computer vision.