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

Tabular Data

Transaction Type
Transaction Type
Transaction Amount
Transaction Amount
Transaction Date
Transaction Date
Transaction Time
Transaction Time
Transaction Location
Transaction Location
User Profile
User Profile
Products Purchased
Products Purchased
Credit Card Fraud Detection

Business Value

Identify fraudulent purchases to reduce financial losses
Identify fraudulent purchases to reduce financial losses
Prevent future fraudulent behavior
Prevent future fraudulent behavior
Protect customers from bad actors
Protect customers from bad actors

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.

Sample Ludwig configuration and Automated ML pipeline

Resources to Get Started

Ready to customize and deploy your own LLM?

Try Predibase