Cookie Consent

By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.

Catching Credit Card Fraud


  • Transaction datasets contain fewer examples of fraudulent behavior, skewing the data and undermining model performance.
  • Training data is therefore needed from competitors, payment processors, and banks, but trade secrets and privacy concerns impede access.
  • Model training slowed without sufficient data, giving cyber-criminals sufficient time to develop new strategies before existing models can have an impact.


  • Devron allowed the credit card provider's data scientists to train fraud models on third party sets without accessing the data directly.
  • Diversified data sources minimize skew and enable the company’s models to better prepare to identify edge cases and sophisticated schemes.
  • Recurring model updates to incorporate data as it is generating allow fraud models to stay one step ahead in responding to cybercrime.
Catching Credit Card Fraud
Catching Credit Card Fraud
< View all Use Cases and Solutions

See how Devron can provide better insight for your organization

Request a Demo