Fraud Detection
Compliance & Security

Fraud Detection

Build a predictive fraud model across multiple bank locations or financial institutions without compromising consumer privacy.


  • 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-detection using machine learning models to stay one step ahead in responding to cybercrime.


Preserve Revenue
Increase the Speed of Model Training
Minimize Risk
Reduce Skew in Training Data