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.
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