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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.
Solution
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.
