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Catching Cyber-Fraud

Example: A large credit card provider losing millions from cyber-fraud needs multiple data sources to improve under-performing fraud detection models.

Challenge

Datasets contain significantly fewer examples of fraudulent transactions, skewing data and undermining model performance.
Training data from competitors,payment processors, and banks is needed, but trade secrets and privacy concerns impede access.
Model training thus slows without the necessary data, giving cyber criminals sufficient time to develop new strategies before existing models can improve outcomes.

Solution

Devron allows data scientists to train cyber-fraud models on third-party sets without directly accessing the data.
Diversified data sources would minimize skew and mean that the company’s models were better prepared to identify edge cases and sophisticated schemes.
Recurring model updates incorporate fresh data and could allow cyber-fraud models to stay one step ahead in identifying new methods of abuse.

Catching Cyber-Fraud

Catching Cyber-Fraud

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