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Fraud prevention requires continuous diligence and advanced capabilities. To stay ahead of fraudsters, modern techniques must be deployed. A powerful application of Machine Learning (ML) is anomaly detection to identify abnormalities in patterns. It can be applied in a multitude of business use-cases to drive efficiencies and cost savings, from IT security to preventive maintenance, quality assurance and more. The identification of outliers in data or patterns can help to identify suspicious, potentially fraudulent transactions.
In this demo we'll explore:
  • Adaptable sample workflows for enterprise ML applications
  • How to use ML prototypes for Anomaly Detection to build and deploy a sample payment card fraud detection model and fraud insights application
  • Deploying the Applied ML Prototype to deliver business value

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