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Online Payment Fraud Detection with Azure Machine Learning


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Fraud detection is one of the earliest industrial applications of data mining and machine learning. Azure Machine Learning helps data scientists easily build and deploy an online transaction fraud detection solution. This session presents best practices, design guidelines and a working implementation for building an online payment fraud detection mechanism in a web portal connected to a credit card payment gateway.

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Online Payment Fraud Detection with Azure Machine Learning

  1. 1. Online Payment Fraud Detection with Azure Machine Learning
  2. 2. Stefano Tempesta
  3. 3. AGENDA Intro to Azure Machine Learning Payment Fraud Detection Anomaly Detection in Azure ML
  4. 4. Azure Machine Learning • Sentiment Analysis • Demand Estimation • Recommendations • Outcome Prediction MODEL REST Service DATASET EXPERIMENT • Training • Scoring • Evaluation
  5. 5. VISA handles 2000 transactions / sec >170M tpd In 2015, financial fraud totaled a cost of € 900 million
  6. 6. Traditional Payment Fraud Detection • Analyze transactions and human-review suspicious ones • Use a combination of data, horizon-scanning and “gut-feel” • Every attempted purchase that raises an alert is either declined or reviewed • False positive • Fail to predict unware threats • Need to update risk score regularly
  7. 7. ML Payment Fraud Detection • Large, historical datasets across many clients and industries • Benefit also small companies • Self-learning models not determined by a fraud analyst • Update risk score quickly
  8. 8. Feature Engineering • Aggregated variables: aggregated transaction amount per account, aggregated transaction count per account in last 24 hours and last 30 days. • Mismatch values: mismatch between shipping Country and billing Country. • Risk tables: fraud risks are calculated using historical probability grouped by country, IP address, etc.
  9. 9. Anomaly Detection • Anomaly Detection encompasses many important tasks in Machine Learning: • Identifying transactions that are potentially fraudulent • Learning patterns that indicate an intrusion has occurred • Finding abnormal clusters of patients • Checking values input to a system • Azure Machine Learning supports: • PCA-Based Anomaly Detection  Principal Components • One-Class Support Vector Machine  Supervised Learning
  10. 10. Anomaly Detection in Azure ML