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USE CASE

Transaction Fraud

Objective

  • Find new ways to improve fraud detection accuracy and detection time across multiple transaction fraud types (CP, CNP, Debit, Wire, etc) 

  • Use AI to generate new fraud rules

  • Detect anomalous patterns for individuals, accounts and networks within large data sets

  • Target different groups and subsets in a tailored approach, comparing different inferences from these subsets with the same inferences from the entire population

  • Flag specific repetitive fraud groups and deploy counter measures

Outcome

  • 6x faster development of state-of-the-art ML models that pre-empted fraudulent transactions

  • Monitored account and network level activities to identify frequent payments,, ATA interactions and money transfers to central accounts

  • Top 5 features created automatically

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Business Value

  • Reduce fraud losses and improve customer experience

  • 2x increase in accurate fraud detection 

  • 11% improvement in accuracy resulting in $1M saved monthly per basis point

H2O's AI and Data Approaches

  • Build AI models and use custom recipes specifically built for generating features/variables that provide associated information about fraudulent behavior. This data is then available to the fraud investigator who can further slice and dice the data and consume the information intuitively.

  • Feature engineering with Deep Learning to model new and complex attack patterns quickly

  • Behavior profiling for data networks - IP addresses, buying patterns

  • Terabytes of data leveraged to deliver high scalability and performance, flexible deployment and integration with other big data frameworks

Transaction Fraud Transaction Fraud

 

Resources

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