Transaction Fraud

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

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

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

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