Financial transaction fraud costs hundreds of billions in losses worldwide and affects all industries as online sales became the dominant channel during the COVID-19 pandemic. Current fraud detection solutions are struggling to keep up, especially in detecting online fraud.
Join us to learn how seven out of the top 10 banks in the world are using TigerGraph Graph DB and machine learning to double the performance of their fraud detection system. We will cover how to:
- Generate machine learning features with every financial transaction by using graph analytics
- Improve fraud scores, find missed fraudulent activity and, reduce false-positives, in a standard machine-learning pipeline
- Identify potential fraud and money-laundering rings by digging deeper into connected datasets (payments, accounts, users, devices)
Speakers: Abhishek Mehta & Gaurav Deshpande, TigerGraph