Detecting Fraud & Anti-Money Laundering (AML) Violations In Real-Time

Fraudsters and money launderers are more adept than ever at escaping the detection of traditional fraud and AML solutions. Over the past 12 months, 63% of businesses have experienced the same or more fraud losses compared to the previous period (2018 Global Fraud and Identity Report, Experian). Ecommerce businesses are incurring high losses with credit card chargeback fraud alone. Telecom providers are struggling daily to sift through calls to find phone scam criminals. The banking industry is suffering hundreds of billions of dollars in costs thanks to new, complex AML compliance requirements.

Relational database and earlier generations of graph database vendors such as Neo4j and DataStax have struggled to provide a real-time solution due to the size and intricacy of the problem.

Real-time deep link analytics powered by a highly scalable graph database is addressing these challenges for some of the largest corporations in the world including Alipay, Visa, Uber, China Mobile and SoftBank.

Join us as we discuss how to:

Minimize fraud with faster detection using deep link analytics
Modernize your AML process with case studies across multiple industries
Get fewer false positives in your fraud detection workflow

Speakers:
Victor Lee, Director Product Management, TigerGraph
Gaurav Deshpande, VP Marketing, TigerGraph