Future Proofing Fraud Prevention with Real-time Data and Intelligence Sharing

In recent years, the complexity of fraud trends has escalated significantly. Fraudsters now use sophisticated methods to enrich customer information from various sources for their monetary gains. They successfully integrate digital signals, customer identity markers, and account-specific information to bypass formidable fraud detection protocols. This has led to an increase in the average loss per fraud event, even as banks deploy advanced analytical and technological solutions. The isolation of data and detection platforms prevents effective intelligence sharing across the customer lifecycle, leaving banks and customers vulnerable to complex fraud schemes. Moreover, legacy fraud systems lack the AI-ML capabilities needed to detect non-linear, evolving fraud schemes without negatively impa