Safe transactions: AI-augmented payment security and monitoring network

This paper introduces Safe Transactions, an AI-augmented customer profiling and payment monitoring platform designed to address the vulnerabilities in banking ecosystems from the customers’ perspective. Traditional fraud detection is often limited to after the payment has been sent to the bank for remittance while customer profiling is legally limited to each bank’s unilateral relationships with its customers.

The network’s primary innovation lies in its multi-stream data architecture. First, it aggregates behavioral and historic patterns to establish a holistic identity verification model. Second, it incorporates actual data generated from pre-testing payments—analyzing transactions before they are formally submitted to payment institutions for remittance. By applying advanced machine learning algorithms to this combined dataset and agglomerated mapping against profiles the system identifies subtle anomalies and high-risk signatures that traditional, single-point monitoring fails to capture. This proactive framework significantly enhances the accuracy of fraud detection, identifies payment screening system alerts, both true and false positives, and ensures a higher integrity of the global remittance pipeline through collaborative, AI-driven intelligence.