FedNow Fraud Detection: Real-Time Risk on Instant Payments

A refreshed FedNow fraud detection guide covering real-time payment risk, the $10 million transaction limit, account activity thresholds, and analyst-ready controls.

A refreshed FedNow fraud detection guide covering real-time payment risk, the $10 million transaction limit, account activity thresholds, and analyst-ready controls.

AI in fraud detection guide for U.S. banks: 2026 fraud data, SR 26-2 governance, scam controls, payee risk, and analytics KPIs.

A refreshed field guide for fraud analysts covering scam tactics, risk signals, controls, KPIs, and customer interventions for bank scam prevention.

What Is First-Party Fraud? First-party fraud in banking is an increasingly critical issue that occurs when real customers use their own identity to commit fraudulent acts. It is a major contributor to financial losses, particularly in U.S. institutions. First-party fraud…

A refreshed guide to synthetic identity fraud, covering why synthetic identities are hard to detect, how AI changes the threat, and what signals banks should monitor.

Account Takeover Fraud (ATO) is one of the fastest-growing cyber threats, causing billions in losses globally. Learn how advanced tools like BioCatch, Duo Security, and ExpressVPN can safeguard your accounts against unauthorized access. Discover strategies, real-world success stories, and actionable tips to stay protected.

In today’s digital economy, Alation and Snowflake integration for data governance is transforming how organizations manage their data. From financial institutions to retail giants, businesses are leveraging this powerful combination to harness data for decision-making, unlocking insights while ensuring that…

In the rapidly evolving landscape of 2024, Generative Artificial Intelligence (GAI) stands at the forefront of a technological revolution, reshaping industries and redefining the boundaries of innovation. This excerpt delves into the transformative power of GAI, highlighting its pivotal role in driving forward scientific discovery, particularly within the realm of materials science. With the ability to semantically interpret and manipulate input data, GAI emerges as a critical tool in predicting material performance, enabling the discovery of novel materials, and facilitating the inverse design process. This not only accelerates the exploration of vast chemical spaces but also integrates complex domain knowledge, pushing the frontiers of what's possible in material science. As we navigate through the challenges and opportunities presented by GAI, its impact on economic growth and the broader scientific community becomes increasingly evident, marking a new era of AI-driven innovation.

In the turbulent waters of the 21st-century global economy, a significant transformation is underway. The rise of China as a global economic powerhouse and the ensuing Tech Cold War (TCW) between the United States and China represent a tectonic shift…

This article offers a detailed comparison of AMD's Instruction-Based Sampling (IBS) and Intel's Precise Event-Based Sampling (PEBS), highlighting how these technologies shape CPU performance analysis. Through insights from a key study, it explores the accuracy, stability, and functional differences of AMD and Intel's event sampling, providing essential knowledge for hardware designers and software developers.