Research Guide

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Secure Onboarding Maturity Scorecard

Most financial institutions are drowning in risk signals but starving for insights. Don’t let a fragmented onboarding stack be the […]

Inbound Payment Fraud: Detection and Mule Risk Modeling

Traditionally, financial institutions have focused on stopping high-risk outbound payments. But with authorized push payment (APP) fraud, the threat has […]

How Feedzai Closes the WhatsApp Scams Loophole

Welcome to the mirror-world, where voices can be faked, apps cloned, and every trusted interaction can be manipulated. WhatsApp scams […]

Feedzai IQ for Acquirer Risk Management

Criminals freely share tips, tactics, and technologies with other bad actors, enabling a thriving underworld  that cost over $1 trillion […]

Prevent Transaction Fraud in Banking

Fraud moves at the speed of money, and now that money moves in milliseconds, yesterday’s defenses won’t cut it. Every […]

Feedzai IQ™ for Retail Banks: Stop Fraud with Network Intelligence

Global scam losses reached $1.03 trillion in 2024. Criminals are sharing information and optimizing their attacks at an unprecedented rate. […]
With the emergence of authorized push payment (APP) fraud, banks need to apply greater focus on incoming payments that go directly to the criminal’s accounts. This is where money mules come into play, enabling criminals to move illicit funds to accounts they control. In the UK, new regulations require banks to extend fraud monitoring to both inbound and outbound payments. Other countries, including the US and Australia, are considering similar measures. Understanding the intricate dance between mule risk modeling and fraud detection becomes paramount as banks navigate this transformative phase. Learn how Feedzai delivers state-of-the-art, AI-driven tools for inbound payment fraud prevention capabilities and empowers banks to stay one step ahead of shifting regulations. Key Benefits: Regulatory Compliance: Ensure your bank remains at the forefront of the latest fraud prevention regulatory frameworks. Inbound Payment Focus: Transition from traditional methods to a comprehensive inbound monitoring approach. Mule Account Detection: Leverage AI and machine learning to counteract money mule operations effectively. Inbound-Payment-Fraud-Detection

Inbound Payment Fraud Detection & Mule Risk Modeling: Protect Your Reputation and Minimize Losses

Proactively combat authorized payment fraud, outsmart money mules, and safeguard your institution’s bottom line. Banks are losing the fight against […]

Inbound Payment Fraud Detection and Mule Risk Modelling: Prepare for Upcoming Reimbursement Regulation

With the emergence of authorized push payment (APP) fraud, banks need to apply greater focus on incoming payments that go […]
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Inbound Payment Fraud Detection and Mule Risk Modelling: Addressing the Upcoming PSR Reimbursement Regulation

The UK is taking bold steps to counteract the complexities of financial fraud. With the introduction of PSR 23/3, the […]
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How it Works: Digital Trust

Banks need to protect customers and build trust from login to logout, and at every transaction that occurs in between. […]
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How Can Banks Adopt a FRAML (Fraud & AML) Solution?

FRAML is shaking up the way financial institutions combat risk. While fraud and anti-money laundering has typically been housed in […]
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Account Takeover Fraud: Becoming Preventative vs. Reactionary

Account takeover (ATO) fraud attacks used to be about bad actors using stolen credentials to take over a victim’s bank […]
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