As we close out 2025, one headline that captures the significant shift reshaping payments and commerce is how AI-powered fraud prevention is reshaping how we fight financial crime and better serve our customers. New breakthroughs in artificial intelligence and machine learning are fundamentally transforming how we protect consumers and maintain trust in digital financial ecosystems.
At Block, we’ve witnessed this evolution firsthand. Our AI-powered scam prevention systems have protected customers from over $2 billion in potential fraud losses since 2020, with our confirmed scam rate remaining below 0.01% of all peer-to-peer transactions. The real story isn’t just about the money saved, but how AI is expanding what’s possible in real-time fraud detection and prevention.
The traditional approach to financial crime prevention was reactive: detect, investigate, then respond. Today’s AI capabilities give us options to be more proactive. Machine learning algorithms now analyze thousands of data points in milliseconds, identifying suspicious patterns before fraudulent transactions complete. We’re moving from catching fraud faster to preventing it from happening in the first place.
This transformation extends beyond simple transaction monitoring. Modern AI systems process multiple types of inputs, analyzing everything from transaction patterns and account behaviors to communication patterns and device fingerprints. At Cash App, our “Scam Prevention Machine” uses models that trigger warnings, block high-risk transactions or escalate cases for human review, all in real time. When customers receive these warnings, they cancel flagged transactions more than half the time, demonstrating AI’s ability to support informed decision-making.
The implications reach into many areas of financial services. In credit decisions, artificial intelligence helps us serve previously underbanked populations by analyzing real-time ecosystem data rather than relying solely on traditional credit scores. Through Cash App Borrow, 70% of our active borrowers have credit scores below 580, yet maintain repayment rates above 97%, showing that AI can expand access while maintaining responsible risk management.
Child protection represents another important area. Our research explores how AI can help identify and prevent financial exploitation of minors in digital environments, as detailed in our white paper on teen protection in the digital economy. Similarly, our work on combating scams demonstrates how AI-driven education campaigns and real-time interventions create layered defense systems.
Looking ahead, the next phase involves AI systems that don’t just detect known fraud patterns but can identify emerging attack vectors. Generative AI is increasingly being used by bad actors to create sophisticated deepfakes and synthetic identities. Our response involves using advanced AI to detect AI-generated fraud attempts, creating an ongoing technological challenge that pushes the boundaries of what’s possible.
The regulatory landscape is evolving alongside these developments. Policymakers increasingly recognize that effective AI governance in financial services involves ensuring these tools are deployed responsibly and transparently while enabling innovation.
As we enter 2026, AI has already begun transforming financial crime prevention. The question is whether financial institutions can adapt effectively to harness AI’s potential while maintaining the human oversight and ethical guardrails that preserve consumer trust. Those who navigate this balance well will help define the future of secure, accessible financial services.
The transformation is underway. Success will depend on thoughtful implementation and responsible innovation.
