Feedzai, the global leader in AI-native financial crime prevention, today announced two groundbreaking initiatives at the HumanX conference: ScamAlert, an advanced GenAI-powered fraud prevention agent, and the TRUST Framework, a comprehensive approach to ensuring ethical, scalable, and responsible AI deployment. These innovations reinforce Feedzai’s dual mission: stopping financial crime while ensuring AI is used responsibly.
Scams have escalated into a global crisis, costing consumers over $1 trillion annually as fraudsters leverage GenAI to execute increasingly sophisticated attacks. Traditional fraud prevention tools often struggle to address these evolving threats, particularly as many scams involve customers unknowingly validating fraudulent transactions.
ScamAlert is a revolutionary AI agent designed to break the scam cycle at its source. By providing real-time alerts and actionable insights, ScamAlert transforms customers from passive victims into active defenders against fraud. Banks can integrate ScamAlert in multiple ways, from self-service payment verification to risk engine augmentation, allowing for real-time scam detection before transactions are processed.
“Scammers are evolving fast, using AI to trick even the most cautious consumers,” said Nuno Sebastiao, CEO and Co-Founder of Feedzai. “With ScamAlert, we’re giving people a powerful AI assistant that warns them in real time, because the best fraud prevention starts before money leaves your account. Fraud prevention is not just a technology problem; it requires collaboration between banks, tech companies, and consumers.”
ScamAlert’s multimodal AI analyzes text, images, and transaction data to detect scams before they happen. Customers can upload screenshots of suspicious emails, text messages, marketplace listings, or invoices directly through their mobile banking app, receiving instant feedback on the legitimacy of their transactions. By leveraging Feedzai’s global intelligence network, ScamAlert enhances banks’ risk assessment models, ensuring that fraud detection remains proactive and effective.
