Traditional fraud prevention can’t keep up with advanced scammers. Anomaly detection provides the essential protection required in today’s digital-first world.
In today’s digital-first world, scammers use more sophisticated tactics to target consumers. Traditional fraud detection, largely rule-based and reactive, struggles to keep pace. This increases the risk of fines, operational costs, and loss of brand trust.
From account takeover to wire fraud, learn more about the major forms of financial crime in our comprehensive glossary.
Enter anomaly detection: a dynamic, AI-powered approach that signals a paradigm shift in fraud prevention.
Anomaly detection is rooted in identifying anomaly signals, or subtle changes in consumer behavior, patterns, or transactional context that deviate from the norm. These signals are continuously reanalyzed to generate precision-led insights without exposing personally identifiable information (PII), providing businesses with powerful, privacy-conscious tools to anticipate and counter emerging threats.
Unlike static systems that depend on predefined rules, anomaly detection models continuously learn from evolving transaction data. They empower businesses to adapt to new fraud strategies and environmental changes in real time, offering resilience and agility where it matters most.
Interested in learning how cross-industry collaboration can help improve your fraud strategies? Watch this on-demand webinar featuring experts from Mastercard and ACI Worldwide.
As payment behavior grows increasingly complex, anomaly detection is no longer optional — it’s foundational. Forward-looking organizations are aligning with intelligence-led strategies to proactively outpace fraudsters and secure their place in the future of digital commerce.
Built for Security. Built to Scale. Built to Lead.
ACI Worldwide’s AI-powered anomaly models are engineered to meet the demands of modern payment ecosystems. Fraud teams can act quickly with intuitive dashboards, automated alerts, and deep behavioral insights. These models scale across transaction volumes, maintaining detection accuracy and user experience.
