Rule-based fraud detection relies on predefined conditions, such as flagging transactions above a certain amount or from specific locations. While effective for known scenarios, these systems struggle to identify new or evolving fraud techniques. AI-driven fraud detection analyzes large volumes of behavioral and transactional data, recognizes subtle patterns, and adapts as fraud tactics change. This allows financial institutions to detect sophisticated fraud earlier, reduce false alarms, and improve customer experience without constantly updating manual rules.
