Fraud losses continue to rise at an alarming pace. In 2024, consumers and businesses reported losing more than $12.5bn to fraud, marking a 25% increase year on year.
According to AiPrise, that growth underlines a reality risk teams across FinTech, payments and crypto already feel daily: criminal tactics are evolving faster than many traditional controls. Fraud no longer arrives as a single obvious red flag. Instead, it moves at speed, hides within legitimate activity and probes systems repeatedly until a weakness is found.
Against this backdrop, rule-based fraud detection remains a critical foundation for any risk stack. While machine learning and advanced analytics add depth, rules are still the first layer that determines which activity flows freely and which deserves scrutiny. Without clearly defined rules, platforms either trust too much or create unnecessary friction for genuine users. Neither approach works when organisations are trying to balance growth, regulatory compliance and customer experience at scale.
At their core, fraud detection rules translate risk policy into enforceable logic. They allow businesses to act in real time, blocking, stepping up or allowing activity in milliseconds. Just as importantly, rules provide explainability. When regulators, auditors or internal teams ask why a transaction was flagged, a well-designed rule set provides clear answers. This transparency is increasingly essential as scrutiny of automated decision-making grows.
One of the most enduring controls is IP velocity monitoring. Fraud rings rely heavily on automation, generating bursts of sign-ups, logins or payment attempts that appear harmless in isolation. Velocity analysis exposes this coordination by focusing on how quickly activity accumulates from the same source. When combined with contextual thresholds for different flows, such as onboarding versus payouts, velocity checks help surface intent before financial damage occurs.
Email age and domain risk rules offer another early signal. Fraud operations frequently depend on newly created or disposable email addresses because they are cheap and short-lived. By applying stricter controls to low-quality domains or newly registered inboxes, platforms can prevent risky users from entering the system quietly and only surfacing later as losses or compliance issues.
Device consistency has also become a powerful indicator. While identities can be rotated easily, devices are harder to change at scale. Legitimate users tend to return from the same environment, whereas fraudsters often rely on emulators or spoofed devices that behave inconsistently. Monitoring device stability, especially during sensitive actions such as withdrawals or credential changes, helps identify account compromise at critical moments.
On the payments side, suspicious BIN range monitoring remains highly relevant. Certain issuers and card types repeatedly appear in confirmed fraud cases, particularly following major data breaches. Applying selective friction to high-risk BINs, rather than blanket restrictions, allows businesses to protect revenue while maintaining conversion rates.
Geography also plays a role. High-risk country triggers enable organisations to align fraud controls with regulatory and sanctions expectations without blocking legitimate cross-border activity. By applying country-aware logic to traffic spikes, payment corridors and profile mismatches, risk teams can respond proportionately to jurisdiction-level threats.
Transaction amount anomaly checks add further context. Fraud often starts with micro-transactions to test success before escalating rapidly. Comparing transaction values against historical behaviour helps surface intent that would otherwise blend into approved traffic. Finally, account takeover indicators bring these signals together by focusing on behavioural shifts, unusual login patterns and rapid profile changes that suggest loss of legitimate control.
Taken together, these rules form a resilient first line of defence. They do not replace advanced analytics, but they remain essential for controlling losses, reducing false positives and keeping risk decisions explainable in an increasingly complex threat landscape.
Copyright © 2026 RegTech Analyst
Copyright © 2018 RegTech Analyst
