Fintech teams should use stronger device and user identification so they can distinguish trusted customers from suspicious sessions earlier in the journey. That reduces unnecessary step-up checks, lowers churn, and improves conversion. The practical goal is to verify more accurately up front, then reserve extra friction for situations that actually look risky, rather than applying the same burden to everyone.
Why Better Identification Lets You Reduce Friction
The practical trade-off is not fraud prevention versus user experience, it is how early you can make a confident trust decision. If you can distinguish a known-good customer device, browser, or session from an unusual one before the user reaches a high-risk action, you can keep the normal journey smooth and reserve extra checks for genuinely suspicious paths.
That approach works best when identification signals are combined, not treated as a single proof point. Strong device reputation, stable session behaviour, and user history can lower false positives, but each signal should be judged as part of a confidence model rather than a hard pass/fail rule.
Done well, this also improves operational consistency. Teams spend less time forcing blanket step-up checks that catch low-risk traffic, and more time tuning the conditions that should trigger friction because they actually correlate with account takeover or payment abuse.
Where Fraud Controls Usually Go Wrong
The most common mistake is using a coarse rule such as “unknown session means challenge.” That catches some bad actors, but it also forces legitimate users through repeated authentication when they switch networks, devices, or payment flows. The result is avoidable drop-off, higher support volume, and more incentive for users to abandon the flow.
Another failure mode is over-relying on one signal such as IP reputation. Fraud actors adapt quickly, and legitimate users change IPs for reasons that have nothing to do with risk. A better control design looks at the whole session shape, the device’s continuity, and whether the current action matches prior behaviour.
Fintech teams should also be careful not to let “friction reduction” become a reason to weaken assurance altogether. The goal is selective friction, not blind trust. If the risk model cannot explain why a session was allowed through without a step-up event, the control is probably too opaque to govern safely.
Risk and Threat Considerations
Fraud controls fail when attackers can imitate normal user behaviour well enough to avoid triggering step-up checks, or when legitimate customers are challenged so often that teams relax the control. The real risk is a control that is both too noisy and too predictable, because it either blocks good users or becomes easy to game.
Failure mechanism: Static challenge rules, weak device continuity, and poor session correlation let adversaries blend into ordinary traffic, while over-broad rules create unnecessary authentication prompts for low-risk users.
Impact: More account takeover attempts slip through, legitimate conversion drops, and the organisation loses confidence in the control because it creates churn without materially improving fraud outcomes.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0 and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | PR.AC-1 — Identity Management, Authentication, and Access Control | Identity assurance underpins selective trust decisions in fraud screening. |
| DE.CM-1 — Monitoring for Anomalies and Events | Behavioural anomaly monitoring helps distinguish risky sessions from trusted ones. | |
| Recommendation — Strengthen identity and access checks for sessions that show elevated fraud risk. Monitor session anomalies to trigger step-up only when risk signals change. | ||
| CIS Controls v8 | 6.1 — Establish an Access Control Policy | Policy should define when extra authentication is required and when it is not. |
| Recommendation — Define risk-based step-up rules so low-risk users are not challenged unnecessarily. | ||
| OWASP Agentic AI Top 10 | A1 — Prompt Injection and Instruction Manipulation | Selected only if automated risk decisions are influenced by AI-assisted fraud workflows. |
| Recommendation — Constrain AI-assisted decisioning so it does not create unreviewed access decisions. | ||
Practitioner Guidance
What to prioritise: Make the first trust decision as early as the session allows, then escalate only when the user, device, or transaction context meaningfully changes. If the control cannot distinguish a stable returning customer from a newly risky session, it is not yet tuned well enough to reduce friction safely.
What to verify: Check whether your step-up logic is driven by risk evidence rather than by convenience rules, and verify that the exceptions are auditable. A good control explains why a known device was trusted, why a new device was challenged, and which signal caused the decision.
Practitioner takeaway: The best fraud control is selective, explainable, and conservative with friction, it challenges uncertainty, not every login or payment action.
Related resources from NHI Mgmt Group
- How should security teams reduce identity fraud without blocking legitimate users?
- How should teams reduce return abuse without making honest customers jump through hoops?
- How should telecom teams reduce SIM registration fraud without blocking legitimate users?
- How can security teams reduce marketplace fraud without blocking legitimate users?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 20, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
