How can fraud and IAM teams work from the same evidence base?

Why This Matters for Security Teams

Fraud and IAM teams often investigate the same account event from different angles, but the evidence they need overlaps far more than most operating models admit. A login anomaly, device change, or impossible travel alert can be a fraud signal, an access risk, or both. When those signals are split across tools and queues, teams miss the chain of events that turns authentication weakness into account takeover, payment abuse, or credential abuse. The problem is not lack of data, but lack of a shared evidence model and review process.

The case for shared evidence is stronger in environments where secrets and access paths are already fragile. NHI Management Group’s Ultimate Guide to NHIs notes that 80% of identity breaches involved compromised non-human identities such as service accounts and API keys, which reinforces a broader lesson: security teams need to connect outcomes, not just logs. NIST’s NIST SP 800-53 Rev 5 Security and Privacy Controls supports that same principle through centralized control evidence and auditability. In practice, many security teams encounter the overlap only after compromise has already been confirmed, rather than through intentional joint review.

How It Works in Practice

The shared-evidence model works best when fraud and IAM agree on a common set of facts, then interpret those facts through their own control lens. That usually means confirmed fraud outcomes, authentication logs, device intelligence, session metadata, credential lifecycle events, and access patterns are preserved in a reviewable case record. The record should be time ordered so analysts can see whether a device change preceded a risky login, whether a password reset followed suspicious behavior, or whether a new access grant enabled the abuse.

Operationally, the teams should align on a few basic rules:

• Use one case identifier across fraud, IAM, and SOC workflows so evidence is not duplicated or lost.
• Tag evidence by confidence level, such as suspected, corroborated, or confirmed, so policy changes are based on verified patterns.
• Separate raw telemetry from decisions, because the same event may justify a fraud block but only a step-up authentication challenge.
• Feed confirmed outcomes back into IAM controls, especially adaptive authentication, device trust, session revocation, and risky access review.

This approach is especially useful where credential abuse is part of the fraud path. NHIMG’s TruffleNet BEC Attack shows how stolen credentials can drive business email compromise at scale, while the 2024 Non-Human Identity Security Report notes that 88.5% of organisations say their non-human IAM practices lag behind or are merely on par with human IAM. That gap matters because the same evidence base can reveal both compromised human accounts and abused service identities. These controls tend to break down when fraud and IAM are forced to share only summaries instead of the underlying event trail, because summaries hide the sequence needed to prove causality.

Common Variations and Edge Cases

Tighter evidence sharing often increases review overhead, requiring organisations to balance speed against the need for corroboration. Current guidance suggests that the shared model should not collapse every alert into one queue, because fraud teams and IAM teams still make different decisions. Fraud may focus on financial loss and recovery, while IAM may focus on access restriction, entitlement tuning, and account hygiene.

There is no universal standard for this yet, but several edge cases are common. First, some identity events are noisy without being malicious, especially in high-velocity customer environments where device churn is normal. Second, some access patterns matter only when combined with external signals such as chargeback history or identity proofing failures. Third, service accounts and API keys need different handling because they lack user-like behavioral cues, which makes confirmed outcomes and rotation evidence more important than login heuristics alone. Best practice is evolving toward joint case review, but the evidence should still be governed by role-specific access controls and retention rules. NIST guidance on control traceability and incident evidence remains a useful anchor, while NHIMG research such as the JetBrains GitHub plugin token exposure shows how leaked tokens can blur the line between fraud, supply chain abuse, and identity compromise.

Related resources from NHI Mgmt Group

• How can fraud, payments, and IAM teams work from the same control model?
• How can IAM and fraud teams work from the same trust model?
• Why do fraud and AML teams need to work from the same identity signals?
• How do IAM and fraud teams work better together on identity proofing?