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25/06/2026 12:25 am
TL;DR: Financial institutions are facing synthetic identities, account takeovers, APP scams, BNPL abuse, and AI-driven bot fraud while AML, KYC, and sanctions obligations grow more complex, according to Transmit Security and KuppingerCole. The real shift is that fraud prevention is becoming an identity governance problem, where customer identity, behavioural signals, and investigation workflows have to be managed together rather than in separate control planes.
NHIMG editorial — based on content published by Transmit Security: fraud reduction intelligence platforms for finance
By the numbers:
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes, and as quickly as 9 minutes in some cases.
Questions worth separating out
Q: How should financial institutions govern fraud prevention inside CIAM workflows?
A: They should treat fraud prevention as part of the identity control plane, not a separate overlay.
Q: When does behavioural fraud detection become effective enough to change decisions?
A: It becomes effective when it can influence action before the fraud event completes, such as at onboarding, login, or pre-transaction review.
Q: What do teams get wrong about predictive AI in fraud investigations?
A: They often assume AI can compensate for incomplete case data.
Practitioner guidance
- Map fraud controls to identity control owners Assign explicit ownership for onboarding, authentication, behavioural risk, and case escalation so fraud signals are not stranded between IAM and fraud operations.
- Review where behavioural signals trigger action Check that device intelligence, session anomalies, and compromised credential alerts can step up, throttle, or block activity before a suspicious transaction is approved.
- Test orchestration across AML, KYC, and fraud workflows Verify that identity proofing providers, sanctions screening, and fraud scoring exchange evidence cleanly, with no manual handoff that delays a decision.
What's in the full article
Transmit Security's full article covers the operational detail this post intentionally leaves for the source:
- KuppingerCole scoring breakdown across product, innovation, and market leadership dimensions.
- Specific fraud use cases such as APP scams, mule activity, BNPL abuse, and credit card fraud patterns.
- Operational description of the platform's CIAM and fraud orchestration features.
- Predictive AI workflow examples for case summaries and analyst queries.
👉 Read Transmit Security's analysis of fraud reduction intelligence for finance →
Fraud reduction intelligence and CIAM: what IAM teams need to know?
Explore further
25/06/2026 9:20 am
Fraud reduction is becoming an identity governance discipline, not a point-solution category. Once fraud prevention, customer identity, and case management are fused in the same platform, the governance question changes from detection coverage to control ownership. IAM teams, fraud teams, and compliance leads now share the same customer trust surface, which means the operating model has to define who owns signals, decisions, and escalation paths.
A few things that frame the scale:
- 85% of organisations lack full visibility into third-party vendors connected via OAuth apps, 38% have no or low visibility, and a further 47% have only partial visibility, according to The State of Non-Human Identity Security.
- Another 1 in 4 organisations are already investing in dedicated NHI security capabilities, with an additional 60% planning to do so within the next twelve months, according to The State of Non-Human Identity Security.
A question worth separating out:
Q: Who should own fraud controls when IAM and fraud teams overlap?
A: Ownership should sit with the team accountable for the decision point, while IAM, fraud, and compliance all contribute the signals and policy. If one group owns alerts and another owns action, attackers exploit the gap. Shared governance matters more than shared tooling.
👉 Read our full editorial: Fraud reduction intelligence now sits inside identity governance
