False positives: The silent drain on fraud teams

False positives are the tax fraud teams pay for incomplete visibility.

When data is fragmented, fraud detection systems err on the side of caution, generating alerts that require manual analysis but rarely result in confirmed fraud.

Especially across banking and payment providers, false positives can account for 80–95% of all alerts. This isn’t just operationally expensive; it directly impacts customer experience as when legitimate transactions are blocked, customers lose trust. The knock-on effect of that is that analysts spend hours validating non-fraud events, and real threats get less attention.

False positives grow for three reasons:

1. Siloed data creates incomplete signals

A transaction might appear suspicious in isolation but completely legitimate when enriched with context, for example, device reputation, behavioural consistency, historic customer patterns or location history. When that context lives in other systems, alerts become inaccurate.

2. Rigid rule-based systems trigger predictable noise

Legacy detection engines often lack the nuance required to distinguish between unusual-but-legitimate customer behaviour and genuine anomalies. Without enrichment, the safest option is to escalate everything.

3. Investigations are slow

When analysts lack unified visibility, they must manually cross-reference multiple systems. This slows triage and increases backlog.

In a search-led architecture the equation changes. By creating a single fraud data lake, analysts can instantly query across all systems - payments, identity, AML, cyber, device data - without waiting for transformation or pre-modelled schemas.

Examples of how context reduces false positives:

• A transaction flagged as unusual may match behavioural patterns from thousands of past legitimate sessions
• A device that appears unfamiliar may share characteristics with a trusted customer profile
• A geographic mismatch might correlate with recent travel patterns
• A suspicious payment to a new beneficiary may be part of a verified customer workflow

This kind of enriched insight lowers noise, allowing analysts to prioritise what matters.

The impact is significant: smaller alert queues, faster investigations, improved customer experience, and more capacity for strategic fraud analysis. Reducing false positives is not simply an efficiency win; it’s one of the fastest ways an organisation can improve fraud-programme ROI and reduce operational cost.

The institutions that excel in fraud defence do not detect more fraud; they detect better. Lowering false positives is the quickest path to that maturity.