High risk signals: Using data to prioritise fraud and other business risks

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By Richard Seaman, 27 April 2026

High risk signals are often hiding in plain sight. The difference between early detection and costly exposure often comes down to one thing: your data. 

Poor quality data can leave teams inundated with low value or misleading alerts, indicators, and anomalies. That noise can drown out the signals that truly matter. To reduce financial and reputational damage, leading organisations turn to trusted, unified data to surface fraud and other risks sooner.

Anomalies in company registries, abnormal financial performance, unusual ownership structures, links to shell companies, or unexpected changes in company control are all potential high risk signals. They can be early signs of fraud such as business misrepresentation, money laundering, or other forms of financial crime – and they often emerge well before losses are realised. 

Signal overload, poor signal quality, and misaligned detection models can cause fraud risk and other threats to go unnoticed. Relying on isolated signals or siloed datasets can also reduce visibility and create blind spots. In some cases, risk is identified but not acted on due to unclear ownership, governance gaps, or a lack of accountability. 

Limited analyst capacity, lack of investigative resources, or budget can further compound the problem, making the detection and mitigation of fraud and other risks an organisational challenge, not just an analytical one.

To cut through the noise, start by:

• Focusing on behavioural and transactional cues that indicate fraud or misuse.
• Setting sensible segmentation and escalation thresholds to separate priority risk from background signals.
• Building organisation-wide awareness and consistency so risk is understood, shared, and addressed early – even when signals are subtle and competing for attention.

High risk signals for fraud and other threats can be bucketed into seven signal categories: financials, group structure, company filings, location, principals, consortium relationships, and enquiries. Rather than reacting to every alert, organisations can set risk thresholds that trigger manual reviews based on the volume and severity of signals detected. This helps focus attention where it matters most – on the business relationships that carry the greatest risk.

Unified data is the foundation for effective detection and early response to fraud and other business risks. It enables organisations to spot high risk signals during onboarding, before making credit decisions, and throughout ongoing monitoring as the signals and risk profiles change over time.

Learn how Dun & Bradstreet can help you detect high risk signals, reduce exposure to fraud and other types of risk, while protecting your reputation and bottom line. Schedule your strategy session today.