A false positive occurs when a compliance system, such as a screening or transaction monitoring tool, incorrectly flags a customer, transaction or activity as suspicious or high-risk when, in reality, no risk or violation is present. 

False positives are common when matching customer information against sanctions lists, politically exposed person (PEP) databases or adverse media sources when going through Know Your Customer (KYC) or Anti-Money Laundering (AML) processes. They can result from factors such as common names, incomplete data, outdated records or overly broad matching criteria. While false positives do not indicate actual financial crime, they require compliance teams to manually review and clear the alerts, which can be time-consuming and resource-intensive.

It’s important to strike a balance between minimizing false positives and ensuring that genuine risks are not overlooked. Through refined screening methodologies, better data quality, risk-based approaches and the use of advanced technologies like artificial intelligence and machine learning, organizations can work towards reducing the rate of false positives.