A false negative occurs when fraud detection or identity verification systems fail to identify a fraud attempt and incorrectly classify it as legitimate, allowing it to pass the security check. For example, in identity verification, a false negative would happen if an application submitted with a stolen identity and/or forged documents was approved, or in fraud detection, it would occur if an unauthorized transaction was not flagged and was approved. False negatives can result in direct financial losses, regulatory issues, and reputational damage.
Use case/ examples for false negatives
Detection tuning: Calibrating fraud detection systems to minimize false negatives by analyzing missed fraud cases, identifying detection gaps, and adjusting scoring thresholds and rules accordingly, or using machine learning to train detection algorithms on missed cases and emergent patterns.
Model validation: Regularly testing identity verification and fraud detection models against known fraud samples to measure their false negative rates and ensure that their detection capabilities remain effective against evolving tactics.
