True negative, also known as specificity, is the ratio of correctly identified non-fraud cases to total non-fraud cases. A true negative test result is one that does not detect the condition when the condition is absent. It is an outcome where the model correctly predicts the negative class, for example if a disease test correctly identifies a healthy person as not having that disease.