Supervised machine learning for distinguishing between risky and legitimate actions in transactions

Abstract

Risky actions versus non-risky actions in a transaction are identified and a fraud score associated with probabilities of the risky actions is updated accordingly for purposes of determining whether the transaction is likely or not likely to be associated with fraud. A machine-learning model is trained to predict the risky actions versus non-risky actions of a transaction based on the transaction features as a whole and compare the predicted action labels of risky and non-risky versus the actual actions taken in the transaction to calculate probabilities of risky actions taken and output a risk or fraud score based thereon. Higher probabilities correlate with lower risk scores and vice versus.