Abstract: A fraudulent transaction detection method comprises: performing feature mapping processing on each of a plurality of transaction data to generate corresponding feature vectors; converting the feature vectors of a transaction to be detected into integrated feature vectors based on a first self-learning model; respectively converting the feature vectors respectively of at least one time sequence transaction into time sequence feature vectors based on a second self-learning model; combining the integrated feature vectors and each of the time sequence feature vectors corresponding to each of the time sequence transactions to form depth feature vectors; classifying the depth feature vectors based on a third self-learning model to determine whether the transaction to be detected is a normal transaction or a fraudulent transaction.

Abstract: A fraudulent transaction detection method comprises: performing feature mapping processing on each of a plurality of transaction data to generate corresponding feature vectors; converting the feature vectors of a transaction to be detected into integrated feature vectors based on a first self-learning model; respectively converting the feature vectors respectively of at least one time sequence transaction into time sequence feature vectors based on a second self-learning model; combining the integrated feature vectors and each of the time sequence feature vectors corresponding to each of the time sequence transactions to form depth feature vectors; classifying the depth feature vectors based on a third self-learning model to determine whether the transaction to be detected is a normal transaction or a fraudulent transaction.