The Toronto-Dominion Bank
SYSTEM AND METHOD FOR MACHINE LEARNING BASED DETECTION OF FRAUD

Abstract:

A computing device for fraud detection of transactions for an entity is disclosed, the computing device receiving a current customer data comprising a transaction request for the entity. The transaction request is analyzed using a trained machine learning model to determine a likelihood of fraud via determining a difference between values of an input vector of pre-defined features for the transaction request applied to the trained machine learning model and an output vector having corresponding features resulting from applying the input vector. The trained machine learning model is an unsupervised model trained with only positive samples of legitimate customer data having values for a plurality of input features corresponding to the pre-defined features for the transaction request and defining the legitimate customer data. The difference is used to automatically classify the current customer data as either fraudulent or legitimate based on a comparison of the difference to a pre-defined threshold.