Experiments on Fraud Detection use case with QML and TDA Mapper

Abstract

In the era of online financial transactions, it is significant for the credit card firms to be equipped with capabilities to identify fraudulent credit card transactions. This work covers study and implementation of two approaches for developing a credit card fraud detection model. First one, with hybrid quantum neural networks. In recent times, Quantum Computers (QC) are making their footprints into AI/ML domain. Quantum neural networks (QNN) hybrid with classical neural net has been used in various tasks such as – natural language processing, image processing etc. The second approach is with Topological Data Analysis (TDA). Finding topological structure in the input data also become relevant from the perspective of noise reduction. The visualization capabilities of TDA can become an aid in classification of credit card fraud as well. TDA is implemented with mapper based method here. In hybrid QNN, we are covering a reference implementation of Xanadu’s StrawberryFields, where a classical network processes the input to be fed into a QNN model. Although technique wise these two approaches are drastically different, for the sake of generalization we implement TDA and hybrid QNN with a publicly available credit card fraud detection dataset. We tested with balanced fraud and genuine features and hybrid QNN model provides accuracy of 89.5%, whereas TDA mapper with our novel approach of classification provides an accuracy of 94%.