Evaluating the computational advantages of the Variational Quantum Circuit model in Financial Fraud Detection

Home banking and digital payments diffusion has greatly increased in recent years. As a result, fraud has also dramatically grown, resulting in the loss of billions of dollars worldwide every year. Therefore, banks and financial institutions are required to offer clients increasingly effective and sophisticated services for illegal transaction detection. Machine learning strategies are commonly employed for this crucial application. However, classical models are not satisfactory enough in highly unbalanced classification tasks like fraud detection. Quantum machine learning, working intrinsically in a higher dimensional computation space thanks to superposition and entanglement, can express more complex models than its classical counterpart and thus could provide a significant advantage in identifying potential illegal transactions. This work aims to analyze the potential of quantum classifiers in the fraud detection context, focusing on the Variational Quantum Circuit (VQC) model. The study has been led by exploiting a dataset based on real transactions provided by Intesa Sanpaolo of 500000 items with 15 features. Considering the limitations of contemporary Noisy Intermediate-Scale Quantum (NISQ) computers and quantum simulators, the dataset has been reduced in the number of transactions and features, exploiting Principal Component Analysis (PCA). The results obtained have been compared on equal terms with those of the most commonly employed classical methods, such as Logistic Regression, Random Forest, XGBoost, Support Vector Machine, and Neural Networks, obtaining a better classification quality in terms of recall. Even though this work is preliminary, the results are encouraging and prove the quantum models’ potential in highly unbalanced classification tasks.