Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models
This work addresses fraud detection in finance, but it is incremental as it compares existing QML models without introducing a new method.
The study compared four Quantum Machine Learning models for financial fraud detection, finding that the Quantum Support Vector Classifier achieved the highest performance with F1 scores of 0.98 for both fraud and non-fraud classes.
In this research, a comparative study of four Quantum Machine Learning (QML) models was conducted for fraud detection in finance. We proved that the Quantum Support Vector Classifier model achieved the highest performance, with F1 scores of 0.98 for fraud and non-fraud classes. Other models like the Variational Quantum Classifier, Estimator Quantum Neural Network (QNN), and Sampler QNN demonstrate promising results, propelling the potential of QML classification for financial applications. While they exhibit certain limitations, the insights attained pave the way for future enhancements and optimisation strategies. However, challenges exist, including the need for more efficient Quantum algorithms and larger and more complex datasets. The article provides solutions to overcome current limitations and contributes new insights to the field of Quantum Machine Learning in fraud detection, with important implications for its future development.