Converging AI innovation and quantum security for data-driven compliance, financial crime re-regulation

This study discusses the use of classical and quantum machine learning models to detect fraudulent bank transactions. Random Forest model was tested on credit card fraud detection data set and scored large percentage 99.95, AUC-ROC score/ROC is 1.0 and F1 scores are high. The most influential predictors were identified to be key features including the amount of transaction, periods between transactions, and location. In order to avoid the problem of class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was utilized, which enhanced the work of the model. Another promising study of quantum hardware scalability limits, but with multiple serious limitations, was the Quantum Support Vector Classifier (QSVC), which faces difficulty in qubit coherence and scalability challenges. These limitations did not allow the model to effectively process large data sets to better accommodate real world applications. Nevertheless, quantum models have the potential to improve the fraud detection system with developing quantum technology. This study brings out the usefulness of Random Forest in detecting fraud cases and outlines the opportunities of quantum models in the future, recommending future research, such as quantum-classical hybrid models, and the enhancement of quantum computers to meet real-time needs.