Data-driven financial fraud detection using hybrid artificial and quantum intelligence

The unauthorized use of a cardholder’s financial data, resulting in significant losses to individuals and companies, is known as credit card fraud. The increasing frequency and complexity of such fraud in the digital era highlight the absolutely vital need for reliable and accurate detection systems. Under the specific challenge of extreme class imbalance, this work investigates the credit card fraud identification performance of several Machine Learning (ML), Deep Learning (DL) and Quantum Machine Learning (VQC) algorithms. The study uses a commonly used dataset consisting of 284,807 anonymized credit card transactions, of which only 492 (0.17%) are fraudulent. To solve the class imbalance, we produced synthetic samples of the minority class utilizing the SMOTE, thus raising model sensitivity. Moreover, we enhanced model performance by means of hyperparameter tuning applied with Grid Search, Random Search, and Keras Tuner. Combining deep learning-based feature extraction with ensemble learning approaches, together with effective data balancing and hyperparameter tuning, yields, according to the results, a very accurate and dependable credit card fraud detection system. The hybrid model that includes AutoEncoder for feature extraction, Bagging (Random Forest), and Boosting (XGBoost) was the best, with 100% accuracy. This shows that this integrated technique is better than others. This approach provides a sensible analysis for building robust, real-time fraud detection systems for practical financial applications.