Building Resilience in Banking Against Fraud with Hyper Ensemble Machine Learning and Anomaly Detection Strategies

Traditional methods of fraud detection rely on rule-based systems or supervised machine learning models that require labelled data and domain knowledge. However, these methods have limitations such as high false positive rates, low scalability, and vulnerability to adversarial attacks. In this paper, a novel approach for bank fraud detection using hyper ensemble machine learning (HEML), which combines multiple unsupervised and semi-supervised models with different features and hyperparameters to achieve high accuracy and robustness, including—logistic regression (LR), decision tree (DT), support vector machine (SVM), neural network (NN), one-class SVM (OCSVM), and isolation forest (IF) are studied.The approach is evaluated on a real-world dataset of bank transactions from a large European bank and compared with several baseline methods.The accuracies of base learners and ensemble learners on the test data of LR, DT, SVM, NN, OCSVM and IF are as follows in order 0.95,0.91,0.96, 0.97, 0.93, 0.92. The results show that HEML outperforms the baselines in terms of precision, recall, F1-score, and AUC-ROC, while reducing the computational cost and human intervention. Additionally, the effectiveness of HEML in detecting new types of frauds that were not seen in the training data is demonstrated. Thus, HEML is a promising technique for bank fraud detection that can adapt to dynamic and complex fraud scenarios. By utilizing multiple models and features, HEML can provide accurate and robust fraud detection while reducing false positives and minimizing human intervention. By employing multiple models and features, HEML has the potential to improve the financial security and stability for both banks and their customers. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.