Abstract

Banks are known to incur substantial financial loss every year because of financial fraud in the banks. This can be mitigated through early detection, the development of a counter-strategy, and the recuperation of losses caused by such fraud. This paper presents a proposed ensemble architecture that integrates Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN) to overcome the limitations of class imbalance and multi-layered patterns in transactional data during Credit Card Fraud Detection (CCFD). With the Kaggle CCFD dataset, some preprocessing methods were performed, such as balancing data using the Synthetic Minority Oversampling Technique (SMOTE) and the top features selected using the Random Forest importance, as well as normalizing the values using Min-Max scaling. The proposed ensemble model reached a true rate of 98.67, a true accuracy of 98.51, a recall of 99.89 and an F1-score of 98.34 - far outperforming the traditional classifiers of Decision Trees (DT), Logistic Regression (LR), Naive Bayes (NBs), and K- K-Nearest Neighbors (KNN). These results demonstrate the ability of the ensemble model to be effective at modeling complex non-linear relationships, minimizing misclassification, and making predictable forecasts in extremely imbalanced data sets. The results highlight that ensemble machine learning (ML) methods have the capacity to augment current fraud detection systems and provide a foundation for future research to create stronger, larger, and safer financial fraud detection systems.

Keywords

Financial Risk Management, Anomaly Detection, Fraudulent Transactions, Ensemble Machine Learning, Data Mining Techniques, Classification Algorithms, Predictive Analytics, Banking Sector Security, Credit Card Fraud Detection.

Conclusion

There has been an increase in attacks by fraudsters on credit card transactions compared to the past. The further development of data science and machine learning has enabled the creation of numerous algorithms to identify fraudulent transactions. In this paper, an ensemble-based method for CCFD is described, which showed impressive results in identifying fraudulent transactions with a 98.67% success rate. The model effectively struck a balance between accuracy and recognition and thus minimized FP and FN, which is paramount to real-life uses where a false miss or false alarm might lead to a loss of money or customer dissatisfaction. Compared to traditional models like DT, LR, KNN, and NBs, the Ensemble demonstrated superior performance by capturing complex, non-linear fraud patterns, thereby proving its robustness and suitability for real-world detection. However, the research is limited by its reliance on a single dataset and synthetic oversampling with SMOTE, which may not fully reflect real-world scenarios. Future work will focus on testing with larger, more diverse datasets, exploring hybrid models such as CNN-LSTM for improved feature learning, and applying federated learning to enhance scalability, privacy, and adaptability.

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