Financial fraud is still one of the most important threats to the financial industry, causing enormous economic losses and mounting difficulties for conventional fraud detection systems. The systems tend to face challenges in dealing with the rising amount of transactional data, the problem of class imbalance, and the continually changing nature of fraudulent activity. In order to solve these problems, in this research a cloud hybrid framework for detecting fraud using Long Short-Term Memory (LSTM) networks, Autoencoders, and Cat Swarm Optimization (CSO) is suggested. The purpose of the suggested framework is to provide improved detection performance and flexibility on a benchmark financial dataset, with a design intended to support scalability in real-time applications. The framework uses the Credit Card Fraud Detection Dataset from Kaggle, which consists primarily of numerical features, including anonymized variables (V1–V28), along with time and amount. The LSTM networks learn the sequential relationships of transactions, while Autoencoders learn to detect anomalies in the data unsupervised. CSO is used to optimize key hyperparameters of the hybrid model, including the learning rate (0.0001–0.01), batch size (32–128), number of LSTM layers (1–3), number of hidden units per layer (16–128), dropout rate (0.1–0.5), and fusion weights (0–1 for each weight, with the sum constrained to 1) between the LSTM and Autoencoder outputs. In addition, CSO is applied for feature subset selection and threshold tuning to further enhance model performance. Preprocessing is performed on the data, including normalization and feature scaling prior to model training. The suggested framework has a 96.2% accuracy, 94.6% precision, 97.9% recall, 96.2% F1-score, and 0.97 AUC-ROC, showing improved performance compared to CNN-based and LSTM-CNN models under the evaluated conditions. However, since no multiple experiments were conducted to verify the robustness, the results should be interpreted as indicative rather than definitive. The framework exhibits competitive fraud detection performance on the evaluated benchmark dataset, particularly in handling class imbalance. In a simulated environment configured to mimic cloud-like conditions, the framework achieved inference latency between 15 and 30 ms, GPU utilization between 60% and 70%, and a data transfer volume of approximately 1.5 GB per day, suggesting its potential for deployment in cloud-based fraud detection systems. The framework indicates immense potential for cloud deployment, with a robust solution for preventing financial fraud. The proposed framework demonstrates the potential of integrating sequential modeling, anomaly detection, and metaheuristic optimization within a unified and cloud-oriented architecture, providing a more comprehensive approach compared to conventional hybrid models.
Qu et al. (Fri,) studied this question.