Articles
| Open Access |
DOI: A Hybrid CNN-LSTM Approach for Detecting Anomalous Bank Transactions: Enhancing Financial Fraud Detection Accuracy
Tamanna Pervin , Department of Business Administration, International American University, Los Angeles, California, USA Sharmin Akter , Department of Information Technology Project Management, St. Francis College, USA Sadia Afrin , Department of Computer & Information Science, Gannon University, USA Md Refat Hossain , Master of Business Administration, Westcliff University, USA MD Sajedul Karim Chy , Department of Business Administration, Washington University of Science and Technology, USA Sadia Akter , Department of Business Administration, International American University, USA Md Minzamul Hasan , Doctor of Business Administration (DBA), College of Business, Westcliff University, USA Md Mafuzur Rahman , Master’s in data Analytics, Harrisburg University of Science & Technology, USA Chowdhury Amin Abdullah , Seidenberg School of CSIS, Pace University, USAAbstract
Detecting fraudulent bank transactions is crucial for maintaining the integrity of financial institutions and preserving customer trust. This study introduces a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model designed to enhance the accuracy and efficiency of fraud detection systems. Utilizing the European Credit Card Fraud Detection dataset comprising 284,807 transactions with significant class imbalance, extensive preprocessing techniques were applied, including Min-Max scaling and Synthetic Minority Over-sampling Technique (SMOTE). Recursive Feature Elimination (RFE) identified the top 20 impactful features, optimizing model performance. The proposed hybrid model demonstrated remarkable effectiveness, achieving superior accuracy (99.5%), precision (93.1%), recall (92.1%), F1-score (92.6%), and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 97.5%. Comparative analyses revealed that the hybrid CNN-LSTM model significantly outperformed traditional machine learning algorithms such as Logistic Regression, Random Forest, and XGBoost. These findings underscore the potential of CNN-LSTM hybrid models in addressing complex fraud detection scenarios, providing financial institutions with a robust and reliable tool for transaction anomaly detection.
Keywords
Fraud Detection, CNN-LSTM Hybrid Model, Credit Card Fraud, SMOTE, Machine Learning
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Copyright (c) 2025 Tamanna Pervin, Sharmin Akter, Sadia Afrin, Md Refat Hossain, MD Sajedul Karim Chy, Sadia Akter, Md Minzamul Hasan, Md Mafuzur Rahman, Chowdhury Amin Abdullah
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