TRANSFORMING BANKING SECURITY: THE ROLE OF DEEP LEARNING IN FRAUD DETECTION SYSTEMS
Abstract
In the digital banking landscape, the increasing volume of online transactions has heightened the risk of fraudulent activities, necessitating the development of more effective detection systems. This study investigates the efficacy of various machine learning and deep learning algorithms in identifying fraudulent transactions, emphasizing Long Short-Term Memory (LSTM) networks. We implemented and evaluated multiple algorithms, including Logistic Regression, Random Forest, Gradient Boosting Machines (GBM), and XGBoost, on a large-scale credit card transaction dataset. Our results demonstrate that the LSTM model outperforms traditional machine learning algorithms, achieving an accuracy of 98.5%, precision of 87.2%, recall of 85.0%, and an Area Under the Curve (AUC) score of 0.94. These findings highlight the superior capability of LSTM networks to capture complex patterns in sequential transaction data, making them an asset for real-time fraud detection in banking. This research underscores the need for financial institutions to adopt advanced deep learning techniques to enhance their fraud detection systems, thereby minimizing financial losses and improving customer trust.
ZENODO DOI :- https://doi.org/10.5281/zenodo.14044576
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- Akhtar, P., Salim, A., & Ahmad, M. (2022). A comprehensive review of sentiment analysis: Techniques, tools, and applications. Journal of Business Research, 123, 344-355.
- Chowdhury, M. S., Shak, M. S., Devi, S., Miah, M. R., Al Mamun, A., Ahmed, E., ... & Mozumder, M. S. A. (2024). Optimizing E-Commerce Pricing Strategies: A Comparative Analysis of Machine Learning Models for Predicting Customer Satisfaction. The American Journal of Engineering and Technology, 6(09), 6-17.
- Md Abu Sayed, Badruddowza, Md Shohail Uddin Sarker, Abdullah Al Mamun, Norun Nabi, Fuad Mahmud, Md Khorshed Alam, Md Tarek Hasan, Md Rashed Buiya, & Mashaeikh Zaman Md. Eftakhar Choudhury. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR PREDICTING CYBERSECURITY ATTACK SUCCESS: A PERFORMANCE EVALUATION. The American Journal of Engineering and Technology, 6(09), 81–91. https://doi.org/10.37547/tajet/Volume06Issue09-10
- Md Al-Imran, Salma Akter, Md Abu Sufian Mozumder, Rowsan Jahan Bhuiyan, Tauhedur Rahman, Md Jamil Ahmmed, Md Nazmul Hossain Mir, Md Amit Hasan, Ashim Chandra Das, & Md. Emran Hossen. (2024). EVALUATING MACHINE LEARNING ALGORITHMS FOR BREAST CANCER DETECTION: A STUDY ON ACCURACY AND PREDICTIVE PERFORMANCE. The American Journal of Engineering and Technology, 6(09), 22–33. https://doi.org/10.37547/tajet/Volume06Issue09-04
- Md Murshid Reja Sweet, Md Parvez Ahmed, Md Abu Sufian Mozumder, Md Arif, Md Salim Chowdhury, Rowsan Jahan Bhuiyan, Tauhedur Rahman, Md Jamil Ahmmed, Estak Ahmed, & Md Atikul Islam Mamun. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR ACCURATE LUNG CANCER PREDICTION. The American Journal of Engineering and Technology, 6(09), 92–103. https://doi.org/10.37547/tajet/Volume06Issue09-11
- Bahl, S., Kumar, P., & Agarwal, A. (2021). Sentiment analysis in banking services: A review of techniques and challenges. International Journal of Information Management, 57, 102317.
- Ashim Chandra Das, Md Shahin Alam Mozumder, Md Amit Hasan, Maniruzzaman Bhuiyan, Md Rasibul Islam, Md Nur Hossain, Salma Akter, & Md Imdadul Alam. (2024). MACHINE LEARNING APPROACHES FOR DEMAND FORECASTING: THE IMPACT OF CUSTOMER SATISFACTION ON PREDICTION ACCURACY. The American Journal of Engineering and Technology, 6(10), 42–53. https://doi.org/10.37547/tajet/Volume06Issue10-06
- Rowsan Jahan Bhuiyan, Salma Akter, Aftab Uddin, Md Shujan Shak, Md Rasibul Islam, S M Shadul Islam Rishad, Farzana Sultana, & Md. Hasan-Or-Rashid. (2024). SENTIMENT ANALYSIS OF CUSTOMER FEEDBACK IN THE BANKING SECTOR: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS. The American Journal of Engineering and Technology, 6(10), 54–66. https://doi.org/10.37547/tajet/Volume06Issue10-07
- Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017). Credit card fraud detection using machine learning techniques: A comparative analysis. Journal of Applied Security Research, 12(4), 1–14. https://doi.org/10.1080/19361610.2017.1315696
- Bhowmik, D. (2019). Detecting financial fraud using machine learning techniques. International Journal of Data Science, 6(2), 102-121. https://doi.org/10.1080/25775327.2019.1123126
- Md Habibur Rahman, Ashim Chandra Das, Md Shujan Shak, Md Kafil Uddin, Md Imdadul Alam, Nafis Anjum, Md Nad Vi Al Bony, & Murshida Alam. (2024). TRANSFORMING CUSTOMER RETENTION IN FINTECH INDUSTRY THROUGH PREDICTIVE ANALYTICS AND MACHINE LEARNING. The American Journal of Engineering and Technology, 6(10), 150–163. https://doi.org/10.37547/tajet/Volume06Issue10-17
- Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2015). Credit card fraud detection: A realistic modeling and a novel
- DYNAMIC PRICING IN FINANCIAL TECHNOLOGY: EVALUATING MACHINE LEARNING SOLUTIONS FOR MARKET ADAPTABILITY. (2024). International Interdisciplinary Business Economics Advancement Journal, 5(10), 13-27. https://doi.org/10.55640/business/volume05issue10-03