Enhancing Fraud Detection in Banking With Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud Prevention
Under the umbrella of artificial intelligence (AI), deep learning enables systems to cluster data and provide incredibly accurate results. This study explores deep learning for fraud detection, utilizing Graph Neural Networks (GNNs) and Autoencoders to enhance business practices and reduce fraudulen...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10689393/ |
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| author | Fawaz Khaled Alarfaj Shabnam Shahzadi |
| author_facet | Fawaz Khaled Alarfaj Shabnam Shahzadi |
| author_sort | Fawaz Khaled Alarfaj |
| collection | DOAJ |
| description | Under the umbrella of artificial intelligence (AI), deep learning enables systems to cluster data and provide incredibly accurate results. This study explores deep learning for fraud detection, utilizing Graph Neural Networks (GNNs) and Autoencoders to enhance business practices and reduce fraudulent activities in large organizations. For real-time fraud detection, we propose Graph neural network with lambda architecture while for credit card fraud detection, we use an autoencoder, validated through case studies from two banks. The findings demonstrate that these methods effectively detect fraud with balance of precision and recall, improving the efficiency of banking systems. Python is employed for analysis, emphasizing the ability of deep learning to manage and prevent fraud in real-time on dynamic datasets. In the end, this study concludes that by using deep learning algorithms, we can control online credit card fraud detection in banks, improve the efficiency of the banking system. We can manage fraudulent activity in real-time and on dynamic datasets by utilizing deep learning algorithms, which allows for ongoing improvement of the fraud detection and prevention system. |
| format | Article |
| id | doaj-art-38dea8a8d63542889bf6896a9df60482 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-38dea8a8d63542889bf6896a9df604822025-02-05T00:00:47ZengIEEEIEEE Access2169-35362025-01-0113206332064610.1109/ACCESS.2024.346628810689393Enhancing Fraud Detection in Banking With Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud PreventionFawaz Khaled Alarfaj0https://orcid.org/0000-0002-6598-6240Shabnam Shahzadi1https://orcid.org/0000-0002-3763-3755Department of Management Information Systems (MIS), School of Business, King Faisal University (KFU), Al-Ahsa, Saudi ArabiaDepartment of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, ChinaUnder the umbrella of artificial intelligence (AI), deep learning enables systems to cluster data and provide incredibly accurate results. This study explores deep learning for fraud detection, utilizing Graph Neural Networks (GNNs) and Autoencoders to enhance business practices and reduce fraudulent activities in large organizations. For real-time fraud detection, we propose Graph neural network with lambda architecture while for credit card fraud detection, we use an autoencoder, validated through case studies from two banks. The findings demonstrate that these methods effectively detect fraud with balance of precision and recall, improving the efficiency of banking systems. Python is employed for analysis, emphasizing the ability of deep learning to manage and prevent fraud in real-time on dynamic datasets. In the end, this study concludes that by using deep learning algorithms, we can control online credit card fraud detection in banks, improve the efficiency of the banking system. We can manage fraudulent activity in real-time and on dynamic datasets by utilizing deep learning algorithms, which allows for ongoing improvement of the fraud detection and prevention system.https://ieeexplore.ieee.org/document/10689393/Deep learningcredit cardfraud detectiongraph neural networkautoencoders |
| spellingShingle | Fawaz Khaled Alarfaj Shabnam Shahzadi Enhancing Fraud Detection in Banking With Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud Prevention IEEE Access Deep learning credit card fraud detection graph neural network autoencoders |
| title | Enhancing Fraud Detection in Banking With Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud Prevention |
| title_full | Enhancing Fraud Detection in Banking With Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud Prevention |
| title_fullStr | Enhancing Fraud Detection in Banking With Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud Prevention |
| title_full_unstemmed | Enhancing Fraud Detection in Banking With Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud Prevention |
| title_short | Enhancing Fraud Detection in Banking With Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud Prevention |
| title_sort | enhancing fraud detection in banking with deep learning graph neural networks and autoencoders for real time credit card fraud prevention |
| topic | Deep learning credit card fraud detection graph neural network autoencoders |
| url | https://ieeexplore.ieee.org/document/10689393/ |
| work_keys_str_mv | AT fawazkhaledalarfaj enhancingfrauddetectioninbankingwithdeeplearninggraphneuralnetworksandautoencodersforrealtimecreditcardfraudprevention AT shabnamshahzadi enhancingfrauddetectioninbankingwithdeeplearninggraphneuralnetworksandautoencodersforrealtimecreditcardfraudprevention |