Online Banking Fraud Detection Model: Decentralized Machine Learning Framework to Enhance Effectiveness and Compliance with Data Privacy Regulations
In such a dynamic and increasingly digitalized financial sector, many sophisticated fraudulent and cybercrime activities continue to challenge conventional detection systems. This research study explores a decentralized anomaly detection framework using deep autoencoders, designed to meet the dual i...
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MDPI AG
2025-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/13/2110 |
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| author | Hisham AbouGrad Lakshmi Sankuru |
| author_facet | Hisham AbouGrad Lakshmi Sankuru |
| author_sort | Hisham AbouGrad |
| collection | DOAJ |
| description | In such a dynamic and increasingly digitalized financial sector, many sophisticated fraudulent and cybercrime activities continue to challenge conventional detection systems. This research study explores a decentralized anomaly detection framework using deep autoencoders, designed to meet the dual imperatives of fraud detection effectiveness and user data privacy. Instead of relying on centralized aggregation or data sharing, the proposed model simulates distributed training across multiple financial nodes, with each institution processing data locally and independently. The framework is evaluated using two real-world datasets, the Credit Card Fraud dataset and the NeurIPS 2022 Bank Account Fraud dataset. The research methodology applied robust preprocessing, the implementation of a compact autoencoder architecture, and a threshold-based anomaly detection strategy. Evaluation metrics, such as confusion matrices, receiver operating characteristic (ROC) curves, precision–recall (PR) curves, and reconstruction error distributions, are used to assess the model’s performance. Also, a threshold sensitivity analysis has been applied to explore detection trade-offs at varying levels of strictness. Although the model’s recall remains modest due to class imbalance, it demonstrates strong precision at higher thresholds, which demonstrates its utility in minimizing false positives. Overall, this research study is a practical and privacy-conscious approach to fraud detection that aligns with the operational realities of financial institutions and regulatory compliance toward scalability, privacy preservation, and interpretable fraud detection solutions suitable for real-world financial environments. |
| format | Article |
| id | doaj-art-b1a972c62f19441eb3f26b857b9ae4fe |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-b1a972c62f19441eb3f26b857b9ae4fe2025-08-20T03:50:16ZengMDPI AGMathematics2227-73902025-06-011313211010.3390/math13132110Online Banking Fraud Detection Model: Decentralized Machine Learning Framework to Enhance Effectiveness and Compliance with Data Privacy RegulationsHisham AbouGrad0Lakshmi Sankuru1Department of Computer Science and Digital Technologies, School of Architecture, Computing and Engineering, University of East London, University Way, Docklands, London E16 2RD, UKDepartment of Computer Science and Digital Technologies, School of Architecture, Computing and Engineering, University of East London, University Way, Docklands, London E16 2RD, UKIn such a dynamic and increasingly digitalized financial sector, many sophisticated fraudulent and cybercrime activities continue to challenge conventional detection systems. This research study explores a decentralized anomaly detection framework using deep autoencoders, designed to meet the dual imperatives of fraud detection effectiveness and user data privacy. Instead of relying on centralized aggregation or data sharing, the proposed model simulates distributed training across multiple financial nodes, with each institution processing data locally and independently. The framework is evaluated using two real-world datasets, the Credit Card Fraud dataset and the NeurIPS 2022 Bank Account Fraud dataset. The research methodology applied robust preprocessing, the implementation of a compact autoencoder architecture, and a threshold-based anomaly detection strategy. Evaluation metrics, such as confusion matrices, receiver operating characteristic (ROC) curves, precision–recall (PR) curves, and reconstruction error distributions, are used to assess the model’s performance. Also, a threshold sensitivity analysis has been applied to explore detection trade-offs at varying levels of strictness. Although the model’s recall remains modest due to class imbalance, it demonstrates strong precision at higher thresholds, which demonstrates its utility in minimizing false positives. Overall, this research study is a practical and privacy-conscious approach to fraud detection that aligns with the operational realities of financial institutions and regulatory compliance toward scalability, privacy preservation, and interpretable fraud detection solutions suitable for real-world financial environments.https://www.mdpi.com/2227-7390/13/13/2110fraud detectionfinancial cybercrimedeep autoencoderanomaly detectionfinancial securitymachine learning |
| spellingShingle | Hisham AbouGrad Lakshmi Sankuru Online Banking Fraud Detection Model: Decentralized Machine Learning Framework to Enhance Effectiveness and Compliance with Data Privacy Regulations Mathematics fraud detection financial cybercrime deep autoencoder anomaly detection financial security machine learning |
| title | Online Banking Fraud Detection Model: Decentralized Machine Learning Framework to Enhance Effectiveness and Compliance with Data Privacy Regulations |
| title_full | Online Banking Fraud Detection Model: Decentralized Machine Learning Framework to Enhance Effectiveness and Compliance with Data Privacy Regulations |
| title_fullStr | Online Banking Fraud Detection Model: Decentralized Machine Learning Framework to Enhance Effectiveness and Compliance with Data Privacy Regulations |
| title_full_unstemmed | Online Banking Fraud Detection Model: Decentralized Machine Learning Framework to Enhance Effectiveness and Compliance with Data Privacy Regulations |
| title_short | Online Banking Fraud Detection Model: Decentralized Machine Learning Framework to Enhance Effectiveness and Compliance with Data Privacy Regulations |
| title_sort | online banking fraud detection model decentralized machine learning framework to enhance effectiveness and compliance with data privacy regulations |
| topic | fraud detection financial cybercrime deep autoencoder anomaly detection financial security machine learning |
| url | https://www.mdpi.com/2227-7390/13/13/2110 |
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