AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review
Rapid advancements in digital innovation and globalization has significantly increased the complexity of financial networks, making them more vulnerable to fraud. Traditional fraud detection methods struggle to keep pace with evolving fraudulent strategies, contributing to an estimated global financ...
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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/11113282/ |
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| author | Nusrat Jahan Sarna Farzana Ahmed Rithen Umme Salma Jui Sayma Belal Al Amin Tasnim Kabir Oishee A. K. M. Muzahidul Islam |
| author_facet | Nusrat Jahan Sarna Farzana Ahmed Rithen Umme Salma Jui Sayma Belal Al Amin Tasnim Kabir Oishee A. K. M. Muzahidul Islam |
| author_sort | Nusrat Jahan Sarna |
| collection | DOAJ |
| description | Rapid advancements in digital innovation and globalization has significantly increased the complexity of financial networks, making them more vulnerable to fraud. Traditional fraud detection methods struggle to keep pace with evolving fraudulent strategies, contributing to an estimated global financial loss of <inline-formula> <tex-math notation="LaTeX">${\$}~5$ </tex-math></inline-formula> trillion. In response, this review paper explores the role of artificial intelligence (AI) in financial fraud detection, highlighting machine learning (ML), deep learning (DL), and hybrid models as transformative solutions. By analyzing vast datasets, AI can uncover hidden fraud patterns and dynamically adapt to emerging threats. Techniques such as supervised and unsupervised learning, along with advanced approaches like Graph Neural Networks (GNNs), have proven particularly effective in detecting various types of financial fraud, including payment fraud, identity theft, and money laundering. This paper presents a comprehensive taxonomy of AI-driven fraud detection methodologies, synthesizing insights from a substantial number of research papers. It systematically categorizes fraud detection techniques based on their application in different types of fraud, providing a structured framework to understand their effectiveness. In addition, it examines the role of cloud computing, edge AI, and distributed systems in enabling real-time transaction monitoring and fraud detection. Although AI significantly improves detection accuracy, reduces operational costs, and strengthens regulatory compliance, challenges such as model explainability, data privacy concerns, algorithmic bias, and the dynamic nature of fraud remain critical barriers to widespread adoption. Our review highlights the need for collaborative efforts among financial institutions, regulators, and technology providers to address these challenges. Future research should focus on improving the transparency of the AI model, integrating AI with blockchain for secure data sharing, and leveraging federated learning to enhance fraud detection capabilities. By addressing these challenges, AI can play a pivotal role in securing financial systems, minimizing fraud risks, and fostering cross-industry collaboration for more resilient fraud detection frameworks. |
| format | Article |
| id | doaj-art-af52ade4994b423dbd6293483b5ce759 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-af52ade4994b423dbd6293483b5ce7592025-08-20T04:03:22ZengIEEEIEEE Access2169-35362025-01-011314120414123310.1109/ACCESS.2025.359606011113282AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic ReviewNusrat Jahan Sarna0https://orcid.org/0009-0004-3926-3681Farzana Ahmed Rithen1https://orcid.org/0009-0009-7570-5367Umme Salma Jui2https://orcid.org/0009-0004-7138-0802Sayma Belal3https://orcid.org/0009-0006-1237-2604Al Amin4https://orcid.org/0009-0008-8665-3630Tasnim Kabir Oishee5https://orcid.org/0009-0005-5572-289XA. K. M. Muzahidul Islam6https://orcid.org/0000-0002-4492-6134Department of Computer Science and Engineering, United International University, Dhaka, BangladeshDepartment of Computer Science and Engineering, United International University, Dhaka, BangladeshDepartment of Computer Science and Engineering, United International University, Dhaka, BangladeshDepartment of Computer Science and Engineering, United International University, Dhaka, BangladeshDepartment of Computer Science and Engineering, United International University, Dhaka, BangladeshDepartment of Computer Science and Engineering, United International University, Dhaka, BangladeshDepartment of Computer Science and Engineering, United International University, Dhaka, BangladeshRapid advancements in digital innovation and globalization has significantly increased the complexity of financial networks, making them more vulnerable to fraud. Traditional fraud detection methods struggle to keep pace with evolving fraudulent strategies, contributing to an estimated global financial loss of <inline-formula> <tex-math notation="LaTeX">${\$}~5$ </tex-math></inline-formula> trillion. In response, this review paper explores the role of artificial intelligence (AI) in financial fraud detection, highlighting machine learning (ML), deep learning (DL), and hybrid models as transformative solutions. By analyzing vast datasets, AI can uncover hidden fraud patterns and dynamically adapt to emerging threats. Techniques such as supervised and unsupervised learning, along with advanced approaches like Graph Neural Networks (GNNs), have proven particularly effective in detecting various types of financial fraud, including payment fraud, identity theft, and money laundering. This paper presents a comprehensive taxonomy of AI-driven fraud detection methodologies, synthesizing insights from a substantial number of research papers. It systematically categorizes fraud detection techniques based on their application in different types of fraud, providing a structured framework to understand their effectiveness. In addition, it examines the role of cloud computing, edge AI, and distributed systems in enabling real-time transaction monitoring and fraud detection. Although AI significantly improves detection accuracy, reduces operational costs, and strengthens regulatory compliance, challenges such as model explainability, data privacy concerns, algorithmic bias, and the dynamic nature of fraud remain critical barriers to widespread adoption. Our review highlights the need for collaborative efforts among financial institutions, regulators, and technology providers to address these challenges. Future research should focus on improving the transparency of the AI model, integrating AI with blockchain for secure data sharing, and leveraging federated learning to enhance fraud detection capabilities. By addressing these challenges, AI can play a pivotal role in securing financial systems, minimizing fraud risks, and fostering cross-industry collaboration for more resilient fraud detection frameworks.https://ieeexplore.ieee.org/document/11113282/Fraud detectionAI modelsfinancial networksanomaly detectionfinancial fraud |
| spellingShingle | Nusrat Jahan Sarna Farzana Ahmed Rithen Umme Salma Jui Sayma Belal Al Amin Tasnim Kabir Oishee A. K. M. Muzahidul Islam AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review IEEE Access Fraud detection AI models financial networks anomaly detection financial fraud |
| title | AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review |
| title_full | AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review |
| title_fullStr | AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review |
| title_full_unstemmed | AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review |
| title_short | AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review |
| title_sort | ai driven fraud detection models in financial networks a comprehensive systematic review |
| topic | Fraud detection AI models financial networks anomaly detection financial fraud |
| url | https://ieeexplore.ieee.org/document/11113282/ |
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