FRAUD-X: An Integrated AI, Blockchain, and Cybersecurity Framework With Early Warning Systems for Mitigating Online Financial Fraud: A Case Study From North Macedonia
Online financial fraud remains a pervasive threat, incurring billions of dollars in global losses annually. Mid-sized markets, such as North Macedonia, face acute challenges as digital adoption in the Banking, Financial Services, and Insurance (BFSI) sector outpaces the establishment of robust, mult...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10908824/ |
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| author | Bekim Fetaji Majlinda Fetaji Affan Hasan Shpetim Rexhepi Goce Armenski |
| author_facet | Bekim Fetaji Majlinda Fetaji Affan Hasan Shpetim Rexhepi Goce Armenski |
| author_sort | Bekim Fetaji |
| collection | DOAJ |
| description | Online financial fraud remains a pervasive threat, incurring billions of dollars in global losses annually. Mid-sized markets, such as North Macedonia, face acute challenges as digital adoption in the Banking, Financial Services, and Insurance (BFSI) sector outpaces the establishment of robust, multi-layered security systems. This paper introduces FRAUD-X, a unified framework merging artificial intelligence (AI)–based anomaly detection, blockchain-driven transaction verification, cybersecurity intrusion detection, and real-time early warning mechanisms into a single pipeline. Drawing upon three datasets—a Credit Card Fraud dataset (Kaggle), the PaySim Mobile Money dataset, and collected 50,000 anonymized local BFSI transactions from North Macedonia—FRAUD-X demonstrates a ~2–4% improvement in F1 compared to single-plane AI approaches, with ~90% recall for zero-day threats. Key enhancements include: 1) a permissioned blockchain for tamper-proof ledger entries, 2) synergistic AI-cybersecurity integration for dynamic risk scoring, and 3) real-time alerts that reduce reaction windows from hours to mere minutes. The framework runs at ~15–16 ms per transaction (~33% CPU usage), supporting near-real-time BFSI operations. Ablation studies confirm that each synergy layer (blockchain, cybersecurity, and early warning) significantly contributes to overall performance. A security analysis illustrates how FRAUD-X mitigates node compromise, collusion attempts, and advanced persistent threats (APT). By providing a replicable roadmap that balances high detection accuracy with operational feasibility, FRAUD-X offers practical value to BFSI entities in North Macedonia and comparable mid-scale markets. |
| format | Article |
| id | doaj-art-d0125fd29b1541598aa536edcf55688d |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d0125fd29b1541598aa536edcf55688d2025-08-20T02:40:40ZengIEEEIEEE Access2169-35362025-01-0113480684808210.1109/ACCESS.2025.354728510908824FRAUD-X: An Integrated AI, Blockchain, and Cybersecurity Framework With Early Warning Systems for Mitigating Online Financial Fraud: A Case Study From North MacedoniaBekim Fetaji0https://orcid.org/0000-0001-9578-9443Majlinda Fetaji1Affan Hasan2Shpetim Rexhepi3https://orcid.org/0000-0001-6720-5009Goce Armenski4Department of Informatics, Mother Teresa University, Skopje, North MacedoniaDepartment of Computer Science, South East European University, Tetovo, North MacedoniaDepartment of Informatics, International Balkan University, Skopje, North MacedoniaDepartment of Informatics, Mother Teresa University, Skopje, North MacedoniaDepartment of Informatics, Saint Cyril and Methodius University, Skopje, North MacedoniaOnline financial fraud remains a pervasive threat, incurring billions of dollars in global losses annually. Mid-sized markets, such as North Macedonia, face acute challenges as digital adoption in the Banking, Financial Services, and Insurance (BFSI) sector outpaces the establishment of robust, multi-layered security systems. This paper introduces FRAUD-X, a unified framework merging artificial intelligence (AI)–based anomaly detection, blockchain-driven transaction verification, cybersecurity intrusion detection, and real-time early warning mechanisms into a single pipeline. Drawing upon three datasets—a Credit Card Fraud dataset (Kaggle), the PaySim Mobile Money dataset, and collected 50,000 anonymized local BFSI transactions from North Macedonia—FRAUD-X demonstrates a ~2–4% improvement in F1 compared to single-plane AI approaches, with ~90% recall for zero-day threats. Key enhancements include: 1) a permissioned blockchain for tamper-proof ledger entries, 2) synergistic AI-cybersecurity integration for dynamic risk scoring, and 3) real-time alerts that reduce reaction windows from hours to mere minutes. The framework runs at ~15–16 ms per transaction (~33% CPU usage), supporting near-real-time BFSI operations. Ablation studies confirm that each synergy layer (blockchain, cybersecurity, and early warning) significantly contributes to overall performance. A security analysis illustrates how FRAUD-X mitigates node compromise, collusion attempts, and advanced persistent threats (APT). By providing a replicable roadmap that balances high detection accuracy with operational feasibility, FRAUD-X offers practical value to BFSI entities in North Macedonia and comparable mid-scale markets.https://ieeexplore.ieee.org/document/10908824/Financial fraud detectionblockchain consensus mechanismsartificial intelligencecybersecurityearly warning systemssecurity analyses |
| spellingShingle | Bekim Fetaji Majlinda Fetaji Affan Hasan Shpetim Rexhepi Goce Armenski FRAUD-X: An Integrated AI, Blockchain, and Cybersecurity Framework With Early Warning Systems for Mitigating Online Financial Fraud: A Case Study From North Macedonia IEEE Access Financial fraud detection blockchain consensus mechanisms artificial intelligence cybersecurity early warning systems security analyses |
| title | FRAUD-X: An Integrated AI, Blockchain, and Cybersecurity Framework With Early Warning Systems for Mitigating Online Financial Fraud: A Case Study From North Macedonia |
| title_full | FRAUD-X: An Integrated AI, Blockchain, and Cybersecurity Framework With Early Warning Systems for Mitigating Online Financial Fraud: A Case Study From North Macedonia |
| title_fullStr | FRAUD-X: An Integrated AI, Blockchain, and Cybersecurity Framework With Early Warning Systems for Mitigating Online Financial Fraud: A Case Study From North Macedonia |
| title_full_unstemmed | FRAUD-X: An Integrated AI, Blockchain, and Cybersecurity Framework With Early Warning Systems for Mitigating Online Financial Fraud: A Case Study From North Macedonia |
| title_short | FRAUD-X: An Integrated AI, Blockchain, and Cybersecurity Framework With Early Warning Systems for Mitigating Online Financial Fraud: A Case Study From North Macedonia |
| title_sort | fraud x an integrated ai blockchain and cybersecurity framework with early warning systems for mitigating online financial fraud a case study from north macedonia |
| topic | Financial fraud detection blockchain consensus mechanisms artificial intelligence cybersecurity early warning systems security analyses |
| url | https://ieeexplore.ieee.org/document/10908824/ |
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