Implementation of Artificial Intelligence in Fraud Detection and Prevention Through a Systematic Literature Review and Its Implications for the Financial Sector
DOI:
https://doi.org/10.61194/ijjm.v7i1.1919Keywords:
Artificial Intelligence, Fraud Detection, Machine Learning, Financial SecurityAbstract
The increasing complexity of financial fraud in the Digital Era requires more advanced and adaptive detection methods. This study examines the implementation of Artificial Intelligence (AI) in fraud detection and prevention through a Systematic Literature Review (SLR), addressing a critical issue in financial technology that remains highly relevant to both academic and professional communities. Although AI-based fraud detection has been widely studied, this research provides a distinct contribution by integrating technical effectiveness with regulatory alignment. The SLR systematically analyzes studies from major academic databases such as Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar to identify key trends, challenges, and implications for the financial sector. The PRISMA framework is used to screen and evaluate relevant literature, ensuring a comprehensive and structured analysis. VOSviewer is applied to visualize key research trends and topic relationships in AI-based fraud detection. The findings indicate that machine learning and deep learning techniques significantly enhance fraud detection accuracy, surpassing traditional rule-based approaches. Natural Language Processing (NLP) has shown effectiveness in analyzing fraud-related documents, while big data analytics facilitates real-time fraud monitoring. However, challenges persist, including data imbalance, regulatory compliance, and data privacy concerns, which must be addressed for successful AI implementation. This study concludes that an integrated AI framework that combines technological advancements with strong regulatory alignment is crucial for effective fraud detection. Future research should explore empirical case studies and real-world applications to validate these theoretical findings.
Downloads
References
Adijat Bello, O., Ogundipe, A., Mohammed, D., Folorunso, A., & Ayodeji Alonge, O. (2023). AI-Driven Approaches for Real-Time Fraud Detection in US Financial Transactions: Challenges and Opportunities. European Journal of Computer Science and Information Technology, 11(6), 84–102.
Alghafiqi, B., & Munajat, E. (2022). Impact of Artificial Intelligence Technology on Accounting Profession. Berkala Akuntansi Dan Keuangan Indonesia, 7(2), 140–159. https://doi.org/10.20473/baki.v7i2.27934
Ali, A., Abd Razak, S., Othman, S. H., Eisa, T. A. E., Al-Dhaqm, A., Nasser, M., Elhassan, T., Elshafie, H., & Saif, A. (2022). Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review. Applied Sciences (Switzerland), 12(19). https://doi.org/10.3390/app12199637
Angela, O., Atoyebi, I., Soyele, A., & Ogunwobi, E. (2024). Enhancing fraud detection and prevention in fintech : Big data and machine learning approaches Enhancing fraud detection and prevention in fintech : Big data and machine learning approaches. ResearchGate, November. https://doi.org/10.30574/wjarr.2024.24.2.3617
Avacharmal, R. (2021). Leveraging Supervised Machine Learning Algorithms for Enhanced Anomaly Detection in Anti-Money Laundering ( AML ) Transaction Monitoring Systems : A Comparative Analysis of Performance and Explainability. African Journal Of Artificial Intelligence and Sustainable Development, 1(2), 68–85.
Bakumenko, A., & Elragal, A. (2022). Detecting Anomalies in Financial Data Using Machine Learning Algorithms. Systems, 10(5). https://doi.org/10.3390/systems10050130
Bello, O. A., Folorunso, A., & Onwuchekwa, J. (2023). A Comprehensive Framework for Strengthening USA Financial Cybersecurity: Integrating Machine Learning and AI in Fraud Detection Systems. European Journal of Computer Science and Information Technology, 11(6), 62–83.
Biswas, N., & Chakrabarti, S. (2020). Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer. Frontiers in Oncology, 10(October), 1–13. https://doi.org/10.3389/fonc.2020.588221
Carcillo, F., Le Borgne, Y. A., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2021). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences, 557(June 2020), 317–331. https://doi.org/10.1016/j.ins.2019.05.042
Caseba, F. L. (2024). Penerapan AI, Big Data, dan Blockchain dalam Fintech Payment Terhadap Risiko Penipuan Komputer (Computer Fraud Risk) : A Systematic Literature Review. Diponegoro Journal Of Accounting, 2(2), 1–15.
Cheng, L., Varshney, K. R., & Liu, H. (2021). Socially responsible AI algorithms: Issues, purposes, and challenges. Journal of Artificial Intelligence Research, 71, 1137–1181. https://doi.org/10.1613/JAIR.1.12814
Creswell, J. (2016). Research design Research design. Research in Social Science: Interdisciplinary Perspectives, September, 68–84.
Creswell, J. (2017). Qualitative Inqury Research Design Choosing Among Five Approaches.
Dawam, A. (2024). PERAN ARTIFICIAL INTELLIGENCE DALAM MENGURANGI PERILAKU KORUPTIF ( Perspektif Pendidikan Islam ). SYAIKHONA : Jurnal Magister Pendidikan Agama Islam, 02(02), 40–72. https://doi.org/10.59166/syaikhona.v2i2.231
Dwivedi, R. (2023). Explainable AI (XAI): Core Ideas, Techniques and Solutions RUDRESH. ACM Comput. Surv., 2(2).
Eid, A. M., Soudan, B., Nasif, A. B., & Injadat, M. N. (2024). Comparative study of ML models for IIoT intrusion detection: impact of data preprocessing and balancing. Neural Computing and Applications, 36(13), 6955–6972. https://doi.org/10.1007/s00521-024-09439-x
Esenogho, E., Mienye, I. D., Swart, T. G., Aruleba, K., & Obaido, G. (2022). A Neural Network Ensemble with Feature Engineering for Improved Credit Card Fraud Detection. IEEE Access, 10, 16400–16407. https://doi.org/10.1109/ACCESS.2022.3148298
Ethan, A. (2024). AI-Driven Anomaly Detection in NoSQL Databases for Enhanced Security AI-Driven Anomaly Detection in NoSQL Databases for Enhanced Security Hemanth Gadde. ResearchGate, 14(01).
Fares, O. H., Butt, I., & Lee, S. H. M. (2023). Utilization of artificial intelligence in the banking sector: a systematic literature review. Journal of Financial Services Marketing, 28(4), 835–852. https://doi.org/10.1057/s41264-022-00176-7
Fernando, Z. J. (2024). AI Hakim : Merevolusi Peradilan yang Berintegritas, Bermartabat, dan Meningkatkan Kesejahteraan Hakim. Jurnal Hukum Dan Peradilan PP., 2(2), 141–166.
Gianini, G., Ghemmogne Fossi, L., Mio, C., Caelen, O., Brunie, L., & Damiani, E. (2020). Managing a pool of rules for credit card fraud detection by a Game Theory based approach. Future Generation Computer Systems, 102(2), 549–561. https://doi.org/10.1016/j.future.2019.08.028
Gil, Y., Garijo, D., Khider, D., Knoblock, C. A., Ratnakar, V., Osorio, M., Vargas, H., Pham, M., Pujara, J., Shbita, B., Vu, B., Chiang, Y. Y., Feldman, D., Lin, Y., Song, H., Kumar, V., Khandelwal, A., Steinbach, M., Tayal, K., … Shu, L. (2021). Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making. ACM Transactions on Interactive Intelligent Systems, 11(2). https://doi.org/10.1145/3453172
Gupta, P. (2023). Leveraging Machine Learning and Artificial Intelligence for Fraud Prevention. International Journal of Computer Science and Engineering, 10(5), 47–52. https://doi.org/10.14445/23488387/ijcse-v10i5p107
Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Systematic Reviews, 18(2), e1230. https://doi.org/https://doi.org/10.1002/cl2.1230
Hashemi, S. K., Mirtaheri, S. L., & Greco, S. (2023). Fraud Detection in Banking Data by Machine Learning Techniques. IEEE Access, 11(January), 3034–3043. https://doi.org/10.1109/ACCESS.2022.3232287
Hassan, M., Aziz, L. A.-R., & Andriansyah, Y. (2023). The Role Artificial Intelligence in Modern Banking: An Exploration of AI-Driven Approaches for Enhanced Fraud Prevention, Risk Management, and Regulatory Compliance. Reviews of Contemporary Business Analytics, 6(1), 110–132.
Hayati, M. S. U., & Hadiprajitno, P. B. (2021). Departemen Akuntansi Fakultas Ekonomika dan Bisnis Universitas Diponegoro. Diponegoro Journal of …, 1203011612(2022), 19840503.
Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances. Expert Systems with Applications, 193, 116429. https://doi.org/10.1016/j.eswa.2021.116429
Hutagalung, E. R. A. (2024). Potensi, Tantangan, dan Implementasi Blockchain untuk Pengembangan Aplikasi dalam Era Digital Modern. Jurnal Multidisiplin Saintek, 5(3), 1–23.
Inampudi, R. K., Researcher, I., Pichaimani, T., Solutions, C. T., Surampudi, Y., & Finance, B. (2020). AI-Enhanced Fraud Detection in Real-Time Payment Systems : Leveraging Machine Learning and Anomaly Detection to Secure Digital Transactions. Australian Journal of Machine Learning Research & Applications, 2(1), 483–523.
Islam, M. T., Hasan, M. M., Redwanuzzaman, M., & Hossain, M. K. (2024). Practices of artificial intelligence to improve the business in Bangladesh. Social Sciences and Humanities Open, 9(December 2023), 100766. https://doi.org/10.1016/j.ssaho.2023.100766
Kamuangu, P. K. (2024). Journal of Economics, Finance and Accounting Studies A Review on Financial Fraud Detection using AI and Machine Learning. Journal of Economics, Finance and Accounting Studies , 6(1), 67–77. https://doi.org/10.32996/jefas
Kshetri, N. (2021). The Role of Artificial Intelligence in Promoting Financial Inclusion in Developing Countries. Journal of Global Information Technology Management, 24(1), 1–6. https://doi.org/10.1080/1097198X.2021.1871273
Kute, D. V., Pradhan, B., Shukla, N., & Alamri, A. (2021). Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering-A Critical Review. IEEE Access, 9, 82300–82317. https://doi.org/10.1109/ACCESS.2021.3086230
Kyrychenko, O. V., Soldatenko, O. A., Gorokhovska, O. V., Voloshyna, M. O., & Maksymova, L. O. (2021). Fraud in the banking system of Ukraine: ways to combat taking into account foreign experience. Revista Amazonia Investiga, 10(45), 208–220. https://doi.org/10.34069/ai/2021.45.09.21
Lin, T. H., & Jiang, J. R. (2021). Credit card fraud detection with autoencoder and probabilistic random forest. Mathematics, 9(21), 4–15. https://doi.org/10.3390/math9212683
Lwin Tun, Z., & Birks, D. (2023). Supporting crime script analyses of scams with natural language processing. Crime Science, 12(1), 1–22. https://doi.org/10.1186/s40163-022-00177-w
Mahya, L., Tarjo, T., Sanusi, Z. M., & Kurniawan, F. A. (2023). Intelligent Automation Of Fraud Detection And Investigation:A Bibliometric Analysis Approach. Jurnal Reviu Akuntansi Dan Keuangan, 13(3), 588–613. https://doi.org/10.22219/jrak.v13i3.28487
Mawlidy, E. R., Dio, R., & Lorensa, L. (2024). Kemampuan Artifical Intelligence Terhadap Pendeteksian Fraud: Studi Literatur. Akurasi : Jurnal Studi Akuntansi Dan Keuangan, 7(1), 89–104. https://doi.org/10.29303/akurasi.v7i1.488
Medhi, D., Singh, P., Goswami, H., & Singh, J. (2024). Futuristic Approach Of Forensic Fraud Investigation In Money Embezzlement. Asset Misappropriation And Larceny, 2024(6), 1283–1303. https://doi.org/10.53555/kuey.v30i6.5489
Mhlanga, D. (2020). Industry 4.0 in finance: the impact of artificial intelligence (ai) on digital financial inclusion. International Journal of Financial Studies, 8(3), 1–14. https://doi.org/10.3390/ijfs8030045
Mubarok, Sari, Wibowo, M. (2025). Comparative Study of Artificial Intelligence (AI) Utilization in Digital Marketing Strategies Between Developed and Developing Countries: A Systematic Literature Review. Ilomata International Journal of Management, 6(1), 156–173. https://doi.org/10.61194/ijjm.v6i1.1534
Nahar, J., & Mintoo, A. A. (2024). Fraud Detection In Banking Leveraging Ai To Identify And Prevent Fraudulent Activities In Real-Time. Journal of Machine Learning, 01(01). https://doi.org/10.70008/jmldeds.v1i01.53
Nuraziza, S., Febri, W., & Sudirman, R. (2024). Studi Literatur: Intergrasi Artificial Intelegence (AI) dalam Manajemen Keuangan (Tantangan dan Kepatuhan Regulasi) 1. Hompage: Journal.Universitaspahlawan.Ac.Id/Index.Php/MONEY MONEY, 2(1), 47.
Ofoegbu, C., Njoku, D. O., Iwuchukwu, V. C., Jibiri, J. E., Ikwuazom, C. T., Ofoegbu, C. I., & Nwokoma, F. O. (2024). Machine Learning Approach for Fraud Detection System in Financial Institution: A Web Base Application. International Journal Of Engineering Research And Development, 20(4), 1–12.
Olateju, O. O., Okon, S. U., Igwenagu, U. T. I., Salami, A. A., Oladoyinbo, T. O., & Olaniyi, O. O. (2024). Combating the Challenges of False Positives in AI-Driven Anomaly Detection Systems and Enhancing Data Security in the Cloud. Asian Journal of Research in Computer Science, 17(6), 264–292. https://doi.org/10.9734/ajrcos/2024/v17i6472
Oluwabusayo Adijat Bello, & Komolafe Olufemi. (2024). Artificial intelligence in fraud prevention: Exploring techniques and applications challenges and opportunities. Computer Science & IT Research Journal, 5(6), 1505–1520. https://doi.org/10.51594/csitrj.v5i6.1252
Paraswansa, A. D., & Utomo, D. C. (2024). Whistleblowing dan Korupsi Pada Sektor Publik: A Systematic Review. Jurnal Akademi Akuntansi, 7(1), 94–113. https://doi.org/10.22219/jaa.v7i1.31336
Rane, N., Paramesha, M., Choudhary, S., & Rane, J. (2024). Artificial Intelligence, Machine Learning, and Deep Learning for Advanced Business Strategies: a Review. SSRN Electronic Journal, June, 10–11. https://doi.org/10.2139/ssrn.4835661
Schumann, G., & Gómez, J. M. (2021). Natural language processing in internal auditing - A structured literature review. 27th Annual Americas Conference on Information Systems, AMCIS 2021, 02(01).
Ślusarek, N. (2022). The Fraudulent Phenomenon of The Financial Pyramids in The Financcial Industry. Journal of Finance and Financial Law, 2(2), 87–107.
Strelcenia, E., & Prakoonwit, S. (2023). A Survey on GAN Techniques for Data Augmentation to Address the Imbalanced Data Issues in Credit Card Fraud Detection. Machine Learning and Knowledge Extraction, 5(1), 304–329. https://doi.org/10.3390/make5010019
Sunday Tubokirifuruar Tula, Onyeka Chrisanctus Ofodile, Chinwe Chinzo Okoye, Adeola Olushola Ajayi Nifise, & Olubusola Odeyemi. (2024). Entrepreneurial Ecosystems in the Usa: a Comparative Review With European Models. International Journal of Management & Entrepreneurship Research, 6(2), 451–466. https://doi.org/10.51594/ijmer.v6i2.799
Truby, J., Brown, R., & Dahdal, A. (2020). Banking on AI: mandating a proactive approach to AI regulation in the financial sector. Law and Financial Markets Review, 14(2), 110–120. https://doi.org/10.1080/17521440.2020.1760454
van der Aalst, W. M. P. (2021). Hybrid intelligence: to automate or not to automate, that is the question. International Journal of Information Systems and Project Management, 9(2), 5–20. https://doi.org/10.12821/ijispm090201
Yang, T., Zheng, X., Xiao, H., Shan, C., Yao, X., Li, Y., & Zhang, J. (2023). Drying Temperature Precision Control System Based on Improved Neural Network PID Controller and Variable-Temperature Drying Experiment of Cantaloupe Slices. Plants, 12(12), 1–20. https://doi.org/10.3390/plants12122257
Yesba, F., Parindingan, R., Fathirah, D., & Syamsuddin, A. (2024). Economics and Digital Business Review Audit Berbasis Data : Strategi Efektif untuk Mengidentifikasi Fraud di Era Digital. Economics and Digital Business Review, 5(2).
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
