International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Artificial Intelligence for Anti-Money Laundering: A Comprehensive Literature Review of Network and Behavioural Analysis Approaches |
|---|---|
| ABSTRACT | Money laundering remains a critical challenge threatening financial systems worldwide, compounded by increasingly sophisticated criminal tactics and limitations of traditional rule-based AML systems. This paper presents a comprehensive review of recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) approaches tailored to anti-money laundering (AML), with a specific emphasis on network and behavioural analytics. Employing a systematic PRISMA-guided methodology, it synthesizes current literature focusing on supervised, unsupervised, and deep learning models, including graph neural networks and explainable AI techniques that enhance detection accuracy and operational efficiency. Key challenges such as false positives, data imbalance, privacy constraints, and regulatory compliance are discussed. The review highlights emerging trends including real-time adaptive monitoring, synthetic data generation, and human-in-the-loop frameworks to address evolving laundering schemes. Future research directions emphasize multimodal data integration, privacy-preserving AI, and standardized evaluation to foster transparent, ethical, and effective AML systems. This review provides valuable insights for researchers, practitioners, and regulators aiming to advance AI-enabled AML solutions in increasingly complex financial landscapes. |
| AUTHOR | RAHUL JOSHI, DR. MANISHA PRAKASH BHARATI M.Sc. Student, Dept. of Technology, Savitribai Phule Pune University, Pune, Maharashtra, India Associate Professor, Dept. of Technology, Savitribai Phule Pune University, Pune, Maharashtra, India |
| VOLUME | 181 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1402009 |
| pdf/9_Artificial Intelligence for Anti-Money Laundering A Comprehensive Literature Review of Network and Behavioural Analysis Approaches.pdf | |
| KEYWORDS | |
| References | [1] M. Weber et al., ‘Scalable Graph Learning for Anti-Money Laundering: A First Look’, 2018, arXiv. doi: 10.48550/ARXIV.1812.00076. [2] R. C. Hellvig and C. A. Blanaru, ‘Impact of globalization on money laundering’, SEA Practical Application of Science, vol. 91–95, no. 32, p. 5, Apr. 2023. [3] I. Vorobyev and A. Krivitskaya, ‘Reducing false positives in bank anti-fraud systems based on rule induction in distributed tree-based models’, Computers & Security, vol. 120, p. 102786, Sept. 2022, doi: 10.1016/j.cose.2022.102786. [4] S. Mousavian and S. J. Miah, ‘Review of artificial intelligence-based applications for money laundering detection’, Intelligent Systems with Applications, vol. 27, p. 200572, Sept. 2025, doi: 10.1016/j.iswa.2025.200572. [5] Preeta Pillai, ‘AI-powered financial anomaly detection: Intelligent systems identifying irregularities in enterprise financial data flows’, World J. Adv. Res. Rev., vol. 26, no. 1, pp. 3406–3414, Apr. 2025, doi: 10.30574/wjarr.2025.26.1.1461. [6] Chinekwu Somtochukwu Odionu, Bernadette Bristol-Alagbariya, and Richard Okon, ‘Behavioral analytics in digital payments: A conceptual analysis of anti-money laundering techniques’, Int. J. Scholarly Res. Multidiscip. Studies, vol. 5, no. 2, pp. 052–072, Dec. 2024, doi: 10.56781/ijsrms.2024.5.2.0047. [7] J. Fan et al., ‘Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions’, 2025, arXiv. doi: 10.48550/ARXIV.2503.10058. [8] K. Coussement, M. Z. Abedin, M. Kraus, S. Maldonado, and K. Topuz, ‘Explainable AI for enhanced decision-making’, Decision Support Systems, vol. 184, p. 114276, Sept. 2024, doi: 10.1016/j.dss.2024.114276. [9] G. Konstantinidis and A. Gegov, ‘Deep Neural Networks for Anti Money Laundering Using Explainable Artificial Intelligence’, in 2024 IEEE 12th International Conference on Intelligent Systems (IS), Varna, Bulgaria: IEEE, Aug. 2024, pp. 1–6. doi: 10.1109/IS61756.2024.10705194. [10] G. Yang, X. Liu, and B. Li, ‘Anti-money laundering supervision by intelligent algorithm’, Computers & Security, vol. 132, p. 103344, Sept. 2023, doi: 10.1016/j.cose.2023.103344. [11] B. Oztas, D. Cetinkaya, F. Adedoyin, M. Budka, G. Aksu, and H. Dogan, ‘Transaction monitoring in anti-money laundering: A qualitative analysis and points of view from industry’, Future Generation Computer Systems, vol. 159, pp. 161–171, Oct. 2024, doi: 10.1016/j.future.2024.05.027. [12] Adedayo Idowu Sunday, Agama Omachi, Kehinde Daniel Abiodun, Shereef Olayinka Jinadu, and Esther Alaka, ‘Enhancing Real-Time Transaction Monitoring through Al- Driving AML Frameworks for U.S financial system’, July 2025, doi: 10.5281/ZENODO.15878854. [13] M. Bramantyo, ‘“Where do you hide your money?”: The explanation of low conviction in money laundering’, Journal of Economic Criminology, vol. 8, p. 100161, June 2025, doi: 10.1016/j.jeconc.2025.100161. [14] D. Caruso, ‘Why False Positives No Longer Matter in AML’, Why False Positives No Longer Matter in AML. [Online]. Available: https://www.workfusion.com/blog/false-positives-do-not-matter-in-aml/ [15] M. D. I. Bin Ibrahim, F. A. Binti Mohd Fikri, M. A. A. Bin Puasa, N. F. B. Mohd Fadzil, and Y. H. Yusoff, ‘Evolution of Money Laundering Typologies: A Concept Paper’, IJRISS, vol. IX, no. VII, pp. 1360–1374, 2025, doi: 10.47772/IJRISS.2025.907000113. [16] S. Antwi, A. B. Tetteh, P. Armah, and E. O. Dankwah, ‘Anti-money laundering measures and financial sector development: Empirical evidence from Africa’, Cogent Economics & Finance, vol. 11, no. 1, p. 2209957, Dec. 2023, doi: 10.1080/23322039.2023.2209957. [17] J. R. Oliveira and A. G. Leal, ‘Enhancing Anti-Money Laundering Protocols: Employing Machine Learning to Minimise False Positives and Improve Operational Cost Efficiency’, in Proceedings of the 2024 8th International Conference on Advances in Artificial Intelligence, London United Kingdom: ACM, Oct. 2024, pp. 8–13. doi: 10.1145/3704137.3704156. [18] B. Deprez, T. Vanderschueren, B. Baesens, T. Verdonck, and W. Verbeke, ‘Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation’, INFORMS Journal on Data Science, p. ijds.2024.0042, Oct. 2025, doi: 10.1287/ijds.2024.0042. [19] N. Kim, S. Patel, R. Mendoza, A. Martinez, I. Cruz, and P. Singh, ‘Graph Neural Networks for Anomaly Detection in Financial Transactions’, 2025, doi: 10.13140/RG.2.2.11881.40805. [20] O. Babatunde-Faustino, ‘AI-Driven Transaction Monitoring Systems for AML in Digital Banking’, SSRN Journal, 2025, doi: 10.2139/ssrn.5393034. [21] Venkata Raja Ravi Kumar Gelle, ‘Enhancing financial security: AI-driven anti-money laundering (AML) and compliance monitoring in the banking sector’, World J. Adv. Res. Rev., vol. 25, no. 1, pp. 2462–2476, Jan. 2025, doi: 10.30574/wjarr.2025.25.1.0365. [22] O. Oyedokun, S. E. Ewim, and O. P. Oyeyemi, ‘A comprehensive review of machine learning applications in AML transaction monitoring’, International Journal of Engineering Research and Development, vol. 20, no. 11, pp. 173–143, 2024. [23] M. Jiao, ‘Big Data Analytics for Anti-Money Laundering Compliance in the Banking Industry’, HSET, vol. 49, pp. 302–309, May 2023, doi: 10.54097/hset.v49i.8522. [24] B. Herron and S. Duggal, ‘Deploying Machine Learning Models in an Anti-Money Laundering (AML) Program’, SAS Global Forum 2020, no. Paper SAS4553-2020. [25] V. Papastefanopoulos, P. Linardatos, and S. Kotsiantis, ‘Unsupervised Outlier Detection: A Meta-Learning Algorithm Based on Feature Selection’, Electronics, vol. 10, no. 18, p. 2236, Sept. 2021, doi: 10.3390/electronics10182236. [26] A. N. Bakry, A. S. Alsharkawy, M. S. Farag, and K. R. Raslan, ‘Automatic suppression of false positive alerts in anti-money laundering systems using machine learning’, J Supercomput, vol. 80, no. 5, pp. 6264–6284, Mar. 2024, doi: 10.1007/s11227-023-05708-z. [27] Í. D. G. Silva, L. H. A. Correia, and E. G. Maziero, ‘Graph Neural Networks Applied to Money Laundering Detection in Intelligent Information Systems’, in Proceedings of the XIX Brazilian Symposium on Information Systems, Maceió Brazil: ACM, May 2023, pp. 252–259. doi: 10.1145/3592813.3592912. [28] R. I. T. Jensen and A. Iosifidis, ‘Fighting Money Laundering With Statistics and Machine Learning’, IEEE Access, vol. 11, pp. 8889–8903, 2023, doi: 10.1109/ACCESS.2023.3239549. [29] C. Charitou, S. Dragicevic, and A. d’Avila Garcez, ‘Synthetic Data Generation for Fraud Detection using GANs’, Sept. 26, 2021, arXiv: arXiv:2109.12546. doi: 10.48550/arXiv.2109.12546. [30] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, ‘SMOTE: Synthetic Minority Over-sampling Technique’, jair, vol. 16, pp. 321–357, June 2002, doi: 10.1613/jair.953. [31] C. Elkan, ‘The Foundations of Cost-Sensitive Learning’, Proceedings of the Seventeenth International Conference on Artificial Intelligence: 4-10 August 2001; Seattle, vol. 1, May 2001. [32] A. K. Shaikh, M. Al-Shamli, and A. Nazir, ‘Designing a relational model to identify relationships between suspicious customers in anti-money laundering (AML) using social network analysis (SNA)’, J Big Data, vol. 8, no. 1, p. 20, Dec. 2021, doi: 10.1186/s40537-021-00411-3. [33] Q. Yu, S. Wang, and Y. Tao, ‘Enhancing Anti-Money Laundering Detection with Self-Attention Graph Neural Networks’, SHS Web Conf., vol. 213, p. 01016, 2025, doi: 10.1051/shsconf/202521301016. [34] R. I. T. Jensen et al., ‘A synthetic data set to benchmark anti-money laundering methods’, Sci Data, vol. 10, no. 1, p. 661, Sept. 2023, doi: 10.1038/s41597-023-02569-2. [35] D. V. Kute, B. Pradhan, N. Shukla, and A. Alamri, ‘Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review’, IEEE Access, vol. 9, pp. 82300–82317, 2021, doi: 10.1109/ACCESS.2021.3086230. [36] I. Adegbola, ‘Explainable AI (XAI) for Enhancing Transparency in Money Laundering Risk Assessment’, July 10, 2025, Preprints. doi: 10.22541/au.175216956.69899227/v1. [37] P. Linardatos, V. Papastefanopoulos, and S. Kotsiantis, ‘Explainable AI: A Review of Machine Learning Interpretability Methods’, Entropy, vol. 23, no. 1, p. 18, Dec. 2020, doi: 10.3390/e23010018. [38] A. V. Ponce‐Bobadilla, V. Schmitt, C. S. Maier, S. Mensing, and S. Stodtmann, ‘Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development’, Clinical Translational Sci, vol. 17, no. 11, p. e70056, Nov. 2024, doi: 10.1111/cts.70056. [39] M. Louhichi, R. Nesmaoui, M. Mbarek, and M. Lazaar, ‘Shapley Values for Explaining the Black Box Nature of Machine Learning Model Clustering’, Procedia Computer Science, vol. 220, pp. 806–811, 2023, doi: 10.1016/j.procs.2023.03.107. [40] Vivian Ofure Eghaghe, Olajide Soji Osundare, Chikezie Paul-Mikki Ewim, and Ifeanyi Chukwunonso Okeke, ‘Navigating the ethical and governance challenges of ai deployment in AML practices within the financial industry’, Int. J. Scholarly Res. Rev., vol. 5, no. 2, pp. 030–051, Oct. 2024, doi: 10.56781/ijsrr.2024.5.2.0047. [41] A. Shirvanporzour, ‘Artificial Intelligence in Banking Risk Management and Anti-Money Laundering: A Comprehensive Review’, 2025. doi: 10.2139/ssrn.5161209. [42] N. N. Ridzuan, M. Masri, M. Anshari, N. L. Fitriyani, and M. Syafrudin, ‘AI in the Financial Sector: The Line between Innovation, Regulation and Ethical Responsibility’, Information, vol. 15, no. 8, p. 432, July 2024, doi: 10.3390/info15080432. [43] P. Kamalaruban et al., ‘Evaluating Fairness in Transaction Fraud Models: Fairness Metrics, Bias Audits, and Challenges’, 2024, arXiv. doi: 10.48550/ARXIV.2409.04373. [44] N. Paladugu, ‘Privacy-Aware Graph Embeddings for Anti-Money Laundering Pipelines’, SSRN Journal, 2025, doi: 10.2139/ssrn.5320964. [45] N. Pocher, M. Zichichi, F. Merizzi, M. Z. Shafiq, and S. Ferretti, ‘Detecting anomalous cryptocurrency transactions: An AML/CFT application of machine learning-based forensics’, Electron Markets, vol. 33, no. 1, p. 37, Dec. 2023, doi: 10.1007/s12525-023-00654-3. [46] Vivian Ofure Eghaghe, Olajide Soji Osundare, Chikezie Paul-Mikki Ewim, and Ifeanyi Chukwunonso Okeke, ‘Advancing AML tactical approaches with data analytics: Transformative strategies for improving regulatory compliance in banks’, Financ. account. res. j., vol. 6, no. 10, pp. 1893–1925, Oct. 2024, doi: 10.51594/farj.v6i10.1644. [47] Md. R. Karim, F. Hermsen, S. A. Chala, P. De Perthuis, and A. Mandal, ‘Scalable Semi-Supervised Graph Learning Techniques for Anti Money Laundering’, IEEE Access, vol. 12, pp. 50012–50029, 2024, doi: 10.1109/ACCESS.2024.3383784. [48] P. V. Naik, N. K. Dintakurthi, Z. Hu, Y. Wang, and R. Qiu, ‘Co-Investigator AI: The Rise of Agentic AI for Smarter, Trustworthy AML Compliance Narratives’, 2025, arXiv. doi: 10.48550/ARXIV.2509.08380. [49] K. Du, Y. Zhao, R. Mao, F. Xing, and E. Cambria, ‘Natural language processing in finance: A survey’, Information Fusion, vol. 115, p. 102755, Mar. 2025, doi: 10.1016/j.inffus.2024.102755. [50] V. Kalokyri et al., ‘AI Model Passport: Data and system traceability framework for transparent AI in health’, Computational and Structural Biotechnology Journal, vol. 28, pp. 386–404, 2025, doi: 10.1016/j.csbj.2025.09.041. [51] Y. Chen, C. Zhao, Y. Xu, C. Nie, and Y. Zhang, ‘Deep Learning in Financial Fraud Detection: Innovations, Challenges, and Applications’, Data Science and Management, p. S2666764925000372, Aug. 2025, doi: 10.1016/j.dsm.2025.08.002. [52] S. Huda, E. Foo, Z. Jadidi, M. H. Newton, and A. Sattar, ‘AMLNet: A Knowledge-Based Multi-Agent Framework to Generate and Detect Realistic Money Laundering Transactions’, 2025, arXiv. doi: 10.48550/ARXIV.2509.11595. |
