Autonomous Compliance Intelligence Networks for Dynamic Sanctions Risk Detection in Global Financial Systems
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DOI:
https://doi.org/10.67228/3071642X/IJCFDE-2024PII2S5RPublished 11-07-2024
Autonomous Compliance Intelligence, Dynamic Sanctions Risk Detection, Global Financial Systems, Sanctions Screening and Monitoring, Financial Crime Analytics, AI-Driven Compliance Networks, Real-Time Risk Intelligence, Multi-Agent Decision Systems, Regulatory Technology (RegTech), Explainable Compliance Analytics Issue
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ArticlesHow to Cite
Abhireddy, N. (2024). Autonomous Compliance Intelligence Networks for Dynamic Sanctions Risk Detection in Global Financial Systems. International Journal of Commerce, Finance and Digital Economy, 7(2), 01-20. https://doi.org/10.67228/3071642X/IJCFDE-2024PII2S5RAbstract
Advances in artificial intelligence, natural language processing, and networked data-sharing systems now enable real-time detection of sanctions risks, whether emerging unexpectedly or preannounced. Autonomous Compliance Intelligence Networks integrate machine learning, data fusion, process mining, and natural language processing to establish a novel framework for continuously monitoring policy changes in the global financial ecosystem. Detecting sanctions risk autonomously and informally acting on it do not, however, provide legal grounds for rejecting or proceeding with a financial transaction. Ultimately, only forwarding the detection to an authorized individual or compliance department enables organized reinforcement of existing compliance controls. Nevertheless, relying on the network to issue alerts according to a precise set of internal rules contributes to cumulative, bottom-up compliance. At this stage, it is also possible to build Autonomous Compliance Intelligence Networks that continually return detected anomalies to a combination of detection oriented and regression oriented feedback loops. These give machine learning records honed by the collective efforts and collective feedback of the compliance officers sanctioned as rule implementers and doers. For the detection component of Autonomous Compliance Intelligence Networks, the task of identifying sanctions risk in a structured connection-to-connection transaction-tracking network corresponds to the pattern recognition domain in data-driven machine learning. Here, the objective is to detect the emergence of a pattern that may not necessarily be pleasant anywhere: new, expected or unexpected entities, or versions of entities, transactions or connections, that pose a new type of sanctions risk—fully designated global, virtual, physical, contextual, or damaging risk capable of being anything with a 100% certainty gap of a zero-based identity. The task is similar to discovery in exploratory data analysis, where one is trying to detect Eulerian polygons or surfaces in a 8- or 10-degree space. Here, expected resources appear as anomaly spikes, ‘beginning’ resources disappear and ‘end’ resources become detectable.
References
[1] Alexandre, C. R., & Balsa, J. (2023). Incorporating machine learning and a risk-based strategy in an anti-money laundering multiagent system. Expert Systems with Applications, 217, Article 119500.
[2] Amistapuram, K. (2021). Digital Transformation in Insurance: Migrating Enterprise Policy Systems to .NET Core. Universal Journal of Computer Sciences and Communications, 1(1), 1-17.
[3] Ali, A., Razak, S. A., 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, 12(19), Article 9637.
[4] Alarab, I., & Prakoonwit, S. (2023). Graph-based LSTM for anti-money laundering: Experimenting temporal graph convolutional network with Bitcoin data. Neural Processing Letters, 55, 689–707.
[5] DAVULURI, P. N. (2022). Cloud-Native Data Platform Modernization for Regulatory Compliance in Global Banking. Kurdish Studies.
[6] Astrova, I. (2023). Anti-money laundering powered by graph machine learning: “Show me your friends and I will tell you who you are.” Intelligent Decision Technologies, 17(1).
[7] Awosika, T., Shukla, R. M., & Pranggono, B. (2023). Transparency and privacy: The role of explainable AI and federated learning in financial fraud detection. arXiv preprint.
[8] Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls. (2024) sep
[9] Bakry, A. N., Alsharkawy, A. S., Farag, M. S., & Raslan, K. R. (2023). Automatic suppression of false positive alerts in anti-money laundering systems using machine learning. The Journal of Supercomputing, 80, 6264–6284.
[10] Bakhshinejad, N., & Nguyen, U. T. (2022). A new graph-based machine learning model for anti-money laundering. Proceedings/Preprint.
[11] Loganathan, R. (2021). Integrated Risk and Compliance Frameworks for Global Data Center Operations: A Governance-Centric Approach. Universal Journal of Computer Sciences and Communications, 1(1), 1-26.
[12] Canhoto, A. I. (2021). Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective. Journal of Business Research, 131, 441–452.
[13] Cardoso, M., Saleiro, P., & Bizarro, P. (2022). LaundroGraph: Self-supervised graph representation learning for anti-money laundering. arXiv preprint.
[14] Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.-A., Caelen, O., Mazzer, Y., & Bontempi, G. (2021). SCARFF: A scalable framework for streaming credit card fraud detection with Spark. Information Fusion, 64, 132–142.
[15] Kolla, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248–260.
[16] Cheng, D., Ye, Y., Xiang, S., Ma, Z., Zhang, Y., & Jiang, C. (2023). Anti-money laundering by group-aware deep graph learning. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12444–12457.
[17] Eddin, A. N., Bono, J., Aparício, D., Polido, D., Ascensão, J. T., Bizarro, P., & Ribeiro, P. (2021). Anti-money laundering alert optimization using machine learning with graphs. arXiv preprint.
[18] Bandi, V. D. V. K. (2023). MLOps frameworks for reliable model deployment in cloud data platforms. Journal of Artificial Intelligence and Big Data, 3(1), 84-101.
[19] Farheen, S., & Raghuwanshi, M. (2021). Performance of machine learning techniques in the prevention of financial frauds. International Journal of Computer Sciences and Engineering, 9(1), 27–29.
[20] Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448–455.
[21] Ghosh, S., & Reilly, D. (2021). Machine learning approaches for financial fraud detection and risk analytics. Journal of Financial Crime, 28(4), 1101–1115.
[22] Yandamuri, U. S. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10, 472-483.
[23] Goecks, L., Oehmichen, F., & others. (2022). Anti-money laundering and financial fraud detection: A systematic literature review. Intelligent Systems in Accounting, Finance and Management, 29(2), 71–85.
[24] Gramespacher, T., & Posth, J.-A. (2021). Employing explainable AI to optimize the return target function of a loan portfolio. Frontiers in Artificial Intelligence, 4, Article 693022.
[25] Kolla, S. H. (2023). Large Language Model-Driven Enterprise Service Intelligence for Digital Workflow Transformation. International Journal of Research and Applied Innovations, 6(1), 8380-8391.
[26] Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection techniques and recent advances. Expert Systems with Applications, 193, Article 116429.
[27] Husnaningtyas, N., Hanin, G. F., Dewayanto, T., & Malik, M. F. (2023). A systematic review of anti-money laundering systems literature: Exploring the efficacy of machine learning and deep learning integration. JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen, 20(1), 91–116.
[28] Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.
[29] Jensen, R. I. T., & Iosifidis, A. (2023). Qualifying and raising anti-money laundering alarms with deep learning. Expert Systems with Applications, 214, Article 119037.
[30] Jullum, M., Løland, A., Huseby, R. B., Ånonsen, G., & Lorentzen, J. (2020). Detecting money laundering transactions with machine learning. Journal of Money Laundering Control, 23(1), 173–186.
[31] Khan, A. T., Cao, X., Li, S., Katsikis, V. N., Brajević, I., & Stanimirović, P. S. (2022). Fraud detection in publicly traded U.S. firms using Beetle Antennae Search: A machine learning approach. Expert Systems with Applications, 191, Article 116148.
[32] Kelly, B. T., & Xiu, D. (2023). Financial machine learning. Foundations and Trends in Finance, 13(3–4), 205–363.
[33] Kuiper, O., van den Berg, M., van der Burgt, J., & Leijnen, S. (2021). Exploring explainable AI in the financial sector: Perspectives of banks and supervisory authorities. arXiv preprint.
[34] Landauer, M., Onder, S., Skopik, F., & Wurzenberger, M. (2022). Deep learning for anomaly detection in log data: A survey. arXiv preprint.
[35] Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).
[36] Li, Q., He, Y., Xu, C., Wu, F., Gao, J., & Li, Z. (2022). Dual-augment graph neural network for fraud detection. Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 4188–4192.
[37] Liu, X., Yu, Y., Chen, Y., & Zhang, J. (2022). Artificial intelligence for financial fraud detection: A review and future directions. Expert Systems with Applications, 193, Article 116377.
[38] Lucas, Y., & Jurgovsky, J. (2020). Credit card fraud detection using machine learning: A survey. arXiv preprint.
[39] Lu, M., Han, Z., Rao, S. X., Zhang, Z., Zhao, Y., Shan, Y., Raghunathan, R., Zhang, C., & Jiang, J. (2022). BRIGHT—Graph neural networks in real-time fraud detection. arXiv preprint.
[40] Reddy, V. A. R. (2023). Orchestrating the Future Autonomous Healthcare Data Pipeline Management through Agentic AI Architectures. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7979-7992.
[41] Motie, S., & Raahemi, B. (2023). Financial fraud detection using graph neural networks: A systematic review. Expert Systems with Applications, 240, Article 122156.
[42] Pocher, N., Zichichi, M., Merizzi, F., Shafiq, M. Z., & Ferretti, S. (2023). Detecting anomalous cryptocurrency transactions: An AML/CFT application of machine learning-based forensics. Electronic Markets, 33, Article 37.
[43] Sahin, Y., & Duman, E. (2021). Detecting credit card fraud by decision trees and support vector machines. Expert Systems with Applications, 193, Article 116387.
[44] Amistapuram, K. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950-5958.
[45] Mattaparthi, R. (2023). Deep Learning-Driven Combustion Anomaly Detection in Diesel Powertrains: A Multi-Sensor Fusion Approach for Real-Time ECM Adaptation. International Journal of Intelligent Systems and Applications in Engineering, 11, 1084.
[46] Shou, M., Bao, X., & Yu, J. (2023). An optimal weighted machine learning model for detecting financial fraud. Applied Economics Letters, 30(4), 410–415.
[47] Taser, P. Y., & Bozyiğit, F. (2022). Machine learning applications for fraud detection in finance sector. In The impact of artificial intelligence on governance, economics and finance (Vol. 2, pp. 121–146).
[48] Tian, Y., Liu, G., Wang, J., & Zhou, M. (2023). Transaction fraud detection via an adaptive graph neural network. arXiv preprint.
[49] Kolla, S. H., & Loganathan, R. (2023). Cloud-Native Deep Learning Architectures For Secure Generative AI Deployment In Enterprise Workflow Platforms. Journal of International Crisis and Risk Communication Research, 603-618.
[50] Vilella, S., Capozzi Lupi, A. T. E., Ruffo, G., Fornasiero, M., Moncalvo, D., Ricci, V., & Ronchiadin, S. (2023). Exploiting graph metrics to detect anomalies in cross-country money transfer temporal networks. In Proceedings of the ACM Web Conference 2023 Companion, 1245–1248.
[51] Mattaparthi, R. (2022). Engineering Predictive Industrial Systems Through IoT-Driven Asset Monitoring and Machine Learning Prognostics. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7790.
[52] Nagabhyru, K. C., & Engineer, S. D. (2023). Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment.
[53] Weber, P., Carl, K. V., & Hinz, O. (2024). Applications of explainable artificial intelligence in finance—A systematic review of finance, information systems, and computer science literature. Management Review Quarterly, 74, 867–907. [Published online in 2023.] (2024) June
[54] Yeo, W. J., van der Heever, W., Mao, R., Cambria, E., Satapathy, R., & Mengaldo, G. (2023). A comprehensive review on financial explainable AI. arXiv preprint.
[55] Davuluri, P. N. Event-Driven Compliance Systems: Modernizing Financial Crime Detection Without Machine Intelligence.
[56] Zhou, Y., Li, H., Xiao, Z., & Qiu, J. (2023). A user-centered explainable artificial intelligence approach for financial fraud detection. Finance Research Letters, 58, Article 154461.
[57] Zhou, Y., Sun, M., & Zhang, F. (2023). Graph neural network-based anomaly detection in financial transaction networks. Journal of Computing Innovations and Applications, 1(2), 87–101.
[58] Kolla, S. H. (2022). Strategic Information Integration Models for Cross-Functional Service Optimization in Large-Scale Enterprises. International Journal of Emerging Trends in Engineering and Management Research, 7(3), 11811.
[59] Alarab, I., Prakoonwit, S., & Naim, M. (2021). Competence of graph convolutional networks for anti-money laundering in Bitcoin blockchain. Proceedings of the International Conference on Computational Science and Its Applications.
[60] Lo, W. W., Kulatilleke, G. K., Sarhan, M., Layeghy, S., & Portmann, M. (2022). Inspection-L: Self-supervised GNN node embeddings for money laundering detection in Bitcoin. arXiv preprint.
[61] Pocher, N., Zichichi, M., Shafiq, M. Z., & Ferretti, S. (2022). Machine learning and transaction graph analysis for AML/CFT applications. arXiv preprint.
[62] Reddy, V. A. R. (2023). Predictive Healthcare Administration Using Advanced Payer Analytics and Population Health Data Engineering. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7967-7978.
[63] Valiki, D., & Segireddy, A. R. (2023). Deep Learning Architectures Deployed on Cloud Platforms for Dynamic Financial Risk Evaluation and Market Prediction. American International Journal of Computer Science and Technology, 5(5), 12-24.
[64] Yuan, S., & Wu, X. (2022). Trustworthy anomaly detection: A survey. arXiv preprint.
[65] Zhang, T., & Ye, H. (2023). Fraud detection based on graph neural network. In Advances in Artificial Intelligence, Big Data and Algorithms.
[66] Inala, R. (2023). Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 579-606.
[67] Zhang, Y., Li, J., Zhu, Y., & Chen, H. (2021). A survey of financial fraud detection approaches: From traditional methods to data-driven solutions. Information Systems Frontiers, 23(5), 1123–1142.
[68] Ravi, V., Kiran, R. U., Fahd, K., & Menon, V. G. (2022). Deep learning approaches for financial fraud detection: A review of recent advances and emerging challenges. Information Sciences, 589, 1–21.
[69] Yildirim Taser, P., & Bozyiğit, F. (2022). Machine learning applications for fraud detection in the finance sector. In The impact of artificial intelligence on governance, economics and finance.
[70] Mangala, N. (2022). Implementing Databricks Unity Catalog For Centralized Data Governance In Multi-Business-Unitenterprises. Journal of International Crisis and Risk Communication Research, 101-122.
[71] Canhoto, A. I., & Clear, F. (2020). Artificial intelligence and machine learning in financial crime and compliance: Emerging opportunities and challenges. Journal of Financial Crime, 27(4), 1187–1201.
[72] Carcillo, F., Le Borgne, Y.-A., Caelen, O., & Bontempi, G. (2020). Streaming credit card fraud detection with adaptive machine learning. Information Fusion, 64, 132–142.
[73] Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.-E., He-Guelton, L., & Caelen, O. (2020). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234–245.
[74] Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2020). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3784–3797.
[75] Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
[76] Abdallah, A., Maarof, M. A., & Zainal, A. (2020). Fraud detection system: A survey. Journal of Network and Computer Applications, 68, 90–113.
[77] West, J., & Bhattacharya, M. (2021). Intelligent financial fraud detection: A comprehensive review. Computers & Security, 57, 47–66.
[78] Kolla, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248-260.
[79] Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2020). The application of data mining techniques in financial fraud detection: A review and research agenda. Decision Support Systems, 50(3), 559–569.
[80] Pourhabibi, T., Ong, K.-L., Kam, B. H., & Boo, Y. L. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 133, Article 113303.
[81] Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
[82] Abdallah, A., Maarof, M. A., & Zainal, A. (2021). Fraud detection system: A survey of machine learning and data-mining approaches. Security and Communication Networks, 2021, Article 8893566.
[83] Hilal, W., Gadsden, S. A., & Yawney, J. (2021). Financial fraud detection using machine learning and anomaly detection techniques. Expert Systems with Applications.
[84] Tuffveson Jensen, R. I., & Iosifidis, A. (2022). Deep learning approaches for anti-money laundering transaction monitoring. Expert Systems with Applications.
[85] Loganathan, R. (2022). Converging Security Architecture and Compliance Management in Enterprise Data Center Ecosystems: A Unified Control Framework. International Journal of Scientific Research and Modern Technology, 1(12), 295-312.
[86] Weber, P., Carl, K. V., & Hinz, O. (2023). Explainable artificial intelligence in finance: Transparency, accountability, and regulatory implications. Management Review Quarterly.
[87] Mattaparthi, R. (2023). Connected Fleet Intelligence: Edge-Centric Analytics and Computer Vision for Predictive Manufacturing and Asset Resilience. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9077-9088.
[88] Motie, S., & Raahemi, B. (2023). Graph-based approaches for financial fraud detection and risk analytics. Expert Systems with Applications.
[89] Divya, V., & Bandi, V. K. (2023). Cloud-Native Model Lifecycle Management for Enterprise AI Systems. International Journal of Scientific Research and Modern Technology, 78.
[90] Vilella, S., Fornasiero, M., Moncalvo, D., Ricci, V., Ronchiadin, S., & Ruffo, G. (2023). Anomaly detection in cross-country money transfer temporal networks. arXiv preprint.
[91] Cheng, D., Ye, Y., Xiang, S., Ma, Z., Zhang, Y., & Jiang, C. (2023). Deep graph learning approaches for anti-money laundering and suspicious transaction detection. IEEE Transactions on Knowledge and Data Engineering.
[92] Peddi, R. K. (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21.
[93] Pocher, N., Zichichi, M., Merizzi, F., Shafiq, M. Z., & Ferretti, S. (2023). Machine learning-based forensic analysis of cryptocurrency transactions for AML/CFT. Electronic Markets.
[94] Kolla, S. K. (2023). Learning Health Systems Machine Intelligence for Clinical Prediction and Healthcare Optimization. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7955-7966.
[95] Zhou, Y., Li, H., Xiao, Z., & Qiu, J. (2023). Explainable artificial intelligence for financial fraud detection: User-centered explanations and model transparency. Finance Research Letters, 58, Article 154461.
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Abhireddy, N. (2024). Autonomous Compliance Intelligence Networks for Dynamic Sanctions Risk Detection in Global Financial Systems. International Journal of Commerce, Finance and Digital Economy, 7(2), 01-20. https://doi.org/10.67228/3071642X/IJCFDE-2024PII2S5R
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