AI-Driven Secure Transaction Governance in Cloud Finance
Keywords:
Generative Artificial Intelligence in Financial Governance, Augmented Governance by Design, Real-Time Secure Transaction Processing, Cloud-Based Financial Ecosystems, AI-Driven Policy and Compliance Automation, Governance Decision Proposal Frameworks, Risk–Control–Accountability Integration, Explainable and Auditable AI Systems, Transparency in Financial Decision-Making, Trust-Centric AI Architectures, Provenance-Enriched Data Pipelines, Compliance-Aware AI Deployment, Secure Cloud Governance Models, RegTech-Enabled Financial Oversight, AI-Supported Enterprise Risk Management.Abstract
Financial governance may benefit from generative AI as it promotes augmented governance by design. The resulting capability is operationalized as a blueprint for real-time secure transaction processing in cloud ecosystems. Through the design and integration of the various system layers, desired characteristics of transparency, explainability, auditability, and trust become built-in features that support and enhance the risk management, control, and decision-making processes of stakeholders across the value network by providing and assuring consistent information, even in the presence of uncertainty.
Generative AI is applied to define the decision-making process, specifying how the Generative AI Governance Decision Proposal defines financial policy and compliance. Consequently, the related sense—analyze—respond model of augmented governance identifies the roles, responsibilities, decision rights, escalation paths, and governance rituals that provide the operational framework for governance. Data ingestion pipelines enrich the provenance of information to support enhanced risk–control–accountability. Transparency is specified from a governance perspective, defining policy alignment for trust in the AI–enabled financial decision outcomes.
References
1. 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, 317–331.
2. Benchaji, I., Douzi, S., El Ouahidi, B., & Jaafari, J. (2021). Enhanced credit card fraud detection based on attention mechanism and LSTM deep model. Journal of Big Data, 8, Article 151.
3. Jogi, N. L., Kumar, K. R., Mangala, N., M.Govindarajan, Gamini, P., & Bundhe, S. (2026). Machine Learning-Based Random Forest Model for Sustainable Supply Chain Sales Forecasting. In 2026 6th International Conference on Intelligent Technologies (CONIT) (pp. 1–6). IEEE. 2026 6th International Conference on Intelligent Technologies (CONIT). https://doi.org/10.1109/conit69683.2026.11620788
4. Wang, L., Zhang, Z., Zhang, X., Zhou, X., Wang, P., & Zheng, Y. (2021). A deep-forest based approach for detecting fraudulent online transaction. In A. R. Hurson & S. Wu (Eds.), AI and Cloud Computing (pp. 1–38). Academic Press.
5. 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.
6. 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, 12(19), Article 9637.
7. Recharla, M., Garapati, R. S., Bandi, V. D. V. K., Yandamuri, U. S., & Mangalampalli, B. M. (2026, March). Generative AI-Enhanced Data Engineering Pipelines for Predictive Biomarker Discovery in Alzheimer’s and Kidney Disease. In 2026 IEEE International Conference on AI Engineering and Innovations (AIEI) (pp. 1-5). IEEE.
8. Ileberi, E., Sun, Y., & Wang, Z. (2022). A machine learning based credit card fraud detection using the GA algorithm for feature selection. Journal of Big Data, 9, Article 24.
9. Singh, A., Jain, A., & Biable, S. E. (2022). Financial fraud detection approach based on firefly optimization algorithm and support vector machine. Applied Computational Intelligence and Soft Computing, 2022, Article 1468015.
10. Alamri, M., & Ykhlef, M. (2022). Survey of credit card anomaly and fraud detection using sampling techniques. Electronics, 11(23), Article 4003.
11. Lakhanpal, S., Tunjungsari, H. K., Raj, S., Chukka, A., Dawadi, D., & Mangalampalli, B. M. (2026, April). The Digital Transformation of Healthcare: Balancing Opportunities, Risks, and Ethical Considerations. In 2026 International Conference on Multidisciplinary Innovations For Smart & Sustainable Future (MISSF) (pp. 1-6). IEEE.
12. Aschi, M., Bonura, S., Masi, N., Messina, D., & Profeta, D. (2022). Cybersecurity and fraud detection in financial transactions. In Big Data and Artificial Intelligence in Digital Finance (pp. 269–278). Springer.
13. Alarfaj, F. K., Malik, I., Khan, H. U., Almusallam, N., Ramzan, M., & Ahmed, M. (2022). Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms. IEEE Access, 10, 39700–39715.
14. Lin, K., & Gao, Y. (2022). Model interpretability of financial fraud detection by group SHAP. Expert Systems with Applications, 210, Article 118354.
15. Lei, Y.-T., Ma, C.-Q., Ren, Y.-S., Chen, X.-Q., Narayan, S., & Huynh, A. N. Q. (2023). A distributed deep neural network model for credit card fraud detection. Finance Research Letters, 58, Article 104547.
16. Nandan, B. P., Kumar, M. V. K., Garapati, R. S., Bandi, V. D. V. K., Davuluri, P. N., & Mangalampalli, B. M. (2026, March). AI-Enhanced Semiconductor Yield Optimization Using Hybrid Deep Learning and Edge Data Analytics. In 2026 IEEE International Conference on AI Engineering and Innovations (AIEI) (pp. 1-6). IEEE.
17. Salekshahrezaee, Z., Leevy, J. L., & Khoshgoftaar, T. M. (2023). The effect of feature extraction and data sampling on credit card fraud detection. Journal of Big Data, 10, Article 6.
18. Fanai, H., & Abbasimehr, H. (2023). A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection. Expert Systems with Applications, 217, Article 119562.
19. Xiao, J., Tian, Y., Jia, Y., Jiang, X., Yu, L., & Wang, S. (2023). Black-box attack-based security evaluation framework for credit card fraud detection models. INFORMS Journal on Computing, 35(5), 986–1001.
20. Zioviris, G., Kolomvatsos, K., & Stamoulis, G. (2024). An intelligent sequential fraud detection model based on deep learning. The Journal of Supercomputing, 80, 14824–14847.
21. Kolla, S. H., Nagabhyru, K. C., & Kummari, D. N. (2026). Letter to the Editor re:“CurvAssist: An AI assisted pipeline for penile shaft segmentation/curvature measurement in children with hypospadias”. Journal of Pediatric Urology.
22. Talukder, M. A., Khalid, M., & Uddin, M. A. (2024). An integrated multistage ensemble machine learning model for fraudulent transaction detection. Journal of Big Data, 11, Article 168.
23. Talukder, M. A., Hossen, R., Uddin, M. A., Uddin, M. N., & Acharjee, U. K. (2024). Securing transactions: A hybrid dependable ensemble machine learning model using IHT-LR and grid search. Cybersecurity, 7, Article 32.
24. Gopinath, A., Davuluri, P. N., Kumar, U. B., MP, S., & Yamini, G. (2026, April). Communication Aware Malware Detection Using Network Flow Bytecode Fusion With Adaptive Focal Optimization. In 2026 2nd International Conference on Intelligent Systems and Computational Networks (ICISCN) (pp. 1-8). IEEE.
25. Asmar, M., & Tuqan, A. (2024). Integrating machine learning for sustaining cybersecurity in digital banks. Heliyon, 10(17), Article e37571.
26. Hernández Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., & Rodríguez Barrero, M. S. (2024). Financial fraud detection through the application of machine learning techniques: A literature review. Humanities and Social Sciences Communications, 11, Article 1130.
27. Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic review. Expert Systems with Applications, 240, Article 122156.
28.
29. Kolla, S. K., Bandi, V. D. V. K., & Meda, R. (2026). Comment on “From laboratory promise to decision-grade practice: strengthening reproducibility and casework relevance in AI-assisted bloodstain ageing”. Forensic Science, Medicine and Pathology, 1-2.
30. Wahid, A., Msahli, M., Bifet, A., & Memmi, G. (2024). NFA: A neural factorization autoencoder based online telephony fraud detection. Digital Communications and Networks, 10(1), 158–167.
31. Khalid, A. R., Owoh, N., Uthmani, O., Ashawa, M., Osamor, J., & Adejoh, J. (2024). Enhancing credit card fraud detection: An ensemble machine learning approach. Big Data and Cognitive Computing, 8(1), Article 6.
32. Sorour, S. E., AlBarrak, K. M., Abohany, A., & Abd El-Mageed, A. A. (2024). Credit card fraud detection using the brown bear optimization algorithm. Alexandria Engineering Journal, 104, 171–192.
33. Mosa, D. T., Sorour, S. E., Abohany, A. A., & Maghraby, F. A. (2024). CCFD: Efficient credit card fraud detection using meta-heuristic techniques and machine learning algorithms. Mathematics, 12(14), Article 2250.
34. Hafez, I. Y., Hafez, A. Y., Saleh, A., Abd El-Mageed, A. A., & Abohany, A. A. (2025). A systematic review of AI-enhanced techniques in credit card fraud detection. Journal of Big Data, 12, Article 6.
35. Subramanian, N., & Kolla, T. Equity in Healthcare Access Policy Lessons from Universal Coverage Models.
36. Cheng, D., Zou, Y., Xiang, S., & Jiang, C. (2025). Graph neural networks for financial fraud detection: A review. Frontiers of Computer Science, 19(9), Article 199609.
37. Islam, S., Gupta, G. R., Chakraborty, A., Singh, S., Soni, A., & Patle, C. (2025). Detecting fraudulent transactions for different patterns in financial networks using layer weighted GCN. Human-Centric Intelligent Systems, 5, 181–195.
38. Çavdar, E. Z., & Bozanta, A. (2026). AI-based credit card fraud detection: A machine learning approach with model explainability on real-world data. Knowledge and Information Systems, 68, Article 161.
39. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).
40. Gorle, V. L. N., & Panigrahi, S. (2026). An efficient heuristic optimization-based fraudulent activities detection in the financial sector using adaptive machine learning and deep learning system. Expert Systems with Applications, 302, Article 130551.
