Data-Governed Autonomous Decisioning: AI Models for Real-Time Optimization of Enterprise Financial Journeys
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V2I1P111Keywords:
Data Governance, Real-Time Optimization, Enterprise Finance, Predictive Modeling, Enterprise FinancialAbstract
The business financial processes are under mounting pressure to be timely, regulated, and data-driven in nature. The existing financial decisioning processes are however limited by the aspect of fragmented data sources, delays in the pipeline of analytic processes, manual approvals and lack of single order of governance controls. Such restrictions are a detrimental factor in terms of timely risk management, optimal resource distribution, and active response to quickly evolving financial circumstances. To overcome such difficulties, the paper is going to suggest a data-governed and autonomous decisioning model that combines metadata-based governance with machine learning (ML) and end-of-real-time decision engines to optimize records of enterprise financial trips.The system proposed is a set of policy-conscious data management, predictive and optimization model, and closed-loop autonomous action engine to assist in automating decisions at high throughput and with compliance. Major elements are controlled data ingestion, sensitivity constrained feature pipelines, reinforcement learning-based optimization of financial events and explainable AI modules, which guarantee auditability and trust. Practical testing on real-world enterprise financial data shows that there are massive gains in decision latency, decision accuracy, compliance obedience, and financial performance relative to conventional rule-based systems. The findings demonstrate that data-controlled artificial intelligence decision models can provide considerable operational responsiveness and regulatory effectiveness to provide scalable, next-generation autonomous enterprise financial systems
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References
[1] Zaharia, M., Das, T., Li, H., Hunter, T., Shenker, S., & Stoica, I. (2013, November). Discretized streams: Fault-tolerant streaming computation at scale. In Proceedings of the twenty-fourth ACM symposium on operating systems principles (pp. 423-438).
[2] Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
[3] Duan, J. (2019). Financial system modeling using deep neural networks (DNNs) for effective risk assessment and prediction. Journal of the Franklin Institute, 356(8), 4716-4731.
[4] Sousa, M. R., Gama, J., & Brandão, E. (2016). A new dynamic modeling framework for credit risk assessment. Expert Systems with Applications, 45, 341-351.
[5] Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in neural information processing systems, 28.
[6] Armbrust, M., Das, T., Sun, L., Yavuz, B., Zhu, S., Murthy, M., ... & Zaharia, M. (2020). Delta lake: high-performance ACID table storage over cloud object stores. Proceedings of the VLDB Endowment, 13(12), 3411-3424.
[7] Chouldechova, A., & Roth, A. (2020). A snapshot of the frontiers of fairness in machine learning. Communications of the ACM, 63(5), 82-89.
[8] Papazoglou, M. P., & Van Den Heuvel, W. J. (2007). Service oriented architectures: approaches, technologies and research issues. The VLDB journal, 16(3), 389-415.
[9] Ahmad, S., & Purdy, S. (2016). Real-time anomaly detection for streaming analytics. arXiv preprint arXiv:1607.02480.
[10] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
[11] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144).
[12] Yubo, C. (2021). Innovation of enterprise financial management based on machine learning and artificial intelligence technology. Journal of Intelligent & Fuzzy Systems, 40(4), 6767-6778.
[13] Bhaskaran, S. V. (2020). Integrating data quality services (dqs) in big data ecosystems: Challenges, best practices, and opportunities for decision-making. Journal of Applied Big Data Analytics, Decision-Making, and Predictive Modelling Systems, 4(11), 1-12.
[14] Popov, G., Lyon, B. K., & Hollcroft, B. D. (2016). Risk assessment: A practical guide to assessing operational risks. John Wiley & Sons.
