Enterprise Financial Digital Twins: AI-Driven Simulation and Autonomous Risk Governance for Capital Markets

  • Authors

    • Vinod Battapothuss Independent Researcher, India. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2025PII5G7V

    Published 06-04-2025

  • Enterprise Financial Digital Twins, AI-Driven Financial Simulation, Autonomous Risk Governance, Capital Markets Intelligence, Digital Twin Analytics, Predictive Risk Modeling, Multi-Agent Financial Systems, Real-Time Market Simulation, Regulatory Compliance Automation, Decision Intelligence for Capital Markets

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    V. Battapothuss, “Enterprise Financial Digital Twins: AI-Driven Simulation and Autonomous Risk Governance for Capital Markets”, IJAIDT, vol. 8, no. 2, pp. 01–17, Jun. 2025, doi: 10.67228/30713315/IJAIDT-2025PII5G7V.

  • Abstract

    As the traditional risk governance landscape continues to focus on discrete assets and business units, the risk profile of the entire enterprise is likely to be missed. By contrast, an Enterprise Financial Digital Twin is proposed that mimics the behavior of the entire enterprise in a financial context, simulating multiple inputs and producing different capital market outputs. The aim is to create an AI-driven Simulation Engine as part of the Model Layer for Capital Markets and extend its horizons. Within this context, the Autonomous Risk Governance framework provides the regulatory link to ensure that the simulated activities of the enterprise remain within defined boundaries, even when the underlying data is driven by Generative AI. The first Financial Digital Twin was defined in 2021 as a real-time digital representation of the enterprise's financial data—covering applied accounting standards, tax regimes, and other enterprise-specific regulations—and capturing its risk profile. Capitalising on Enterprise Digital Twins that use functional data-driven Markov processes for scenario generation, Enterprise Financial Digital Twins are placed within the broader context of Digital Risk Governance. Risk Governance Services are based on the compliance and decision-making capabilities of Financial Digital Twins, and the proposed framework is self-sustaining in the sense that, as long as it is continually monitored and amended, it will compute appropriate decisions autonomously while remaining within desired compliance bounds.

  • References

    [1] Agrawal, A., Fischer, M., & Singh, V. (2022). Digital twin: From concept to practice. Journal of Management in Engineering, 38(3), 04022010.

    [2] Kolla, S. K., & Mangalampalli, B. M. (2024). Edge-Based Deep Learning Systems for Point-of-Care Diagnostic Intelligence. Journal of Neonatal Surgery, 13(1), 2387-2399.

    [3] Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.

    [4] Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.

    [5] Barredo Arrieta, A., et al. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.

    [6] Aitha, A. R. (2023). CloudBased Microservices Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 607-632.

    [7] Buehler, H., Gonon, L., Teichmann, J., & Wood, B. (2019). Deep hedging. Quantitative Finance, 19(8), 1271–1291.

    [8] Cao, L. (2022). AI in finance: Challenges, techniques, and opportunities. ACM Computing Surveys, 55(3), 1–38.

    [9] Černevičienė, J., & Kabašinskas, A. (2024). Explainable artificial intelligence (XAI) in finance: A systematic literature review. Artificial Intelligence Review, 57, 216.

    [10] Kolla, S. H., & Peddi, R. K. (2024). Designing Governance-Aligned GenAI Pipelines Using Small Language Models for Enterprise Workflow Intelligence. International Journal of Science, Research and Technology, 7(6), 13256-13268.

    [11] Chen, L., Pelger, M., & Zhu, J. (2024). Deep learning in asset pricing. Management Science, 70(2), 714–750.

    [12] Chen, W., Zhang, H., Mehlawat, M. K., & Jia, L. (2021). Mean–variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing, 100, 106943.

    [13] Amistapuram, K. (2024). Generative AI in Insurance: Automating Claims Documentation and Customer Communication. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 461-475.

    [14] Dixon, M. F., Halperin, I., & Bilokon, P. (2020). Machine learning in finance: From theory to practice. Springer.

    [15] Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., & Walther, A. (2022). Predictably unequal? The effects of machine learning on credit markets. The Journal of Finance, 77(1), 5–47.

    [16] Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971.

    [17] Giudici, P., & Raffinetti, E. (2021). Shapley-Lorenz explainable artificial intelligence. Expert Systems with Applications, 167, 114104.

    [18] Nagabhyru, K. C., & Engineer, S. D. (2023). Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment.

    [19] Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577.

    [20] Guan, M., & Liu, X.-Y. (2022). Explainable deep reinforcement learning for portfolio management: An empirical approach. Proceedings of the Second ACM International Conference on AI in Finance, 1–9.

    [21] Kolla, S. H., & Peddi, R. K. (2024). Designing Governance-Aligned GenAI Pipelines Using Small Language Models for Enterprise Workflow Intelligence. International Journal of Science, Research and Technology, 7(6), 13256-13268.

    [22] Avinash Pamisetty, Vijaya Rama Raju Gottimukkala. (2024). Agentic AI-Driven Multi-Cloud Big Data Architecture For Predictive Demand, Credit Risk, And Inventory Financing In National Food Service Supply Chains. Metallurgical and Materials Engineering, 30(4), 959–975. https://doi.org/10.63278/mme.v30i4.1933

    [23] Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273.

    [24] Reddy, V. A. R., & Kolla, S. K. (1984). Infrastructure-As-Code Practices For Regulated Healthcare Cloud Environments. Metallurgical and Materials Engineering, 30 (4), 1028–1042.

    [25] Gu, S., Kelly, B., & Xiu, D. (2021). Autoencoder asset pricing models. Journal of Econometrics, 222(1), 429–450.

    [26] Härdle, W. K., Wang, W., & Yu, L. (2016). TENET: Tail-event driven network risk. Journal of Econometrics, 192(2), 499–513.

    [27] Yandamuri, U. S. (2024). AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology. Metallurgical and Materials Engineering.

    [28] Heaton, J. B., Polson, N. G., & Witte, J. H. (2017). Deep learning for finance: Deep portfolios. Applied Stochastic Models in Business and Industry, 33(1), 3–12.

    [29] Jiang, W., Liu, M., Xu, M., Chen, S., Shi, K., Liu, P., Zhang, C., & Zhao, F. (2024). New reinforcement learning based on representation transfer for portfolio management. Knowledge-Based Systems, 293, 111697.

    [30] Kelly, B. T., Pruitt, S., & Su, Y. (2019). Characteristics are covariances: A unified model of risk and return. Journal of Financial Economics, 134(3), 501–524.

    [31] Kelly, B. T., Kuznetsov, B., Malamud, S., & Xu, Y. (2023). Deep learning in asset pricing. Management Science, 69, 1–24.

    [32] Vardhan Kumar Bandi, V. D. (2024). Automated Feature Engineering Systems in Large-Scale Healthcare Data Environments. Journal of Neonatal Surgery, 13(1), 2127-2141.

    [33] Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689–702.

    [34] Kumar, A., Kumar, A., Kumari, S., Kumar, S., Kumari, N., & Behura, A. K. (2024). Artificial intelligence: The strategy of financial risk management. Finance: Theory and Practice, 28(3), 174–182.

    [35] Leippold, M., Wang, Q., & Zhou, W. (2022). Machine learning in the Chinese stock market. Journal of Financial Economics, 145(2), 64–82.

    [36] Bandi, V. D. V. K. (2024). AI-Driven Predictive Risk Modeling Architectures for Financial Systems. International Journal Of Finance, 37(3), 54-78.

    [37] Amistapuram, K. (2024). Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning. Available at SSRN.

    [38] Deep Learning-Driven Optimization of ISO 20022 Protocol Stacks for Secure Cross-Border Messaging. (2024). MSW Management Journal, 34(2), 1545-1554.

    [39] Li, Y., Zheng, W., & Zheng, Z. (2019). Deep robust reinforcement learning for practical algorithmic trading. IEEE Access, 7, 108014–108022.

    [40] Kolla, T. (2024). Intelligent Discovery and Governance of Healthcare Data Assets Through AI-Powered Catalog Architectures. International Journal of Emerging Trends in Engineering and Management Research, 9(4), 16083.

    [41] Liu, X.-Y., Yang, H., Gao, J., & Wang, C. D. (2021). FinRL: Deep reinforcement learning framework to automate trading in quantitative finance. Proceedings of the Second ACM International Conference on AI in Finance, 1–9.

    [42] Garapati, R. S. (2022). Web-Centric Cloud Framework for Real-Time Monitoring and Risk Prediction in Clinical Trials Using Machine Learning. Current Research in Public Health, 2, 1346.

    [43] Liu, X.-Y., Xia, Z., Rui, J., Gao, J., Yang, H., Zhu, M., Wang, C. D., Wang, Z., & Guo, J. (2022). FinRL-Meta: Market environments and benchmarks for data-driven financial reinforcement learning. Advances in Neural Information Processing Systems, 35, 1835–1849.

    [44] Reddy Segireddy, A. (2024). Federated Cloud Approaches for Multi-Regional Payment Messaging Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(2), 442-450.

    [45] Lu, Y., Liu, C., Wang, K. I.-K., Huang, H., & Xu, X. (2020). Digital twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 61, 101837.

    [46] Ma, Y., Han, R., & Wang, W. (2021). Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications, 165, 113973.

    [47] Mienye, I. D., & Sun, Y. (2022). A survey of ensemble learning: Concepts, algorithms, applications, and prospects. IEEE Access, 10, 99129–99149.

    [48] Mattaparthi, R. (2024). Transformer-Based Fault Diagnosis for Large-Scale Standby Power Generators: Partial Discharge Pattern Recognition at Hyperscale Data Center Installations. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8781-8799.

    [49] Kolla, T. (2024). Graph Neural Networks for HCC Risk Adjustment and Interoperability. International Journal of Science, Research and Technology, 7(6), 13244-13255.

    [50] Mosavi, A., Faghan, Y., Ghamisi, P., Duan, P., Ardabili, S. F., Salwana, E., & Band, S. S. (2020). Comprehensive review of deep reinforcement learning methods and applications in economics. Mathematics, 8(10), 1640.

    [51] Noriega, J. P., Rivera, L. A., & Herrera, J. A. (2023). Machine learning for credit risk prediction: A systematic literature review. Data, 8(11), 169.

    [52] Inala, R., & Somu, B. (2024). Agentic ai in retail banking: Redefining customer service and financial decision-making. Journal of Artificial Intelligence and Big Data Disciplines, 1(1), 1-19.

    [53] Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 93, 106384.

    [54] Palafox, L. (2022). Introduction to reinforcement learning in finance. Quantitative Finance, 22(3), 447–460.

    [55] Pang, G., Shen, C., Cao, L., & van den Hengel, A. (2021). Deep learning for anomaly detection: A review. ACM Computing Surveys, 54(2), 1–38.

    [56] Supriya, Y., Victor, N., Srivastava, G., & Gadekallu, T. R. (2023, May). A hybrid federated learning model for insurance fraud detection. In 2023 IEEE international conference on communications workshops (ICC workshops) (pp. 1516-1522). IEEE.

    [57] Ryll, L., Barton, M. E., Zhang, B. Z., McWaters, R. J., Schizas, E., Hao, R., Bear, K., Preziuso, M., Seger, E., Wardrop, R., Rau, P. R., & Coombes, R. (2020). Transforming paradigms: A global AI in financial services survey. Cambridge Centre for Alternative Finance.

    [58] Kolla, S. K. (2024). Clinical Knowledge Intelligence through Deep Learning and Natural Language Understanding in Healthcare Platforms. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(3), 14088.

    [59] 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.

    [60] Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied Soft Computing, 90, 106181.

    [61] Mangala, N. (2024). Leveraging Microsoft Fabric lakehouse as an AI-ready data platform for enterprise analytics. Journal of Information Systems Engineering and Management.

    [62] Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A. (2022). Digital twins: State of the art theory and practice, challenges, and open research questions. Journal of Industrial Information Integration, 30, 100383.

    [63] Sirignano, J., & Cont, R. (2019). Universal features of price formation in financial markets: Perspectives from deep learning. Quantitative Finance, 19(9), 1449–1459.

    [64] Tao, F., & Zhang, M. (2017). Digital twin shop-floor: A new shop-floor paradigm towards smart manufacturing. IEEE Access, 5, 20418–20427.

    [65] Kolla, S. K. (2024). Federated Machine Learning On Big Healthcare Data For Privacy-Preserving Analytics. The Review of Diabetic Studies, 175-190.

    [66] Bandi, V. D. V. K. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30(4), 1011-1027.

    [67] Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415.

    [68] van der Aalst, W. M. P., Hinz, O., & Weinhardt, C. (2021). Big digital platforms. Business & Information Systems Engineering, 63, 645–648.

    [69] Nagabhyru, K. C. (2024). Data Engineering in the Age of Large Language Models: Transforming Data Access, Curation, and Enterprise Interpretation. Computer Fraud and Security.

    [70] Virando, G. E., Lee, B., Kee, S.-H., & Yee, J.-J. (2024). Digital twin simulation and implementation in safety risk management process. IEEE Access, 12, 195671–195695.

    [71] Vuletić, M., Prenzel, F., & Cucuringu, M. (2024). Fin-GAN: Forecasting and classifying financial time series via generative adversarial networks. Quantitative Finance, 24(2), 175–199.

    [72] Reddy, V. A. R. (2024). Generative Intelligence for Healthcare Claims Processing and Personalized Benefits Management. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(3), 14099.

    [73] Wang, Y., Su, Z., Guo, S., Dai, M., Luan, T. H., & Liu, Y. (2023). A survey on digital twins: Architecture, enabling technologies, security and privacy, and future prospects. IEEE Internet of Things Journal, 10(17), 14965–14987.

    [74] 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.

    [75] Wiese, M., Knobloch, R., Korn, R., & Kretschmer, P. (2020). Quant GANs: Deep generation of financial time series. Quantitative Finance, 20(9), 1419–1440.

    [76] Loganathan, R. (2024). Generative AI-enabled compliance documentation and audit trail automation for global data center governance. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 487-504.

    [77] Yang, H., Liu, X.-Y., Zhong, S., & Walid, A. (2020). Deep reinforcement learning for automated stock trading: An ensemble strategy. Proceedings of the First ACM International Conference on AI in Finance, 1–8.

    [78] Yang, H., Liu, X.-Y., & Wang, C. D. (2023). FinGPT: Open-source financial large language models. FinLLM Symposium at IJCAI 2023.

    [79] Nagubandi, A. R. (2023). Advanced Multi-Agent AI Systems for Autonomous Reconciliation Across Enterprise Multi-Counterparty Derivatives, Collateral, and Accounting Platforms. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 653-674.

    [80] Yoon, J., Jarrett, D., & van der Schaar, M. (2019). Time-series generative adversarial networks. Advances in Neural Information Processing Systems, 32, 5508–5518.

    [81] Zhang, Z., Zohren, S., & Roberts, S. (2020). Deep learning for portfolio optimization. The Journal of Financial Data Science, 2(4), 8–20.

    [82] Kolla, T. (2024). AI-Powered Data Catalog Systems For Healthcare Data Discovery And Governance. South Eastern European Journal of Public Health, 2296–2311. https://doi.org/10.70135/seejph.vi.7077

    [83] Zhang, Z., Zohren, S., & Roberts, S. (2019). DeepLOB: Deep convolutional neural networks for limit order books. IEEE Transactions on Signal Processing, 67(11), 3001–3012.

    [84] Araci, D. (2019). FinBERT: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063.

    [85] Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.

    [86] Malo, P., Sinha, A., Korhonen, P., Wallenius, J., & Takala, P. (2014). Good debt or bad debt: Detecting semantic orientations in economic texts. Journal of the Association for Information Science and Technology, 65(4), 782–796.

    [87] Yang, Y., UY, M. C. S., & Huang, A. (2020). FinBERT: A pretrained language model for financial communications. arXiv preprint arXiv:2006.08097.

    [88] Davuluri, P. S. L. N. (1936). AI-Driven Data Governance Frameworks for Automated Regulatory Reporting and Audit Readiness. Metallurgical and Materials Engineering, 30 (4), 996–1010.

    [89] Wu, S., Green, A., Head-Gordon, M., & Tang, W. (2023). BloombergGPT: A large language model for finance. arXiv preprint arXiv:2303.17564.

    [90] Lee, J., Stevens, N., Han, S. C., & Song, M. (2024). A survey of large language models in finance (FinLLMs). arXiv preprint arXiv:2402.02315.

    [91] Kolla, S. H. (2024). Retrieval-Augmented Enterprise Intelligence: Enhancing Accuracy, Trust, and Operational Decision-Making. International Journal of Future Innovative Science and Technology (IJFIST), 7(2), 12425.

    [92] Dixon, M., Klabjan, D., & Bang, J. H. (2017). Classification-based financial markets prediction using deep neural networks. Algorithmic Finance, 6(3–4), 67–77.

    [93] Segireddy, A. R. (2024). Machine Learning-Driven Anomaly Detection in CI/CD Pipelines for Financial Applications. Journal of Computational Analysis and Applications, 33(8).

    [94] Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669.

    [95] Guo, T., & Antulov-Fantulin, N. (2018). Predicting short-term Bitcoin price fluctuations from buy and sell orders. The Journal of Mathematical Finance, 8(1), 17–40.

    [96] Sasi Kumar Kolla, & Venkata Akhilesh Ranga Reddy. (2024). Evaluating Cloud-Native vs. Hybrid Architectures for Health Benefit Administration Systems. International Journal of Medical Toxicology and Legal Medicine, 27(5), 1042–1053. Retrieved from https://ijmtlm.org/index.php/journal/article/view/1483

    [97] Ranga Reddy, V. A. (2024). Comparing Batch vs. Streaming Approaches in Healthcare Data Warehousing Environments. Journal of Neonatal Surgery, 13(1), 2287-2309.

    [98] Koshiyama, A., Firoozye, N., & Treleaven, P. (2021). Generative adversarial networks for financial trading strategies fine-tuning and combination. Quantitative Finance, 21(5), 797–813.

    [99] Dixon, M. F., Polson, N. G., & Sokolov, V. O. (2020). Deep learning for spatio-temporal modeling: Dynamic traffic flows and high frequency trading. Applied Stochastic Models in Business and Industry, 36(5), 788–807.

    [100] Ruf, J., & Wang, W. (2020). Neural networks for option pricing and hedging: A literature review. Journal of Computational Finance, 24(1), 1–46.

    [101] Peddi, R. K. (2024). AI-Based Workforce Analytics for SLA Governance and Uptime Assurance in Data Centers. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8589-8601.

    [102] Vamsee Pamisetty, Keerthi Amistapuram. (2024). Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning. Metallurgical and Materials Engineering, 30(4), 976–995. https://doi.org/10.63278/mme.v30i4.1934.

    [103] Aitha, A. R. (2024). Generative AI-Powered Fraud Detection in Workers' Compensation: A DevOps-Based Multi-Cloud Architecture Leveraging, Deep Learning, and Explainable AI. Deep Learning, and Explainable AI (July 26, 2024).

    [104] Cao, L., Zhang, Z., Li, X., & Shi, Y. (2021). Data science thinking: The next scientific, technological and economic revolution. Data Science and Engineering, 6, 1–3.

    [105] Joshi, A., Sujatha, G., Gupta, N., Kumar, R., Sen, M. K., Ish, P., ... & Popalwar, H. (2024). Clinical utility of pulmonary rehabilitation in diffuse parenchymal lung diseases. Journal of Advanced Lung Health, 4(3), 159-165.

    [106] Cao, L. (2023). AI in finance: Challenges, techniques, and opportunities. ACM Computing Surveys, 55(3), Article 64, 1–38.

  • Downloads