Deep Learning-Driven Financial Integration Framework for Real-Time Collateral Optimization and Regulatory Compliance Governance

  • Authors

    • Ghatoth Mishra Independent Researcher, USA. Author
    • Sophia Muller Junior Research Assistant, Rhineburg Institute of Applied Sciences, Germany. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2024PII2N3A

    Published 08-07-2024

  • Collateral Liquidity Optimization, Real-Time Financial Analytics, Deep Learning, Financial Models, Regulatory Governance Systems, Collateral Management Frameworks, Liquidity Risk Optimization, Financial Market Integration, Real-Time Monitoring Systems, Compliance And Traceability, Intelligent Liquidity Management

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    Mishra, G. ., & Muller, S. (2024). Deep Learning-Driven Financial Integration Framework for Real-Time Collateral Optimization and Regulatory Compliance Governance. International Journal of Commerce, Finance and Digital Economy, 7(2), 01-17. https://doi.org/10.67228/3071642X/IJCFDE-2024PII2N3A

  • Abstract

    The analytical foundations for an integrated approach to boosting the efficiency of collateral liquidity and creating the necessary transparency for regulatory governance in the global financial system are presented. The proposed approach combines real-time optimization of collateral allocation and use with a new generation of deep-learning models. These models integrate the activities of the main actors directly or indirectly involved in the collateral management process, creating the necessary interconnections for depositing and borrowing collateral in the form of cash or securities and integrating with the liquidity in the foreign-exchange-market system. The quantity of any eligible asset, such as the collateral of any entity, is not a given assumption, and the management of any eligible asset may be affected by factors considered unmanageable in standard approaches, such as haircuts, settlement cycles, and monitoring speed and cost. Integration of trading activity in any market or portfolio strategy, including funding and hedging, generates an additional margin that decreases the funding cost of liquidity, increasing the collateral deposit capacity of any eligible entity or creating a surplus for new non-collateralized margin-taking activities. The integrated system allows real-time monitoring of changeable processes. Consequently, the approach describes how to align these aspects of integration with the regulatory necessities of the financial system. Governance aspects of the alignment are also studied in-depth because deploying a new generation of models on a larger scale ends up increasing the demand for solutions that can guarantee reliability to the involved users and/or can provide adequate traceability of the models when used by users requiring an additional level of compliance.

  • References

    [1] Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2), 564–608. [https://doi.org/10.1257/aer.20130456](https://doi.org/10.1257/aer.20130456)

    [2] Acharya, V. V., Pedersen, L. H., Philippon, T., & Richardson, M. (2017). Measuring systemic risk. The Review of Financial Studies, 30(1), 2–47. [https://doi.org/10.1093/rfs/hhw088](https://doi.org/10.1093/rfs/hhw088)

    [3] Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative AI. Business & Information Systems Engineering, 66(1), 111–126. [https://doi.org/10.1007/s12599-023-00834-7](https://doi.org/10.1007/s12599-023-00834-7)

    [4] Kalisetty, S. (2023). Big Data–Driven Cloud Collaboration Models for Enhancing Supplier–Retailer Synchronization in Mod-ern Manufacturing Supply Chains. Journal of Computational Analy-sis and Applications (JoCAAA), 31(4), 2188-2205.

    [5] Ebert, C., & Louridas, P. (2023). Generative AI for software practitioners. IEEE Software, 40(4), 30–38. [https://doi.org/10.1109/MS.2023.3265877](https://doi.org/10.1109/MS.2023.3265877)

    [6] Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. [https://doi.org/10.1126/science.adh2586](https://doi.org/10.1126/science.adh2586)

    [7] Pandugula, C., & Nampalli, R. C. R. Optimizing Retail Performance: Cloud-Enabled Big Data Strategies for Enhanced Consumer Insights.

    [8] Doshi, A. R., & Hauser, O. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28), eadn5290. [https://doi.org/10.1126/sciadv.adn5290](https://doi.org/10.1126/sciadv.adn5290)

    [9] van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023). ChatGPT: Five priorities for research. Nature, 614(7947), 224–226. [https://doi.org/10.1038/d41586-023-00288-7](https://doi.org/10.1038/d41586-023-00288-7)

    [10] AZITH, T. G. V. (2023). Exploring the intersection of bioethics and AI-driven clinical decision-making: Navigating the ethical challenges of deep learning applications in personalized medicine and experimental treatments. JOURNAL OF MATERIAL SCIENCES & MANUFACTURING RESEARCH Учредители: Scientific Research and Community Ltd, 4(6), 1-10.

    [11] Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018). Generative adversarial networks: An overview. IEEE Signal Processing Magazine, 35(1), 53–65. [https://doi.org/10.1109/MSP.2017.2765202](https://doi.org/10.1109/MSP.2017.2765202)

    [12] Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2023). A review on generative adversarial networks: Algorithms, theory, and applications. IEEE Transactions on Knowledge and Data Engineering, 35(4), 3313–3332. [https://doi.org/10.1109/TKDE.2021.3130191](https://doi.org/10.1109/TKDE.2021.3130191)

    [13] Pandugula, C., & Yasmeen, Z. (2023). Exploring Advanced Cybersecurity Mechanisms for Attack Prevention in Cloud-Based Retail Ecosystems. Journal for ReAttach Therapy and Developmental Diversities, 6, 1704-1714.

    [14] Jennings, N. R., Sycara, K., & Wooldridge, M. (1998). A roadmap of agent research and development. Autonomous Agents and Multi-Agent Systems, 1(1), 7–38. [https://doi.org/10.1023/A:1010090405266](https://doi.org/10.1023/A:1010090405266)

    [15] Shoham, Y. (1993). Agent-oriented programming. Artificial Intelligence, 60(1), 51–92. [https://doi.org/10.1016/0004-3702(93)90034-9](https://doi.org/10.1016/0004-3702%2893%2990034-9)

    [16] Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162. [https://doi.org/10.1057/jos.2010.3](https://doi.org/10.1057/jos.2010.3)

    [17] Vankayalapati, R. K., & Seenu, A. (2023). Energy-efficient ai clusters: Reducing carbon footprints with cloud and high-speed storage synergies. Available at SSRN 5091827.

    [18] Wooldridge, M., & Ciancarini, P. (2001). Agent-oriented software engineering: The state of the art. In P. Ciancarini & M. Wooldridge (Eds.), Agent-oriented software engineering (pp. 1–28). Springer. [https://doi.org/10.1007/3-540-44564-1_1](https://doi.org/10.1007/3-540-44564-1_1)

    [19] Reddy, R., Yasmeen, Z., Maguluri, K. K., & Ganesh, P. (2023). Impact of AI-Powered Health Insurance Discounts and Wellness Programs on Member Engagement and Retention. Letters in High Energy Physics, 2023.

    [20] Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the fifth utility. Future Generation Computer Systems, 25(6), 599–616. [https://doi.org/10.1016/j.future.2008.12.001](https://doi.org/10.1016/j.future.2008.12.001)

    [21] Pamisetty, V. (2023). Leveraging AI, big data, and cloud computing for enhanced tax compliance, fraud detection, and fiscal impact analysis in government financial management. Fraud Detection, and Fiscal Impact Analysis in Government Financial Management (December 15, 2023).

    [22] Burns, B., Grant, B., Oppenheimer, D., Brewer, E., & Wilkes, J. (2016). Borg, Omega, and Kubernetes. Communications of the ACM, 59(5), 50–57. [https://doi.org/10.1145/2890784](https://doi.org/10.1145/2890784)

    [23] Recharla, M., Nuka, S. T., Chakilam, C., Chava, K., & Suura, S. R. (2023). Next-generation technologies for early disease detection and treatment: harnessing intelligent systems and genetic innovations for improved patient outcomes. Journal for ReAttach Therapy and Developmental Diversities, 6, 1921-1937.

    [24] Dragoni, N., Giallorenzo, S., Lluch Lafuente, A., Mazzara, M., Montesi, F., Mustafin, R., & Safina, L. (2017). Microservices: Yesterday, today, and tomorrow. In M. Mazzara & B. Meyer (Eds.), Present and ulterior software engineering (pp. 195–216). Springer. [https://doi.org/10.1007/978-3-319-67425-4_12](https://doi.org/10.1007/978-3-319-67425-4_12)

    [25] Aravind, R., & Shah, C. V. (2023). Physics model-based design for predictive maintenance in autonomous vehicles using AI. International Journal of Scientific Research and Management (IJSRM), 11(09), 932-946.

    [26] Challa, K. (2023). Optimizing Financial Forecasting Using Cloud Based Machine Learning Models. Journal for ReAttach Therapy and Developmental Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2), 3565.

    [27] Di Francesco, P., Malavolta, I., & Lago, P. (2017). Research on architecting microservices: Trends, focus, and potential for industrial adoption. In 2017 IEEE International Conference on Software Architecture (ICSA) (pp. 21–30). IEEE. [https://doi.org/10.1109/ICSA.2017.24](https://doi.org/10.1109/ICSA.2017.24)

    [28] Varghese, B., & Buyya, R. (2018). Next generation cloud computing: New trends and research directions. Future Generation Computer Systems, 79, 849–861. [https://doi.org/10.1016/j.future.2017.09.020](https://doi.org/10.1016/j.future.2017.09.020)

    [29] Pamisetty, A. (2023). Cloud-driven transformation of banking supply chain analytics using big data frameworks. Available at SSRN.

    [30] Lwakatare, L. E., Kilamo, T., Karvonen, T., Sauvola, T., Heikkilä, V., Itkonen, J., Kuvaja, P., Mikkonen, T., Oivo, M., & Lassenius, C. (2019). DevOps in practice: A multiple case study of five companies. Information and Software Technology, 114, 217–230. [https://doi.org/10.1016/j.infsof.2019.06.010](https://doi.org/10.1016/j.infsof.2019.06.010)

    [31] Komaragiri, V. B. (2023). Leveraging Artificial Intelligence to Improve Quality of Service in Next-Generation Broadband Networks. Journal for ReAttach Therapy and Developmental Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2), 3571.

    [32] Shahin, M., Babar, M. A., & Zhu, L. (2017). Continuous integration, delivery and deployment: A systematic review on approaches, tools, challenges and practices. IEEE Access, 5, 3909–3943. [https://doi.org/10.1109/ACCESS.2017.2685629](https://doi.org/10.1109/ACCESS.2017.2685629)

    [33] Chen, L. (2015). Continuous delivery: Huge benefits, but challenges too. IEEE Software, 32(2), 50–54. [https://doi.org/10.1109/MS.2015.27](https://doi.org/10.1109/MS.2015.27)

    [34] Kannan, S. (2023). The Convergence of AI, Machine Learning, and Neural Networks in Precision Agriculture: Generative AI as a Catalyst for Future Food Systems. JOURNAL FOR REATTACH THERAPY AND DEVELOPMENTAL DIVERSITIES.

    [35] Leppänen, M., Mäkinen, S., Pagels, M., Eloranta, V.-P., Itkonen, J., Mäntylä, M. V., & Männistö, T. (2015). The highways and country roads to continuous deployment. IEEE Software, 32(2), 64–72. [https://doi.org/10.1109/MS.2015.50](https://doi.org/10.1109/MS.2015.50)

    [36] Suura, S. R., Chava, K., Recharla, M., & Chakilam, C. (2023). Evaluating Drug Efficacy and Patient Outcomes in Personalized Medicine: The Role of AI-Enhanced Neuroimaging and Digital Transformation in Biopharmaceutical Services. Journal for ReAttach Therapy and Developmental Diversities, 6, 1892-1904.

    [37] Mäntylä, M. V., Adams, B., Khomh, F., Engström, E., & Petersen, K. (2015). On rapid releases and software testing: A case study and a semi-systematic literature review. Empirical Software Engineering, 20(5), 1384–1425. [https://doi.org/10.1007/s10664-014-9338-4](https://doi.org/10.1007/s10664-014-9338-4)

    [38] Annapareddy, V. N., Gadi, A. L., Komaragiri, V. B., Koppolu, H. K. R., & Kannan, S. (2023). AI-Driven Optimization of Renewable Energy Systems: Enhancing Grid Efficiency and Smart Mobility Through 5G and 6G Network Integration. Educational Administration: Theory and Practice, 29(4), 4748-4763.

    [39] Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., & Zimmermann, T. (2019). Software engineering for machine learning: A case study. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (pp. 291–300). IEEE. [https://doi.org/10.1109/ICSE-SEIP.2019.00042](https://doi.org/10.1109/ICSE-SEIP.2019.00042)

    [40] Inala, R. (2023). AI-powered investment decision support systems: Building smart data products with embedded governance controls. Journal for ReAttach Therapy and Developmental Diversities, 6(10), 2251-2266.

    [41] Baylor, D., Breck, E., Cheng, H.-T., Fiedel, N., Foo, C. Y., Haque, Z., Haykal, S., Ispir, M., Jain, V., Koc, L., Koo, C. Y., Lew, L., Mewald, C., Modi, A. N., Polyzotis, N., Ramesh, S., Roy, S., Whang, S. E., Wicke, M., … Zinkevich, M. (2017). TFX: A TensorFlow-based production-scale machine learning platform. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1387–1395). ACM. [https://doi.org/10.1145/3097983.3098021](https://doi.org/10.1145/3097983.3098021)

    [42] Sheelam, G. K. (2023). Adaptive AI workflows for edge-to-cloud processing in decentralized mobile infrastructure. Journal for Reattach Therapy and Development Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2). 3570ugh Predictive Intelligence.

    [43] Aravind, R., & Surabhii, S. N. R. D. (2023). Harnessing Artificial Intelligence for Enhanced Vehicle Control and Diagnostics.

    [44] Kummari, D. N. (2023). Energy Consumption Optimization in Smart Factories Using AI-Based Analytics: Evidence from Automotive Plants. Journal for Reattach Therapy and Development Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2), 3572.

    [45] Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the FinTech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of Management Information Systems, 35(1), 220–265. [https://doi.org/10.1080/07421222.2018.1440766](https://doi.org/10.1080/07421222.2018.1440766)

    [46] Lakkarasu, P., Kaulwar, P. K., Dodda, A., Singireddy, S., & Burugulla, J. K. R. (2023). Innovative computational frameworks for secure financial ecosystems: Integrating intelligent automation, risk analytics, and digital infrastructure. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 334-371.

    [47] Puschmann, T. (2017). FinTech. Business & Information Systems Engineering, 59(1), 69–76. [https://doi.org/10.1007/s12599-017-0464-6](https://doi.org/10.1007/s12599-017-0464-6)

    [48] Kalisetty, S., & Singireddy, J. (2023). Agentic AI in retail: A paradigm shift in autonomous customer interaction and supply chain automation. American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN, 3067-4190.

    [49] Vives, X. (2019). Digital disruption in banking. Annual Review of Financial Economics, 11, 243–272. [https://doi.org/10.1146/annurev-financial-100719-120854](https://doi.org/10.1146/annurev-financial-100719-120854)

    [50] Kaulwar, P. K. (2023). Tax Optimization and Compliance in Global Business Operations: Analyzing the Challenges and Opportunities of International Taxation Policies and Transfer Pricing. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 150-181.

    [51] Aravind, R., Surabhi, S. N. D., & Shah, C. V. (2023). Remote Vehicle Access: Leveraging Cloud Infrastructure for Secure and Efficient OTA Updates with Advanced AI. EuropeanEconomic Letters (EEL), 13 (4), 1308–1319. Retrieved fromhttps.

    [52] Paleti, S. (2023). Transforming Money Transfers and Financial Inclusion: The Impact of AI-Powered Risk Mitigation and Deep Learning-Based Fraud Prevention in Cross-Border Transactions. Available at SSRN, 5158588.

    [53] Bartlett, R., Morse, A., Stanton, R., & Wallace, N. (2022). Consumer-lending discrimination in the FinTech era. Journal of Financial Economics, 143(1), 30–56. [https://doi.org/10.1016/j.jfineco.2021.05.047](https://doi.org/10.1016/j.jfineco.2021.05.047)

    [54] Adusupalli, B. (2023). DevOps-Enabled Tax Intelligence: A Scalable Architecture for Real-Time Compliance in Insurance Advisory. Journal for Reattach Therapy and Development Diversities. Green Publication. https://doi. org/10.53555/jrtdd. v6i10s (2), 358.

    [55] 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. [https://doi.org/10.1016/j.asoc.2020.106181](https://doi.org/10.1016/j.asoc.2020.106181)

    [56] Nandan, B. P., & Chitta, S. S. (2023). Machine Learning Driven Metrology and Defect Detection in Extreme Ultraviolet (EUV) Lithography: A Paradigm Shift in Semiconductor Manufacturing. Educational Administration: Theory and Practice, 29 (4), 4555–4568. International Journal of Scientific Research and Modern Technology, 1(12), 216-226.

    [57] Kalisetty, S., & Singireddy, J. (2023). Optimizing Tax Preparation and Filing Services: A Comparative Study of Traditional Methods and AI Augmented Tax Compliance Frameworks. Available at SSRN 5206185.

    [58] Recharla, M. Integrated Genomic and Neurobiological Pathway Mapping for Early Detection of Alzheimer’s Disease. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI, 10.

    [59] Pandiri, L., & Chitta, S. (2022). Leveraging AI and Big Data for Real-Time Risk Profiling and Claims Processing: A Case Study on Usage-Based Auto Insurance. Kurdish Studies. Green Publication. https://doi. org/10.53555/ks. v10i2, 3760.

    [60] Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18. [https://doi.org/10.1007/s13174-010-0007-6](https://doi.org/10.1007/s13174-010-0007-6)

    [61] Mashetty, S. (2023). View of A Comparative Analysis of Patented Technologies Supporting Mortgage and Housing Finance. Educational Administration: Theory and Practice.

    [62] Kalisetty, S. (2023). Harnessing Big Data and Deep Learning for Real-Time Demand Forecasting in Retail: A Scalable AI-Driven Approach. American Online Journal of Science and Engineering (AOJSE)(ISSN: 3067-1140), 1(1).

    [63] Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of Finance, 29(2), 449–470. [https://doi.org/10.1111/j.1540-6261.1974.tb03058.x](https://doi.org/10.1111/j.1540-6261.1974.tb03058.x)

    [64] Rampini, A. A., & Viswanathan, S. (2010). Collateral, risk management, and the distribution of debt capacity. The Journal of Finance, 65(6), 2293–2322. [https://doi.org/10.1111/j.1540-6261.2010.01616.x](https://doi.org/10.1111/j.1540-6261.2010.01616.x)

    [65] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). 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). Association for Computing Machinery. [https://doi.org/10.1145/2939672.2939778](https://doi.org/10.1145/2939672.2939778)

    [66] Rudin, C. (2019). Stop explaining black box machine learning models for high-stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. [https://doi.org/10.1038/s42256-019-0048-x](https://doi.org/10.1038/s42256-019-0048-x)

    [67] Sirignano, J., & Cont, R. (2019). Universal features of price formation in financial markets: Perspectives from deep learning. Quantitative Finance, 19(9), 1449–1459. [https://doi.org/10.1080/14697688.2019.1622295](https://doi.org/10.1080/14697688.2019.1622295)

  • Downloads

Similar Articles

21-30 of 99

You may also start an advanced similarity search for this article.