The Role of Central Bank Digital Currencies (CBDCs) in Corporate Financial Strategies and Reporting
Keywords:
CBDCs, Corporate Financial StrategiesAbstract
Central Bank Digital Currencies (CBDCs), digital assets issued and regulated by central banks, are poised to transform the financial landscape, impacting corporate financial strategies and reporting practices. These currencies promise to streamline payment systems, eliminate inefficiencies, and reduce reliance on intermediaries, offering businesses a more efficient & secure way to manage transactions. By enabling real-time settlements and reducing counterparty risks, CBDCs can significantly improve cash flow management and liquidity planning, making financial operations more predictable and transparent. Moreover, the inherent traceability and precision of CBDCs can enhance the accuracy & reliability of corporate financial reporting, ensuring compliance with evolving regulatory requirements. This level of transparency not only strengthens stakeholder trust but also reduces audit complexities by providing immutable records of transactions. However, integrating CBDCs into existing systems requires overcoming several challenges. Organizations must invest in robust cybersecurity measures to safeguard digital assets from emerging threats while simultaneously updating legacy infrastructures to accommodate the unique features of CBDCs. The transition also demands strategic planning to manage potential disruptions in relationships with traditional financial institutions & to address uncertainties around market acceptance and adoption rates. For businesses, adopting CBDCs is not merely about operational efficiency but also about staying competitive in an increasingly digitalized economy. To harness the benefits of this innovation, companies need to adopt a forward-looking approach, investing in technological upgrades, workforce training, and collaboration with financial regulators. The shift to a CBDC-driven environment signals a profound change, demanding agility, strategic foresight, and a willingness to adapt to evolving financial ecosystems. At the same time, the journey involves: Complexities, The potential for streamlined operations, Enhanced compliance, Improved financial transparency positions CBDCs as transformative in corporate financial strategies and reporting. By preparing for this paradigm shift, businesses can mitigate the integration challenges and capitalize on the opportunities presented by this new era of digital finance.
Downloads
References
Ward, O., & Rochemont, S. (2019). Understanding central bank digital currencies (CBDC). Institute and Faculty of Actuaries, 13(2), 263-268.
Wang, Y., Lucey, B. M., Vigne, S. A., & Yarovaya, L. (2022). The effects of central bank digital currencies news on financial markets. Technological Forecasting and Social Change, 180, 121715.
Shkliar, A. I. (2020). The phenomenon of central banks' digital currencies (CBDC): key attributes and implementation perspectives. Ukrainian Society, 1(72), 123-137.
Bindseil, U. (2019). Central bank digital currency: Financial system implications and control. International Journal of Political Economy, 48(4), 303-335.
Fung, B. S. C., & Halaburda, H. (2016). Central bank digital currencies: a framework for assessing why and how (No. 2016-22). Bank of Canada Staff Discussion Paper.
Nabilou, H. (2019). Central bank digital currencies: Preliminary legal observations. Journal of Banking Regulation.
Engert, W., & Fung, B. S. C. (2017). Central bank digital currency: Motivations and implications (No. 2017-16). Bank of Canada Staff Discussion Paper.
Opare, E. A., & Kim, K. (2020). A compendium of practices for central bank digital currencies for multinational financial infrastructures. IEEE Access, 8, 110810-110847.
Yao, Q. (2018). A systematic framework to understand central bank digital currency. Science China Information Sciences, 61, 1-8.
Wong, P., & Maniff, J. L. (2020). Comparing means of payment: what role for a central bank digital currency?.
Ozili, P. K. (2022). Central bank digital currency in Nigeria: opportunities and risks. In The new digital era: digitalisation, emerging risks and opportunities (pp. 125-133). Emerald Publishing Limited.
Priyadarshini, D., & Kar, S. (2021). Central bank digital currency (CBDC): critical issues and the Indian perspective. Institute of Economic Growth Working Paper, 444.
Richards, T., Thompson, C., & Dark, C. (2020). Retail Central Bank Digital Currency: Design Considerations, Rationales and Implications| Bulletin–September 2020.
Barontini, C., & Holden, H. (2019). Proceeding with caution-a survey on central bank digital currency. Proceeding with Caution-A Survey on Central Bank Digital Currency (January 8, 2019). BIS Paper, (101).
Bordo, M. D., & Levin, A. T. (2017). Central bank digital currency and the future of monetary policy (No. w23711). National Bureau of Economic Research.
Thumburu, S. K. R. (2022). EDI and Blockchain in Supply Chain: A Security Analysis. Journal of Innovative Technologies, 5(1).
Thumburu, S. K. R. (2022). A Framework for Seamless EDI Migrations to the Cloud: Best Practices and Challenges. Innovative Engineering Sciences Journal, 2(1).
Gade, K. R. (2022). Data Modeling for the Modern Enterprise: Navigating Complexity and Uncertainty. Innovative Engineering Sciences Journal, 2(1).
Gade, K. R. (2022). Migrations: AWS Cloud Optimization Strategies to Reduce Costs and Improve Performance. MZ Computing Journal, 3(1).
Katari, A., & Vangala, R. Data Privacy and Compliance in Cloud Data Management for Fintech.
Katari, A., Ankam, M., & Shankar, R. Data Versioning and Time Travel In Delta Lake for Financial Services: Use Cases and Implementation.
Komandla, V. Enhancing Product Development through Continuous Feedback Integration “Vineela Komandla”.
Komandla, V. Enhancing Security and Growth: Evaluating Password Vault Solutions for Fintech Companies.
Thumburu, S. K. R. (2021). A Framework for EDI Data Governance in Supply Chain Organizations. Innovative Computer Sciences Journal, 7(1).
Thumburu, S. K. R. (2021). EDI Migration and Legacy System Modernization: A Roadmap. Innovative Engineering Sciences Journal, 1(1).
Boda, V. V. R., & Immaneni, J. (2022). Optimizing CI/CD in Healthcare: Tried and True Techniques. Innovative Computer Sciences Journal, 8(1).
Immaneni, J. (2022). End-to-End MLOps in Financial Services: Resilient Machine Learning with Kubernetes. Journal of Computational Innovation, 2(1).
Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2022). The Shift Towards Distributed Data Architectures in Cloud Environments. Innovative Computer Sciences Journal, 8(1).
Nookala, G. (2022). Improving Business Intelligence through Agile Data Modeling: A Case Study. Journal of Computational Innovation, 2(1).
Nookala, G. (2021). Automated Data Warehouse Optimization Using Machine Learning Algorithms. Journal of Computational Innovation, 1(1).
Muneer Ahmed Salamkar. Scalable Data Architectures: Key Principles for Building Systems That Efficiently Manage Growing Data Volumes and Complexity. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Jan. 2021, pp. 251-70
Muneer Ahmed Salamkar, and Jayaram Immaneni. Automated Data Pipeline Creation: Leveraging ML Algorithms to Design and Optimize Data Pipelines. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, June 2021, pp. 230-5
Muneer Ahmed Salamkar. Data Integration: AI-Driven Approaches to Streamline Data Integration from Various Sources. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, Mar. 2023, pp. 668-94
Muneer Ahmed Salamkar, et al. Data Transformation and Enrichment: Utilizing ML to Automatically Transform and Enrich Data for Better Analytics. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, July 2023, pp. 613-38
Naresh Dulam, et al. “Data Mesh Best Practices: Governance, Domains, and Data Products”. Australian Journal of Machine Learning Research & Applications, vol. 2, no. 1, May 2022, pp. 524-47
Naresh Dulam, et al. “Apache Iceberg 1.0: The Future of Table Formats in Data Lakes”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, Feb. 2022, pp. 519-42
Naresh Dulam, et al. “Kubernetes at the Edge: Enabling AI and Big Data Workloads in Remote Locations”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Oct. 2022, pp. 251-77
Naresh Dulam, et al. “Data Mesh and Data Governance: Finding the Balance”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Dec. 2022, pp. 226-50
Sarbaree Mishra, and Jeevan Manda. “Building a Scalable Enterprise Scale Data Mesh With Apache Snowflake and Iceberg”. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, June 2023, pp. 695-16
Sarbaree Mishra. “Scaling Rule Based Anomaly and Fraud Detection and Business Process Monitoring through Apache Flink”. Australian Journal of Machine Learning Research & Applications, vol. 3, no. 1, Mar. 2023, pp. 677-98
Sarbaree Mishra. “A Reinforcement Learning Approach for Training Complex Decision Making Models”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, July 2022, pp. 329-52
Sarbaree Mishra, et al. “Leveraging in-Memory Computing for Speeding up Apache Spark and Hadoop Distributed Data Processing”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Sept. 2022, pp. 304-28
Babulal Shaik. Developing Predictive Autoscaling Algorithms for Variable Traffic Patterns . Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, July 2021, pp. 71-90
Babulal Shaik, et al. Automating Zero-Downtime Deployments in Kubernetes on Amazon EKS . Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Oct. 2021, pp. 355-77
