Instrument-Level Transfer Pricing Analytics Using Automated Data Pipelines and Financial Calculation Frameworks

  • Authors

    • Bhargavaram Potharaju Systems Operations Engineer, Wells Fargo Charlotte, NC, USA. Author
    • Radha Rani Sajja Asst Professor, Vigan University Guntur, Andhra Pradesh, India. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2021PII7J3Q

    Published 12-07-2021

  • Transfer Pricing Analytics, Instrument-Level Calculations, Treasury Analytics, Balance-Sheet Analytics, Net Interest Margin, Financial Data Modelling, ETL Automation, Batch Processing, Reconciliation, Audit-Ready Analytics

    Issue

    Vol. 4 No. 2 (2021)

    Section

    Articles

    How to Cite

    Potharaju, B., & Sajja, R. R. (2021). Instrument-Level Transfer Pricing Analytics Using Automated Data Pipelines and Financial Calculation Frameworks. International Journal of Commerce, Finance and Digital Economy, 4(2), 01-34. https://doi.org/10.67228/3071642X/IJCFDE-2021PII7J3Q

  • Abstract

    Transfer pricing is an important means by which multinational corporations allocate income, expenses and risks among their global operations. However, traditional manual data collection and spreadsheet calculations are error-prone, take a long time to report and invite more regulatory scrutiny. In this paper we propose an integrated analytical framework to address these vulnerabilities. The framework is focused on instrument-level transfer pricing, enabled by automated enterprise data integration pipelines and standardized calculation models. Legacy systems are based on high-level aggregate data, whereas this architecture automatically ingests and validates transactional data from heterogeneous systems to assess individual financial instruments against parameters such as interest rate structures, liquidity profiles, credit risks and market benchmarks. The modular solution comprises automated computational workflows, interactive visualization dashboards, compliance reporting and scenario-based financial simulations to assess pricing sensitivity in changing market environments. Performance insights indicate that automation of instrument level analytics dramatically decreases the processing lead time and improves pricing consistency, audit trail and regulatory compliance, providing a scalable foundation for financial control at the enterprise level.

  • References

    [1] Cooper, R., & Kaplan, R. S. (1991). The Design of Cost Management Systems: Text, Cases, and Readings. Prentice Hall.

    [2] Elmasri, R., & Navathe, S. B. (2017). Fundamentals of Database Systems (7th ed.). Pearson.

    [3] Golfarelli, M., & Rizzi, S. (2018). Data Warehouse Design: Modern Principles and Methodologies (2nd ed.). McGraw-Hill.

    [4] Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.

    [5] Inmon, W. H. (2005). Building the Data Warehouse (4th ed.). Wiley.

    [6] International Organization for Standardization. (2015). ISO 9001:2015 Quality Management Systems—Requirements. ISO.

    [7] Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley.

    [8] Laudon, K. C., & Laudon, J. P. (2020). Management Information Systems: Managing the Digital Firm (16th ed.). Pearson.

    [9] OECD. (2017). OECD Transfer Pricing Guidelines for Multinational Enterprises and Tax Administrations. OECD Publishing.

    [10] Oracle Corporation. (2020). Oracle Financial Services Analytical Applications User Guide. Oracle Corporation.

    [11] Pressman, R. S., & Maxim, B. R. (2020). Software Engineering: A Practitioner's Approach (9th ed.). McGraw-Hill.

    [12] Silberschatz, A., Korth, H. F., & Sudarshan, S. (2019). Database System Concepts (7th ed.). McGraw-Hill.

    [13] Stair, R., & Reynolds, G. (2019). Principles of Information Systems (13th ed.). Cengage Learning.

    [14] Turban, E., Pollard, C., & Wood, G. (2018). Information Technology for Management (11th ed.). Wiley.

    [15] Wang, S., Wang, H., & Xu, L. (2020). Enterprise financial analytics using automated data integration frameworks. Journal of Information Systems Engineering, 35(4), 215–228.

    [16] Richardson, T., Brown, P., & Lewis, M. (2019). Business intelligence applications for enterprise financial reporting. International Journal of Business Information Systems, 30(2), 167–184.

    [17] Sharma, A., & Gupta, R. (2020). Financial risk assessment using enterprise analytical models. Journal of Financial Analytics, 18(3), 94–108.

    [18] Kim, J., & Park, H. (2019). Automated ETL frameworks for enterprise financial data processing. International Journal of Data Engineering, 11(1), 55–72.

    [19] OECD. (2020). Transfer Pricing Country Profile Series. OECD Publishing.

    [20] Hall, J. A. (2018). Accounting Information Systems (9th ed.). Cengage Learning.

  • Downloads

Similar Articles

21-30 of 96

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