A Conceptual Framework for Data-Driven Treasury Optimization and Operational Efficiency in Multinationals
Abstract
Globalization and digital transformation have reshaped the financial landscape of multinational corporations (MNCs), demanding data-driven strategies for efficient treasury operations. Traditional treasury management models—reliant on static reporting, manual cash forecasting, and fragmented banking systems—struggle to meet modern demands for agility, risk transparency, and real-time decision-making. This review proposes a conceptual framework for data-driven treasury optimization that integrates predictive analytics, artificial intelligence, and centralized data architectures to improve liquidity management, capital allocation, and operational efficiency across subsidiaries. The framework emphasizes three core dimensions: (1) data integration and governance, which ensure consistency and visibility across multi-currency and multi-jurisdictional environments; (2) analytical intelligence, leveraging machine learning for cash forecasting, currency hedging, and working capital optimization; and (3) process automation, deploying robotic process automation (RPA) and API-driven connectivity for straight-through processing and compliance monitoring. Through a systematic synthesis of current treasury digitalization practices and case examples, the study highlights how data-centric transformation reduces financial risk, enhances decision accuracy, and supports sustainable growth in multinational operations. The conceptual framework offers a roadmap for CFOs and treasurers to align digital treasury strategy with enterprise-wide performance optimization, regulatory alignment, and value creation in a volatile global economy.
Keywords
Data-Driven Treasury, Predictive Analytics, Operational Efficiency, Multinational Corporations, Treasury Optimization, Financial Risk Management.
Citations
IRE Journals:
Omolara Adeyoyin, Esther Nkem Awanye, Obiajulu Obiora Morah, Lovelyn Ekpedo "A Conceptual Framework for Data-Driven Treasury Optimization and Operational Efficiency in Multinationals" Iconic Research And Engineering Journals Volume 3 Issue 5 2019 Page 328-344
IEEE:
Omolara Adeyoyin, Esther Nkem Awanye, Obiajulu Obiora Morah, Lovelyn Ekpedo
"A Conceptual Framework for Data-Driven Treasury Optimization and Operational Efficiency in Multinationals" Iconic Research And Engineering Journals, 3(5)
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