A Predictive Analytics Model for Multi-Currency IT Operational Expenditure Management

• Author(s): Olatunde Gaffar ; Ayoola Olamilekan Sikiru ; Mary Otunba ; Adedoyin Adeola Adenuga
• Paper ID: 1709985
• Page: 263-280
• Published Date: 31-03-2019
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 2 Issue 9 March-2019

Abstract

In an increasingly globalized IT landscape, managing operational expenditures (OpEx) across multiple currencies presents significant challenges in accuracy, forecasting, and strategic control. Currency volatility, inconsistent data standards, and fragmented financial systems complicate budgeting and financial planning for multinational organizations. This proposes a predictive analytics model tailored to address the complexities of multi-currency IT OpEx management. The model integrates time-series forecasting, machine learning algorithms, and real-time currency exchange data to deliver accurate, currency-adjusted expenditure predictions that support both tactical and strategic decision-making. The proposed framework incorporates key components such as historical IT spend, dynamic exchange rates, contract terms, and region-specific economic indicators. A hybrid modeling approach, combining ARIMA for temporal trends and gradient boosting (e.g., XGBoost) for feature-based learning, enables high-precision forecasting of IT-related costs across diverse services, including cloud subscriptions, software licensing, infrastructure, and outsourced support. The model normalizes financial data into base currencies for internal planning while allowing for localized currency risk assessments. Additionally, it supports scenario simulations to evaluate cost fluctuations under varying macroeconomic and market conditions. The architecture is designed for seamless integration with enterprise resource planning (ERP) systems and financial dashboards, enabling real-time updates and actionable insights. By linking predictive capabilities with alerting systems, organizations can proactively manage OpEx deviations and currency-driven risks. The model enhances financial visibility, facilitates cross-functional collaboration between IT and finance, and empowers procurement teams with robust, data-driven negotiation tools. Ultimately, this predictive analytics model represents a strategic advancement in financial operations, enabling multinational organizations to optimize IT expenditure planning, reduce budgetary uncertainty, and improve overall financial governance. It supports a transition from reactive to proactive financial management and aligns expenditure forecasting with broader goals of operational efficiency and global fiscal resilience.

Keywords

Predictive analytics model, Multi-Currency, IT operational, Expenditure management

Citations

IRE Journals:
Olatunde Gaffar , Ayoola Olamilekan Sikiru , Mary Otunba , Adedoyin Adeola Adenuga "A Predictive Analytics Model for Multi-Currency IT Operational Expenditure Management" Iconic Research And Engineering Journals Volume 2 Issue 9 2019 Page 263-280

IEEE:
Olatunde Gaffar , Ayoola Olamilekan Sikiru , Mary Otunba , Adedoyin Adeola Adenuga "A Predictive Analytics Model for Multi-Currency IT Operational Expenditure Management" Iconic Research And Engineering Journals, 2(9)