Predictive Financial Modeling for Strategic Technology Investments and Regulatory Compliance in Multinational Financial Institutions

• Author(s): Tewogbade Lateefat ; Folake Ajoke Bankole
• Paper ID: 1709861
• Page: 423-442
• Published Date: 31-05-2020
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 11 May-2020

Abstract

This study explores the development and application of predictive financial modeling to support strategic technology investments and ensure regulatory compliance in multinational financial institutions. As these institutions face increasing pressure to modernize operations while navigating complex global regulatory landscapes, leveraging advanced analytics and machine learning has become essential for informed financial decision-making. The research investigates the integration of predictive financial models that utilize historical financial data, macroeconomic indicators, and compliance metrics to forecast the financial implications of technology adoption, ranging from digital banking infrastructure to cybersecurity systems. By combining scenario analysis with real-time data inputs, the proposed modeling framework enhances capital allocation strategies and mitigates regulatory risks. This approach provides decision-makers with actionable insights into investment timing, return on investment (ROI), and potential compliance gaps across jurisdictions. The study incorporates case studies from leading multinational banks to validate model effectiveness, highlighting how predictive analytics can improve budget accuracy, optimize cost-benefit analysis, and align technological innovation with cross-border compliance requirements such as Basel III, GDPR, and Dodd-Frank. Key findings reveal that institutions adopting predictive modeling frameworks exhibit improved agility in regulatory reporting, reduced non-compliance penalties, and accelerated digital transformation. The model also supports proactive engagement with regulators by enabling scenario-based simulations and automated documentation of audit trails. Furthermore, the research underscores the importance of cross-functional collaboration between finance, compliance, and IT departments in implementing successful modeling practices. In conclusion, this paper presents a robust framework for predictive financial modeling that empowers multinational financial institutions to make strategic, data-driven technology investments while maintaining regulatory integrity. The study advocates for embedding such models into enterprise risk management systems to foster sustainable growth and competitive advantage in an increasingly digital and regulated financial environment.

Keywords

Predictive Financial Modeling, Strategic Technology Investments, Regulatory Compliance, Multinational Financial Institutions, Machine Learning, Risk Management, ROI, Scenario Analysis, Regulatory Frameworks, Digital Transformation, Capital Allocation, Data-Driven Decision-Making, Basel III, GDPR, Dodd-Frank, audit trails.

Citations

IRE Journals:
Tewogbade Lateefat , Folake Ajoke Bankole "Predictive Financial Modeling for Strategic Technology Investments and Regulatory Compliance in Multinational Financial Institutions" Iconic Research And Engineering Journals Volume 3 Issue 11 2020 Page 423-442

IEEE:
Tewogbade Lateefat , Folake Ajoke Bankole "Predictive Financial Modeling for Strategic Technology Investments and Regulatory Compliance in Multinational Financial Institutions" Iconic Research And Engineering Journals, 3(11)