From Financial Modelling To Financial Decision Intelligence: The Emerging Role Of Artificial Intelligence

Leveraging on Financial Intelligence for Effective Strategic Decision-Making in the Digital Economy

This study investigates the influence of financial intelligence on strategic decision-making effectiveness in the digital economy. Adopting a quantitative research approach and a cross-sectional survey design, data were collected from 164 respondents drawn from managerial, financial, and strategic roles across diverse industries. The study operationalised financial intelligence into dimensions: financial data analytics, financial reporting quality, financial technology adoption, financial risk intelligence, and financial forecasting capability. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), the findings revealed that al dimensions significantly and positively affect strategic decision-making effectiveness, with FinTech adoption emerging as the strongest predictor. The model explained 62% of the variance in strategic decision-making effectiveness, confirming substantial explanatory power. The results validate the Resource-Based View and Dynamic Capabilities Theory, demonstrating that financial intelligence constitutes both a strategic resource and an adaptive capability essential for organisational competitiveness. The study concludes that financial intelligence is not merely an operational function but a strategic asset that enhances agility, resilience, and innovation in digitally transformed environments. The implications highlight the need for managers to invest in digital financial systems, analytics, and forecasting tools, while policymakers should support enabling infrastructures. Ultimately, financial intelligence is indispensable for organisations seeking sustainable growth and competitive advantage in the digital economy.

Artificial Intelligence and the Future of Business Decision-Making: A Comprehensive Review

The study concludes that AI is not replacing managerial judgment but augmenting human decision-making through intelligent data-driven insights, and organizations that strategically embrace responsible AI adoption while investing in digital capabilities and ethical governance are likely to achieve sustainable competitive advantage.

The Role of Business Analytics in Financial Decision-Making: A Review

A Conceptual Framework for Economic Intelligence, Market Research, and Policy Decision-Making in Financial Services

This paper explores the conceptual framework that integrates economic intelligence, market research, and policy decision-making within the context of financial services. It highlights the critical role of economic intelligence in informing policy decisions by analyzing macroeconomic indicators, financial reports, and geopolitical risks. The study emphasizes the importance of market research in understanding consumer behavior, sector dynamics, and the demand for financial products and services, offering valuable insights for regulatory bodies and financial institutions. With advancements in technology, particularly predictive analytics and artificial intelligence, the paper outlines how these tools are reshaping financial market research, enabling policymakers and financial services firms to anticipate trends, manage risks, and optimize decision-making. Regulatory frameworks and compliance requirements, coupled with the evolving role of central banks and financial stability measures, are examined in light of their influence on financial policy. The paper concludes with a discussion of the limitations of current methodologies and presents future research opportunities, particularly in the areas of machine learning, real-time data analytics, and the integration of behavioral economics into policy design. This work contributes to understanding the interconnected nature of economic intelligence, market research, and financial policy, offering insights for scholars, policymakers, and financial practitioners

Artificial Intelligence in Supporting Strategic Decision-Making and Its Impact on Organizational Performance

AI capability is a new strategic capability in the organization that goes beyond operational efficiency and can support the quality strategic decision-making, sustainable performance of an organization, and high decision quality. Though AI capability is evolving, current research remains disparate in how to transform an AI capability to a organizational value with the role of governance, leadership, and organizations capability. To solve this, in this study, a integrated conceptual framework grounded in the theory of resource-based view(RBV), dynamic capabilities theory(DCT) and the AI Governance literature is developed and empirically tested. In the model, the sequential relation between AI capability, AI governance, strategic decision quality, organizational agility, and organizational performance was proposed and the moderating role of digital leadership was examined. An explanatory sequential mixed-methods research design was used. The empirical analysis includes two phases. In the first phase, a cross-sectional survey of 446 senior executives and strategic decision makers of public and private organizations was conducted to empirically test the proposed integrated model using Partial Least Squares Structural Equation Modeling (PLS-SEM). In the second phase, qualitative data from 30 semi-structured interviews with senior executives was collected to gain a deep understanding of AI governance, digital leadership and organizational agility practices. Multi-group analysis further revealed differences in the proposed relationships for public and private organizations. Findings revealed that AI capability not only significantly strengthens the AI governance, and consequently the strategic decision quality, but it also improve the organizational agility, resulting in improved performance. Furthermore, digital leadership has a positive effect on reinforcing the association between AI governance and the strategic decision quality. Overall, this study integrates the technology capability, the organizational capability and the leadership capability to establish an AI-enabled strategic decision-making and performance management framework, and provides strategic insights for organizations that aim to realize greater value from their AI investments.

Generative Artificial Intelligence in Business Decision-Making: Emerging Frameworks, Applications, and Future Challenges

It is concluded that no single framework adequately explains GenAI's enterprise decision-making effects across all analytical levels, and that individual-, organizational-, and task-level frameworks must be combined rather than treated as competing explanations, identifying multi-level theoretical integration as the central future research prospect.