AI is recasting financial modelling from a periodic forecasting exercise into a continuous organizational capability: Financial Decision Intelligence blends data, analytics, governance and human–AI collaboration to strengthen strategic financial decision-making, but the framework remains conceptual and awaits empirical validation.

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

Iman Sjamsu Rahardjo, Rida Justin Jacobalis, Idha Adhani, Bintoro Ariyanto, Oktovina Deci Rahakbauw · July 25, 2026 · Jurnal Minfo Polgan

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Iman Sjamsu Rahardjo provider ID
Rida Justin Jacobalis provider ID
Idha Adhani provider ID
Bintoro Ariyanto provider ID
Oktovina Deci Rahakbauw provider ID

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Latest observation: August 25, 2026

Iman Sjamsu Rahardjo provider ID
Rida Justin Jacobalis provider ID
Idha Adhani provider ID
Bintoro Ariyanto provider ID
O. Rahakbauw provider ID

The paper reconceptualizes financial modelling as a dynamic organizational capability—Financial Decision Intelligence—showing how AI, data, governance, and human-AI interaction turn financial decision-making into a continuous process of strategic intelligence creation.

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Through the introduction of Financial Decision Intelligence, the study provides an integrative theoretical perspective explaining how Artificial Intelligence transforms financial decision-making into a continuous process of strategic intelligence creation. In doing so, it offers a conceptual foundation for future empirical research and advances the understanding of how organizations can leverage AI to strengthen strategic decision capability in the digital economy. Using an Integrative Literature Review (ILR), the study synthesizes multidisciplinary literature spanning strategic management, finance, business analytics, information systems, and artificial intelligence to develop an integrated theoretical framework. This study contributes to the strategic management literature by reconceptualizing financial modelling as a dynamic organizational capability, integrating previously fragmented theoretical perspectives, and establishing a conceptual foundation for future empirical research on AI-enabled financial decision capability. Beyond its theoretical contribution, the proposed framework also offers practical guidance for organizations seeking to strengthen evidence-based financial governance, strategic adaptability, and sustainable competitive advantage in increasingly data-intensive business environments.

Summary

Main Finding

The paper introduces "Financial Decision Intelligence" (FDI) as an integrative theoretical perspective showing how AI transforms financial decision-making from discrete tasks into a continuous process of strategic intelligence creation. It reconceptualizes financial modelling as a dynamic organizational capability and provides a conceptual foundation for future empirical work on AI-enabled financial decision capability.

Key Points

Financial Decision Intelligence (FDI): AI-enabled orientation that turns financial decision-making into ongoing strategic intelligence generation rather than isolated analyses.
Reconceptualization: Financial modelling is framed as a dynamic organizational capability (not just technical models), dependent on people, processes, data, and governance.
Multidisciplinary integration: Synthesizes literature from strategic management, finance, business analytics, information systems, and AI to resolve fragmented perspectives.
Theoretical contribution: Provides an integrated framework linking AI technologies to enhanced strategic decision capability and organizational outcomes.
Practical guidance: Framework highlights ways organizations can leverage AI to improve evidence-based financial governance, strategic adaptability, and sustainable competitive advantage in data-intensive environments.
Research agenda: Establishes constructs and relationships for empirical testing of how AI adoption in finance affects firm behavior and performance.

Data & Methods

Methodology: Integrative Literature Review (ILR).
Scope: Multidisciplinary synthesis across strategic management, finance, business analytics, information systems, and AI literatures.
Outputs: Conceptual framework and propositions (no primary empirical data or quantitative tests).
Limitations inherent to method: theory-driven synthesis rather than causal or statistical inference; empirical validation and operationalization of constructs remain outstanding.

Implications for AI Economics

Firm productivity & resource allocation: FDI implies AI can change the timing, precision, and scope of financial decisions—potentially improving capital allocation efficiency, investment timing, and risk management.
Competitive dynamics: Treating financial modelling as a dynamic capability suggests persistent advantages for firms that embed AI into routines, data governance, and decision processes; heterogeneity in capability adoption may drive persistent performance dispersion.
Measurement and valuation of AI: Economics research should develop metrics to operationalize FDI capability (e.g., data quality indices, model deployment frequency, decision cycle time, governance maturity) to quantify AI’s contribution to economic outcomes.
Empirical strategies suggested:
- Construct firm-level panel measures of FDI adoption (tools, processes, governance) and link to productivity, investment, and market outcomes.
- Use event studies or difference-in-differences on AI deployments or governance changes to identify causal effects.
- Leverage natural experiments (e.g., regulatory changes, data access shocks) and randomized evaluations where feasible to isolate effects.
Policy and regulation: Findings imply regulators should consider how AI-enabled financial decision capabilities affect systemic risk, transparency, and market fairness; governance standards and data-access policies can shape diffusion and welfare effects.
Future research opportunities:
- Operationalize and validate the FDI constructs empirically.
- Map heterogeneity in returns to FDI across industries and firm sizes.
- Study complementarities between AI, human expertise, and organizational routines.
- Evaluate macroeconomic implications of widespread FDI adoption (e.g., aggregate investment dynamics, labor reallocation in finance).

Overall, the paper offers a structured conceptual platform for AI economics researchers to design empirical studies that quantify how AI-enabled financial decision capabilities alter firm behavior, market outcomes, and welfare.

Assessment

Paper Typereview_meta Evidence Strengthn/a — The paper is conceptual and based on an integrative literature review rather than primary empirical analysis, so it does not provide causal or statistical evidence to support its claims. Methods Rigormedium — The study synthesizes multidisciplinary literatures and proposes a coherent theoretical framework, which indicates intellectual rigor; however, no details are provided about systematic search protocols, inclusion/exclusion criteria, or formal quality appraisal of sources, and the framework is not empirically validated, limiting methodological robustness. SampleNo primary data; the study draws on published literature across strategic management, finance, business analytics, information systems, and artificial intelligence to synthesize concepts and build an integrated theoretical framework. Themesorg_design human_ai_collab governance innovation adoption GeneralizabilityConceptual framework not empirically validated — applicability to real firms or sectors is untested, Rapid evolution of AI tools may outpace elements of the framework, Organizational heterogeneity (size, resources, data maturity) may limit applicability, Regulatory and institutional differences across countries may affect transferability, Sector-specific financial processes (e.g., banking vs. manufacturing) may require adaptation

Claims (6)

Claim	Direction	Outcome	Confidence & Evidence	Details
The study introduces "Financial Decision Intelligence" and provides an integrative theoretical perspective explaining how Artificial Intelligence transforms financial decision-making into a continuous process of strategic intelligence creation. Decision Quality	positive	transformation of financial decision-making into continuous strategic intelligence creation	Reading fidelity high Study strength speculative	not reported 0.04
The study offers a conceptual foundation for future empirical research on AI-enabled financial decision capability. Research Productivity	positive	availability of a conceptual foundation for future research	Reading fidelity high Study strength low	not reported 0.12
Using an Integrative Literature Review (ILR), the study synthesizes multidisciplinary literature spanning strategic management, finance, business analytics, information systems, and artificial intelligence to develop an integrated theoretical framework. Research Productivity	positive	development of an integrated theoretical framework via literature synthesis	Reading fidelity high Study strength high	not reported 0.4
The study contributes to strategic management literature by reconceptualizing financial modelling as a dynamic organizational capability. Organizational Efficiency	positive	reconceptualization of financial modelling as a dynamic capability	Reading fidelity high Study strength speculative	not reported 0.04
The framework integrates previously fragmented theoretical perspectives on AI-enabled financial decision capability. Research Productivity	positive	integration of fragmented theoretical perspectives	Reading fidelity high Study strength low	not reported 0.12
The proposed framework offers practical guidance for organizations seeking to strengthen evidence-based financial governance, strategic adaptability, and sustainable competitive advantage in increasingly data-intensive business environments. Organizational Efficiency	positive	strengthening of evidence-based financial governance, strategic adaptability, and sustainable competitive advantage	Reading fidelity high Study strength speculative	not reported 0.04