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A survey of 420 finance professionals links AI and intelligent analytics to stronger self-reported financial decision-making—AI shows the larger association (β=0.463) and the model explains 68% of variance—but results are based on cross-sectional, convenience-sample self-reports and do not establish causation.

Finance 5.0: Transforming Financial Decision-Making Through Artificial Intelligence and Intelligent Analytics
Sarmad Bin Saeed, Saba Mahmood, Abid Manzoor · August 20, 2026 · Journal of Business Insight and Innovation
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A cross-sectional survey of 420 finance professionals finds positive associations between self-reported use of AI (β=0.463) and intelligent analytics (β=0.387) and improved financial decision-making, with the model explaining 68.4% of variance, but evidence is correlational and perception-based.

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Finance 5.0 is a revolution in financial management that brings AI and intelligent analytics into the corporate decision-making process. This research paper focused on examining the impact of these digital technologies on financial decision-making in present organizations. A cross-sectional survey approach was used for the quantitative research design. Primary data were gathered from 420 finance professionals such as financial managers, accountants, analysts, banking officers, and finance executives of organizations that have implemented digital financial technologies (DFTs). The questionnaires measured Artificial Intelligence, Intelligent analytics, and financial decision-making on a 5-point scale in a structured manner. The collected data were analysed using descriptive statistics and multiple regression analysis in SPSS 29. The results showed a positive attitude toward all the Study variables. The highest mean (SD = 0.52) was found for financial decision making, followed by intelligent analytics (mean = 4.19, SD = 0.61), and artificial intelligence (mean = 4.27, SD = 0.56). The regression analysis revealed that artificial intelligence had a significant effect on financial decision making (β = 0.463, p < 0.001), as did intelligent analytics (β = 0.387, p < 0.001). The overall model accounted for 68.4 % of the variance explained (R² = 0.684) in financial decision making. The study found success in financial planning and forecasting, efficiency, and strategic decision making with the help of intelligent technologies in Finance 5.0. The result has important implications for financial institutions, corporate organizations, policy and decision makers, as well as technology developers who want to gain a competitive advantage and/or accelerate digital financial transformation by developing smart financial ecosystems. References Akter, S., Wamba, S. 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Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257 Sutton, S. G., Holt, M., & Arnold, V. (2023). The reports of my death are greatly exaggerated—Artificial intelligence research in accounting. International Journal of Accounting Information Systems, 48, 100598. https://doi.org/10.1016/j.accinf.2022.100598 Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022 Verma, S., Sharma, R., Kumar, A., & Gupta, P. (2024). Artificial intelligence adoption in financial services: Enhancing financial decision-making through intelligent analytics. 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Summary

Main Finding

AI and intelligent analytics are strongly and positively associated with better financial decision-making in organizations adopting Finance 5.0. In a cross‑sectional survey of 420 finance professionals, multiple regression shows both predictors are significant (AI: β = 0.463, p < 0.001; Intelligent Analytics: β = 0.387, p < 0.001) and together explain 68.4% of the variance in financial decision‑making (R² = 0.684).

Key Points

  • Study context: "Finance 5.0"—an integrated financial ecosystem combining AI, intelligent analytics, cloud, blockchain and big data to enable adaptive, real‑time financial decisions.
  • Sample: 420 finance professionals (financial managers, accountants, analysts, banking officers, executives) from organizations that have implemented digital financial technologies.
  • Survey measures (5‑point Likert): Artificial Intelligence (mean = 4.27, SD = 0.56), Intelligent Analytics (mean = 4.19, SD = 0.61). Financial decision‑making had the highest mean (SD = 0.52) per the abstract.
  • Quantitative results: Both AI and intelligent analytics have statistically significant positive effects on perceived quality of financial decision‑making; combined model explains a large share of variance (R² = 0.684).
  • Reported benefits: improved financial planning and forecasting, operational efficiency, and strategic decision making; improved fraud/risk detection, credit assessment, portfolio optimization and forecasting accuracy.
  • Implementation challenges noted: data quality, algorithmic transparency, cybersecurity, ethical governance, workforce readiness, regulatory standards, and the need for organizational change beyond technology.

Data & Methods

  • Design: Cross‑sectional quantitative survey.
  • Population & sampling: Finance professionals in organizations using digital financial technologies; convenience sampling.
  • n = 420 respondents.
  • Instrument: Self‑administered structured questionnaire. Demographics + multi‑item constructs for AI, intelligent analytics, and financial decision‑making rated 1 (Strongly Disagree) to 5 (Strongly Agree).
  • Analysis: Data screening and descriptive statistics; multiple regression analysis performed in SPSS v29 to assess effects of AI and intelligent analytics on financial decision‑making.
  • Limitations (implicit in methods): cross‑sectional and self‑report design (limits causal inference), convenience sampling (limits generalizability), limited detail on construct validity/reliability reported in the excerpt.

Implications for AI Economics

  • Firm productivity and value capture: Strong association between AI/analytics and decision quality implies firms that adopt Finance 5.0 technologies may realize measurable gains in forecasting accuracy, risk management, and resource allocation—key drivers of firm‑level productivity and profitability.
  • Returns to AI investment: High R² suggests large within‑firm effects; economists can treat AI and analytics as complementary capital investments with substantial marginal returns in financial management contexts. Quantifying these returns longitudinally would inform investment and valuation models.
  • Labor and skill complementarities: Findings underscore complementarity between human financial professionals and AI tools—policy and firm strategies should emphasize reskilling and analytic literacy to capture value and avoid displacement costs.
  • Market structure and competition: Faster, data‑driven decision cycles may confer competitive advantages to early adopters, potentially increasing market concentration in sectors where Finance 5.0 is deployed effectively.
  • Regulatory and governance considerations: Data quality, algorithmic transparency, ethics and cybersecurity risks highlighted by the study point to market failures (information asymmetries, externalities) that justify public interventions—standards for model explainability, data governance infrastructure, and auditability will affect adoption dynamics.
  • Research priorities for AI economics:
    • Establish causal estimates (panel, quasi‑experimental or randomized designs) of AI/analytics investments on firm performance and employment.
    • Estimate heterogeneous effects across firm size, sector, and country digital maturity.
    • Model general equilibrium and distributional effects (wages, employment composition, industry reallocation).
    • Measure complementarities between data infrastructure, human capital, and AI adoption to inform policy on subsidies, training, and data‑sharing platforms.

Actionable takeaway: For economists studying technological change, this paper provides survey evidence that AI and intelligent analytics substantially correlate with improved financial decision outcomes—next steps should move from cross‑sectional associations to causal, micro‑founded estimates of returns, distributional impacts, and policy interventions that shape the diffusion of Finance 5.0.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on self-reported, cross-sectional survey data from a convenience sample, so observed relationships (significant betas, R²) reflect correlation and may be driven by common-method bias, reverse causality, omitted variables, or sampling bias rather than causal effects of AI/analytics on decision-making. Methods Rigorlow — The study uses convenience sampling, self-administered questionnaires without reported validation of measures, a single cross-sectional wave, and basic regression without causal identification strategies or robustness checks reported; descriptive and regression results are presented but potential biases (common-method variance, selection, measurement validity) are not addressed. Sample420 finance professionals (financial managers, accountants, analysts, auditors, banking officers, finance executives) from organizations that have implemented digital financial technologies; recruited via convenience sampling using printed and online self-administered questionnaires; geographic/sampling frame and response rate not reported. Themeshuman_ai_collab adoption org_design IdentificationCross-sectional convenience survey of finance professionals with multiple regression analysis of self-reported AI, intelligent analytics, and financial decision-making measures; no experimental or quasi-experimental design and no strategies to support causal identification (results are associative). GeneralizabilityConvenience, non-probability sample limits representativeness across firms, sectors, and countries, Sample restricted to organizations that already implemented digital financial technologies (selection on adoption), Findings rely on self-reported perceptions rather than objective performance metrics, Cross-sectional design prevents inference about temporal ordering or causality, Measurement and instrument validation details not reported, limiting comparability

Claims (4)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Artificial intelligence had a statistically significant positive effect on financial decision-making among finance professionals in organizations using digital financial technologies (β = 0.463, p < 0.001). Decision Quality positive Financial decision-making
Reading fidelity high
Study strength low
n=420
β = 0.463
0.15
Intelligent analytics had a statistically significant positive effect on financial decision-making among finance professionals in organizations using digital financial technologies (β = 0.387, p < 0.001). Decision Quality positive Financial decision-making
Reading fidelity high
Study strength low
n=420
β = 0.387
0.15
Artificial intelligence and intelligent analytics together explained 68.4% of the variance in financial decision-making. Decision Quality positive Financial decision-making
Reading fidelity high
Study strength low
n=420
R² = 0.684, accounting for 68.4% of variance
0.15
The study reported positive attitudes toward artificial intelligence, intelligent analytics, and financial decision-making among the surveyed finance professionals. Decision Quality positive Attitudes toward artificial intelligence, intelligent analytics, and financial decision-making
Reading fidelity high
Study strength low
n=420
0.15

Notes