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October 13, 2025Business Ethics and Leadership9 citationsOpen Access

Ethics, Institutions, Infrastructure, and Governance in AI National-Level Readiness: A Hidden Driver of Banking Transformation

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SSSvetlana SitnickaMMMuslum MursalovHMHamdulla Mammadov

Key Points

  • Stronger national AI readiness positively impacts bank profitability.
  • A one-unit increase in AI readiness index leads to a 0.524 increase in ROA.
  • Fixed effects model confirmed stability and significance of results.
  • Ethical, institutional, and infrastructural factors are crucial for financial performance.

Abstract

The growing deployment of artificial intelligence (AI) in banking raises critical questions about whether national-level readiness, defined by ethics, institutions, infrastructure, and governance (EIIG), translates into measurable financial performance gains. This article provides empirical evidence on the link between government AI readiness and banking sector profitability. Using an unbalanced panel dataset of 136 countries covering 2020–2024, the study integrates Return on Assets (ROA) from the IMF with the Government AI Readiness Index from Oxford Insights, which embeds EIIG dimensions of ethical frameworks, institutional quality, infrastructural robustness, and governance capacity. Data preprocessing involved applying Yeo–Johnson transformations to address non-normal distributions, and panel econometric models were estimated using both fixed and random effects, with the Hausman test guiding model selection. The results indicate that stronger national EIIG readiness has a significant positive impact on bank profitability. The fixed effects model indicates that a one-unit increase in the transformed AI readiness index is associated with a 0.524 increase in transformed ROA (p < 0.001). In contrast, the random effects specification produced a negative coefficient (β = –0.126, p < 0.01). The Hausman test (χ² = 42.98, p < 0.001) confirmed fixed effects as the consistent estimator. Robust covariance estimators (clustered by country, clustered by year, and Driscoll–Kraay) further confirmed the stability of the coefficients, which remained consistently positive and significant. Country-specific fixed effects highlight structural heterogeneity: advanced economies such as Germany (α = –4.14) and the United Kingdom (α = –4.15) exhibit structurally lower profitability, while emerging economies, including Malawi (α = +0.72), Ghana (α = –0.59), and Mozambique (α = –0.24) align more closely with or exceed global averages. These findings underscore that ethical safeguards, institutional capacities, digital infrastructures, and governance mechanisms are not peripheral but central in enabling AI readiness to deliver sustainable financial performance.

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Cite This Study

Sitnicka et al. (2025) studied this question.

synapsesocial.com/papers/68ed3352c8c3d6f5ff5dd869https://doi.org/10.61093/bel.9(3).290-304.2025
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Artificial intelligence in banking: who wins, who waits, and why it matters2026
  2. 2Artificial Intelligence Governance and Organizational Readiness in Banking: Evidence from Albania2026
  3. 3Research on the Transformation Acceleration of Financial Institutions and Governance Efficiency with Artificial Intelligence Technology2026
  4. 4Artificial intelligence in banking: a transformative force for profitability or an overhyped investment? Evidence from Malaysia2026
  5. 5Measuring Governance or Wealth? Construct Validation of AI Readiness Indices2026 · 1 citations