social_science3 papersavg year 2026quality 6/5weak evidence

The paper mentions high costs of AI implementation as a disadvantage but does not provide detailed analysis of cost-benefit ratios, ROI metrics, or scalability considerations for different sizes of ba

Research gap analysis derived from 3 social_science papers in our local library.

The gap

The paper mentions high costs of AI implementation as a disadvantage but does not provide detailed analysis of cost-benefit ratios, ROI metrics, or scalability considerations for different sizes of banking institutions.

Consensus across the literature

Clustered from 3 gap mentions across 3 papers via embedding cosine ≥ 0.62.

Research trend

Established — well-defined area with open sub-problems.

Supporting evidence — 3 representative gaps

  • Artificial Intelligence in the Banking Sector: Transforming Traditional Banking into Smart and Secure (2026) · doi

    The paper mentions high costs of AI implementation as a disadvantage but does not provide detailed analysis of cost-benefit ratios, ROI metrics, or scalability considerations for different sizes of banking institutions.

    Keywords: mentions high costs implementation disadvantage provide detailed cost benefit ratios metrics scalability considerations different sizes
  • Impact on Adoption of Artificial Intelligence(AI) in the Banking Industry. (2026) · doi

    The paper lists data privacy, security concerns, and regulatory limitations as major obstacles to AI adoption in banking, but lacks empirical data on how different regulatory frameworks (GDPR, banking compliance standards, data localization requirements) differentially impact AI adoption rates across banking institutions or geographies.

    Keywords: AI adoption banking regulatory compliance data privacy security constraints
  • Strategic value driven by artificial intelligence in global businesses: a bibliometric and qualitative analysis of the most influential literature (2026) · doi

    The literature documents AI implementations yielding limited or negative returns on investment, but lacks empirical quantification of performance thresholds for data infrastructure adequacy, managerial expertise levels, and strategic alignment quality required to avoid increased operational complexity without strategic gains in AI-driven business environments.

    Keywords: AI adoption data infrastructure managerial expertise strategic alignment return on investment organizational complexity

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