Data privacy and security threats associated with AI technology adoption in banking services are mentioned as challenges, but specific mitigation strategies, regulatory compliance frameworks, and impl

The gap

Consensus across the literature

Supporting evidence — 4 representative gaps

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

  Data privacy and security threats associated with AI technology adoption in banking services are mentioned as challenges, but specific mitigation strategies, regulatory compliance frameworks, and implementation guidelines are not discussed.

  Keywords: privacy security threats associated technology adoption banking services mentioned challenges specific mitigation strategies regulatory compliance
• ADOPTION OF ARTIFICIAL INTELLIGENCE IN ACCOUNTING AND ITS IMPACT ON FINANCIAL REPORTING QUALITY AND TRANSPARENCY (2026) · doi

  The paper does not examine potential negative consequences or risks associated with AI adoption in accounting, such as algorithmic bias in automated transaction classification, over-reliance on AI predictions leading to reduced auditor skepticism, or data privacy risks in AI-enabled financial disclosure systems. Future research should investigate adverse effects and develop mitigation strategies for implementing AI in high-stakes financial reporting environments.

  Keywords: AI adoption algorithmic bias transaction classification auditor skepticism data privacy financial disclosure
• Recruiter's Perception of Artificial Intelligence Based Tools in Recruitment at Virtusa, Chennai (2026) · doi

  Data privacy concerns are reported by 18% of respondents regarding candidate data processing in AI systems, but the paper does not specify which regulatory frameworks (GDPR, CCPA, India's DPDP Act) apply to Virtusa's recruitment context or how privacy compliance impacts AI tool selection. Compliance-driven AI tool evaluation frameworks for recruitment require development.

  Keywords: data privacy compliance candidate data GDPR CCPA recruitment AI regulatory framework
• Impact on Adoption of Artificial Intelligence(AI) in the Banking Industry. (2026) · doi

  The study identifies lack of expertise as an impediment to AI adoption but does not quantify the specific skill gaps (machine learning engineering, data science, AI governance expertise) required for different AI banking applications or measure training outcomes needed to address workforce readiness for AI-powered banking systems.

  Keywords: AI adoption banking expertise skill gaps workforce training machine learning engineering