Artificial Intelligence (AI) is redefining compliance frameworks in the banking sector by automating regulatory processes, reducing operational risks, and improving fraud detection. This study investigates the impact of AI maturity on compliance effectiveness, proactive risk management, and cost efficiency. Data were collected from 150 banking professionals through a structured questionnaire, supported by secondary data from 10 banks. Using descriptive statistics, Pearson correlation, and multiple regression, the findings confirm that AI maturity is a strong predictor of compliance effectiveness (β = .72, p < .001) and operational cost reduction (β = .73, p < .001). The results also reveal significant barriers, including data privacy concerns, algorithmic bias, and regulatory uncertainty, which negatively affect AI adoption. The study emphasizes the strategic need for ethical AI frameworks, workforce training, and regulator–bank collaboration to leverage AI’s full potential. These findings contribute to the emerging literature on AI-enabled governance and offer actionable insights for both practitioners and policymakers aiming to create smarter compliance ecosystems. Keywords: Artificial Intelligence, Banking Compliance, AI Maturity, Fraud Detection, Regulatory Technology (RegTech), Operational Efficiency, Risk Management
Singh et al. (2025) studied this question.