Mixed-methods study demonstrates improved efficiency and fraud detection from banking AI in emerging economies, suggesting infrastructure and regulatory reforms are essential.
HRMARS - Artificial Intelligence (AI) is reshaping banking systems through accelerated digital transformation, particularly in emerging economies where financial inclusion, efficiency, and risk resilience are critical challenges. The rapid adoption of AI-driven tools in banking operations has created new opportunities to enhance operational efficiency, strengthen risk management, and improve customer experience. However, limited infrastructure, regulatory constraints, and skill gaps continue to hinder optimal integration. This study examines the impact of AI and digital transformation on banking operations with a focus on operational efficiency, risk management, and customer service improvement. The main problem addressed is the slow and uneven adoption of AI technologies in emerging banking sectors, which restricts competitiveness and innovation. The research objective is to analyze how AI-enabled systems influence banking performance and decision-making processes. A mixed-method approach was employed, combining secondary data analysis from recent banking reports (2023–2025) with qualitative insights from industry case studies in emerging economies. Findings reveal that AI significantly reduces transaction processing time, improves fraud detection accuracy, and enhances personalized customer services through predictive analytics and chat-based systems. However, challenges such as cybersecurity risks, data privacy concerns, and technological disparity remain significant barriers. The study concludes that AI-driven digital transformation is essential for modern banking competitiveness in emerging economies. It recommends strategic investment in digital infrastructure, workforce upskilling, and regulatory modernization to maximize benefits while minimizing risks.
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Permono et al. (2026) studied this question.
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