Artificial intelligence (AI) has become an important driver of innovation and digital transformation in modern financial systems. This study empirically examines patterns of AI adoption across key banking functions and geographical regions using descriptive and comparative analysis of secondary quantitative data obtained from recent industry reports and market analyses. The findings indicate that AI adoption is highest in fraud detection (60–65%) and risk management (approximately 60%), while adoption in credit scoring remains comparatively lower (45–50%). The regional analysis reveals substantial disparities, with North America recording the highest adoption rate (65%) and Africa and the Middle East the lowest (40%). The results also highlight increasing investment and market growth, confirming the strategic importance of AI for financial innovation. The study contributes to understanding current patterns of AI-driven transformation while emphasizing the need for responsible implementation, regulatory adaptation, data protection, and transparent AI governance.
This study investigates the impact of artificial intelligence (AI) adoption on financial reporting accuracy within emerging economies, with a specific focus on firms in Ibadan, Nigeria. Employing a mixed-methods design, the research integrates panel-data regression analysis of 120 firms over five years with semi-struct...
Olushola Rasheed Jimoh, K. Adeagbo· International Journal of Afr...· 0 citations
The AI Performance Enabling Ecosystem (APEE) framework is introduced—anchored in data quality, governance maturity, and regulatory compliance—as the primary determinants of AI-driven risk performance in emerging markets, offering actionable insights for regulators, policymakers, and financial institutions across the ME...
This study aims to identify the key challenges and development prospects of AI implementation to enhance business efficiency and ensure sustainable development, providing strategic guidance for organizations to balance technological innovation with responsibility and risk management.
This study investigates the impact of Artificial Intelligence (AI) adoption on operational efficiency and financial risk management in six major Indonesian banks: BNI, BRI, BCA, Danamon, Mandiri, and CIMB Niaga. The research implements Difference-in-Difference (DiD) methodology coupled with Bayesian Vector Autoregressi...
N. Sari, Denisha Albania Prajoko, Nahdiyah Istiqomah et al.· Journal of Central Banking L...· 0 citations
Findings reveal that AI and BI significantly enhance the precision, speed, and objectivity of credit risk assessments, enabling improved identification of high-risk borrowers and reducing subjective biases.
A. Ashun, Abubakar Sani, A. Sagoe· Journal of Applied Business...· 0 citations
It is found that stock markets had become more efficient by being data-driven due to the advent of AI, with proper regulation, ethical AI practices and human oversight to maximize the benefits and minimize pitfalls on certain risks.
Aadi Chandra· International journal of soc...· 0 citations
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