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LITERATURE REVIEW 
The literature on the use of artificial intelligence (AI) in the financial sector of developing and 
underdeveloped countries is broad and multidimensional. Several studies focus on distinct areas, 
including challenges, prospects, ethical considerations, and comparative analyses.
Chen & Leung (2018) conducted an extensive review of AI's prospects in Southeast Asian 
financial services. They highlighted the potential of AI in improving efficiency and personalizing 
customer services but emphasized the need for a supportive regulatory environment and robust 
cybersecurity measures. Guo, J., & Li, B. (2018) concentrated on AI's impact on financial 
inclusion in Nigeria. Their research found that AI has the potential to significantly increase 
financial accessibility, particularly among unbanked populations. However, they argued that this 
necessitates government investment in technological infrastructure and literacy. Dhara et.al, 
(2022) in their study of the Indian banking sector, identified key challenges in adopting AI, 
including lack of infrastructure, insufficient expertise, and unclear regulatory frameworks. They 
emphasized the need for a coherent national strategy to align AI development with financial 
sector goals. 
Bughin et al. (2017) surveyed various developing countries, discovering common barriers such 
as lack of technical know-how, resistance from traditional financial institutions, and data privacy 
concerns. They recommended fostering innovation through government incentives and cross-
border collaborations. McClelland, C. (2020) explored how AI could act as a catalyst for 
economic growth in underdeveloped regions. They identified areas where AI can enhance 
efficiency, such as fraud detection, risk management, and decision-making, but stressed the need 
for investment in human capital to fully realize these benefits. Nadeem et al. (2020) focused on 
AI's potential in revolutionizing microfinance, leading to poverty reduction in developing 
countries. They found that AI-powered algorithms could make micro-lending more efficient and 

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