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Artificial Intelligence (AI) in Fintech has emerged as a transformative force, revolutionizing financial services with its potential for efficiency, innovation, and improved customer experiences. This paper delves into the paradigm shift brought about by AI in the Fintech sector, focusing on the theoretical and practical application and acceptance it has gained and challenges it presents to harness its benefits effectively. This study employs a comprehensive analysis of secondary data sourced from reputable corporate databases. Utilizing a quantitative approach, data was rigorously screened and filtered to ensure accuracy and relevance. Analysis was conducted using statistical methods to identify patterns and trends within the data. This methodology is anticipated to reveal insights into the application and acceptance of AI with special references to banking and finance. Challenges include concerns over data privacy, regulatory compliance, algorithmic biases, and potential cyber threats. Suggestions involve robust data governance frameworks, adherence to regulatory guidelines, ethical AI practices, and advanced cybersecurity measures. The research aims to provide a comprehensive understanding of the risks associated with AI in Fintech and proposes actionable measures to ensure a secure and sustainable integration, thus maximizing the potential of AI in the financial technology landscape. A literature research combined information from multiple sources were collected to create a cohesive narrative about AI's current condition and future prospects in Fintech. To verify data dependability, cross-referencing from numerous sources and assessing trustworthiness based on publication repute and relevancy were carried out. This study employs a comprehensive analysis of secondary data sourced from reputable corporate databases. Utilizing a quantitative approach, data was rigorously screened and filtered to ensure accuracy and relevance. Analysis was conducted using statistical methods to identify patterns and trends within the data. This methodology is anticipated to reveal insights into [mention the expected outcome or findings.
Ramrakhyani et al. (2024) studied this question.
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