Leveraging Artificial Intelligence and Machine Learning for Real-Time Loan Approval Processes in FinTech
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Abstract
AI and ML have revolutionized loan granting across the financial technology industries through lending performance evaluations, changing from conventional, manual analysis to automatic, real-time computations. This transition resolves some of the main failures of conventional methods, significantly decreasing approval time, increasing the accuracy of risk assessment, and creating custom loan services for various customer types. Big data and symbiotic non-conventional parameters, including social media scores and behavioral patterns, are used in the AI and ML systems to determine an applicant's creditworthiness, thus extending a fair credit–risk culture in financial services. Through certain critical technologies like neural networks, NLP, and credit scoring models, there is a more secure and dynamic way of lending since real-time frauds are detected online. This paper focuses on the development, issues, and impact of the regulation of using artificial intelligence in the credit approval process among FinTech firms. The research indicates that while using AI improves business performance and customer experiences, the case necessitates appropriate data security and bias elimination policies to be implemented by FinTech companies. The paper concludes with prospects for the development of AI to further progress financial inclusion and the development of loaning industries.
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