Financial Inclusion through AI: Predictive Loan Approvals for Underbanked Populations
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Abstract
Financial inclusion has become a key goal for ensuring equitable access to financial services, especially for underbanked populations. Traditional financial systems often exclude these populations due to a lack of credit history, limited access to banks, and other socio-economic factors. However, recent advancements in Artificial Intelligence (AI) offer significant potential in bridging these gaps. This paper explores the use of AI-driven predictive models to enable more accurate and fair loan approvals for underbanked individuals. By leveraging machine learning algorithms, such as decision trees, neural networks, and ensemble methods, this research aims to provide a framework for assessing creditworthiness beyond traditional metrics like credit scores. The paper also investigates how AI can improve the financial inclusion process by using alternative data sources such as mobile phone usage, transaction histories, and social media patterns. Additionally, it discusses the ethical considerations, potential biases, and privacy concerns associated with implementing AI for loan approvals. Ultimately, the paper argues that AI, when used responsibly, can play a crucial role in fostering financial inclusion by offering accessible credit to populations traditionally left out of the formal banking system.