Automated Credit Scoring: Leveraging Machine Learning for Financial Inclusion
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Abstract
This paper examines how machine learning algorithms can improve credit scoring models to enhance financial inclusion. It highlights the use of AI to analyze alternative data sources, such as social media activity and transaction history, to assess creditworthiness. The study compares traditional credit scoring systems with ML-based models, discussing the potential for reduced bias and increased access to credit for underrepresented populations.
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References
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