Building a Credit Scoring Model Using Alternative Data Analysis and Artificial Intelligence Methods to Promote Financial Inclusion
DOI:
https://doi.org/10.32826/reyf.v2i5.373Keywords:
Financial inclusion, artificial intelligence, alternative data, XGBoost, credit risk assessmentAbstract
Abstract: This study develops a credit scoring model using alternative data and artificial intelligence to promote financial inclusion. Techniques such as XGBoost, logistic regression, and Random Forest were implemented, demonstrating the effectiveness of unconventional data in credit risk assessment and its impact on financial inclusion.
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