Implementing Adaptive Credit Scoring Through Xgboost and Deep Learning in Open Finance for Microcredit

Abstract

Objective: This study aims to develop and test the effectiveness of an AI-based adaptive credit scoring system that integrates Open Finance principles and the use of alternative data in assessing microcredit eligibility in emerging markets.
Research Design & Methods: This study uses a mixed-methods approach with AI model experiments to test machine learning and deep learning-based adaptive credit scoring systems and thematic analysis to understand implementation challenges and regulatory preparedness.
Findings: Hypothetical results show that AI models (XGBoost, Deep Learning) significantly outperform traditional models (logistic regression) in microcredit eligibility assessment, with an AUC improvement of 15-20% (e.g., AUC 0.88 for XGBoost vs. 0.72 for logistic regression). Similar improvements are also seen in Precision, Recall, and F1-score metrics. Adaptivity testing shows a 2-5% increase in model accuracy after each retraining cycle, with the model's ability to dynamically adjust credit scores in real-time to changes in user behaviour. Interpretation of the results through XAI identified consistent utility bill payment patterns, digital wallet transaction frequency, and mobile phone number usage duration as the most influential predictive factors.
Implications & Recommendations: Integration of AI and Open Finance with alternative data improves the efficiency and inclusiveness of credit scoring, so it is recommended that industry players and regulators develop data infrastructure, apply Responsible AI principles, and formulate comprehensive and collaborative Open Finance regulations.
Contribution & Value Added: This research contributes to the FinTech and AI literature by providing technical and policy guidance to promote financial inclusion by developing adaptive assessment machine and responsible AI oversight in emerging markets.