International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Credit scoring plays a crucial role in the financial sector, helping institutions assess both the repayment ability and risk profile of borrowers. Over the years, machine learning has brought major improvements to this process. However, many existing models still face challenges related to data quality, interpretability, and genuine predictive stability. This study proposes a practical machine learning framework that combines automated data correction, guided by expert domain knowledge, with powerful ensemble-based learning techniques. The system achieves complete classification accuracy on a real-world credit dataset, marking a significant step forward in data-driven lending analysis.
By integrating traditional banking logic with modern supervised algorithms, the framework ensures highly accurate predictions, clear interpretability, and robust financial outcomes. Experimental analysis confirms that proper data refinement and consensus modeling can effectively distinguish between reliable and risky borrowers. The proposed approach can serve as a foundation for future AI-driven credit scoring systems that meet both operational and regulatory expectations.

Keywords: Credit Scoring, Machine Learning, Ensemble Learning, Domain Expertise, Data Correction, Feature Engineering, Explainable AI, Uncertainty Quantification, FinTech, Predictive Modeling, Supervised Learning, XGBoost, LightGBM, Random Forest, Gradient Boosting, Financial Inclusion, Risk Assessment, Credit Risk Modeling, Model Interpretability.

Downloads: | DOI: 10.17148/IJARCCE.2025.141142