A Comprehensive Evaluation of Data Balancing Techniques and Machine Learning Models for Credit Risk Assessment

Safae Ndama (), Oussama Ndama () and El Mokhtar En-Naimi ()
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Safae Ndama: FST of Tangier, Abdelmalek Essaâdi University, DSAI2S Research Team, C3S Laboratory
Oussama Ndama: FST of Tangier, Abdelmalek Essaâdi University, DSAI2S Research Team, C3S Laboratory
El Mokhtar En-Naimi: FST of Tangier, Abdelmalek Essaâdi University, DSAI2S Research Team, C3S Laboratory

A chapter in Technological Innovations for Sustainable Development, 2025, pp 142-155 from Springer

Abstract: Abstract Credit risk assessment plays a critical role in ensuring responsible lending and reducing financial losses in the banking sector. With the growing availability of financial data, machine learning (ML) has emerged as a powerful tool for evaluating creditworthiness by uncovering hidden patterns. However, the highly imbalanced nature of credit datasets where non-default cases vastly outnumber defaults poses a significant challenge to predictive accuracy. To address this, our study examines and compares six resampling techniques: Synthetic Minority Over-sampling Technique (SMOTE), Random Over Sampling, Random Under Sampling, Adaptive Synthetic Sampling (ADASYN), Tomek Links, and a hybrid SMOTE-RUS approach. These methods are applied across various ML models, including Decision Tree, Random Forest, XGBoost, Neural Network, AdaBoost, CatBoost, and K-Nearest Neighbors. The hybrid SMOTE-RUS technique demonstrated the most balanced outcomes, enhancing accuracy while reducing overfitting risks. SMOTE and ADASYN also showed notable improvements in detecting minority-class instances. This research offers a detailed comparative analysis of resampling strategies, helping to guide the development of more reliable and equitable credit scoring models for real-world applications.

Keywords: Credit Risk Assessment; Machine Learning in Finance; Class Imbalance; Data Balancing Techniques; SMOTE and ADASYN (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-032-06725-8_12

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