Predicting Unsettled Debts in Imbalanced Data using Resampling Methods

Unsettled debt is a central global problem for banks and businesses. Being able to accurately predict which loans will default would be incredibly valuable for understanding the state of the economy. Machine learning models can be used to predict debt defaults, which can help these organizations make better decisions and reduce their risk. This study contributed a unique dataset collected from Egyptian Social Insurance organization (NOSI) with a proposed approach for defaulted credit prediction using classification learning and resampling methods for solving imbalanced data, which is a common challenge in debt default and credit data analysis studies. The approach was evaluated using three tree-based classification algorithms: Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). The Similarity Oversampling and Under-sampling Pre-processing (SOUP) resampling technique outperformed all other resampling techniques when applied with RF, achieving high micro-average precision and recall of 66.67% and 67.38%, respectively, compared to the original dataset's 50.42% and 50.39%.