Highly class-imbalanced dynamic financial distress prediction based on Bagging-XGBoost

SMOTETomek-DNN: A Machine Learning Framework for Credit Risk Prediction with an Imbalanced Dataset

The experimental findings demonstrate that incorporating resampling techniques substantially improved the default detection performance and suggest that hybrid sampling integrated with advanced learning architectures can provide a reliable and practical solution for managing credit risk in imbalanced microfinance datas...

Explainable Ensemble Learning for Loan Approval Prediction Using XGBoost, LightGBM, and Random Forest with SHAP Analysis

An explainable ensemble learning framework for predicting loan approval using Random Forest, XGBoost, and LightGBM models is proposed and it is revealed that CreditScore, EmploymentType, and Income are the most influential factors in determining loan approval decisions.

SMOTE-Stack-XAI: An Explainable Stacked Ensemble Learning Framework Integrating Random Forest, XGBoost, SVM and Deep Neural Networks for Real-Time Credit Card Fraud Detection

SMOTE-Stack-XAI, a stacked ensemble framework that uses the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance, outperforms the RF, hybrid RF-SVM, and hybrid SVM-LR baselines with an accuracy of 99.3%, precision of 99.2%, recall of 99.4%, and F1-score of 99.3%.

Machine Learning Applications in Credit Risk Prediction

Machine Learning in Small Business Finance: SBA Loan Default Prediction with XGBoost and Explainable AI

This study advances small business credit risk assessment by comparing traditional statistical models with advanced machine learning (ML) techniques, using a publicly available data set of 89,621 U.S. Small Business Administration (SBA)-backed loans. The analysis compares logistic regression, decision tree and extreme...

Mitigating Financial Fraud: A Hybrid SMOTE-Tomek and Stacked Ensemble Model Approach

The study introduces a Stacked Logistic Regression ensemble to combine the predictive capacity of optimized Random Forest and XGBoost base classifiers and reveals that the proposed stacked model performance surpasses both individual base models.