Zonglin Han

Credit scoring is vital in the financial industry,assessing the risk of lending to credit card applicants. Traditionalcredit scoring methods face challenges with large datasets anddata imbalance between creditworthy and non-creditworthy applicants.This paper introduces an advanced machine learningand deep learning framework to improve the accuracy andreliability of credit card approval predictions. We utilized extensivedatasets of user application records and credit history,implementing a comprehensive preprocessing strategy, featureengineering, and model integration. Our methodology combinesneural networks with an ensemble of base models, includinglogistic regression, support vector machines, k-nearest neighbors,decision trees, random forests, and gradient boosting. The ensembleapproach addresses data imbalance using Synthetic MinorityOver-sampling Technique (SMOTE) and mitigates overfittingrisks. Experimental results show that our integrated modelsurpasses traditional single-model approaches in precision, recall,F1-score, AUC, and Kappa, providing a robust and scalablesolution for credit card approval predictions. This researchunderscores the potential of advanced machine learning techniquesto transform credit risk assessment and financial decisionmaking.

Credit scoring is vital in the financial industry, assessing the risk of lending to credit card applicants. Traditional credit scoring methods face challenges with large datasets and data imbalance between creditworthy and non-creditworthy applicants. This paper introduces an advanced machine learning and deep learning framework to improve the accuracy and reliability of credit card approval predictions. We utilized extensive datasets of user application records and credit history, implementing a comprehensive preprocessing strategy, feature engineering, and model integration. Our methodology combines neural networks with an ensemble of base models, including logistic regression, support vector machines, k-nearest neighbors, decision trees, random forests, and gradient boosting. The ensemble approach addresses data imbalance using Synthetic Minority Over-sampling Technique (SMOTE) and mitigates overfitting risks. Experimental results show that our integrated model surpasses traditional single-model approaches in precision, recall, F1-score, AUC, and Kappa, providing a robust and scalable solution for credit card approval predictions. This research underscores the potential of advanced machine learning techniques to transform credit risk assessment and financial decision-making.

We study entire solutions to homogeneous reaction–diffusion equations in several dimensions with Fisher–KPP reactions. Any entire solution is known to satisfy and we consider here those satisfying When is and concave, our main result provides an almost complete characterization of transition fronts as well as transition solutions with bounded width within this class of solutions.