Credit Risk Prediction Based on Machine Learning Methods

Yu Li

51 citations0/0 files storedInternational Conference on Computer Science & Education (ICCSE)

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Financial Distress and Bankruptcy Prediction Customer churn and segmentation Imbalanced Data Classification Techniques

Abstract

Credit scoring which helps to evaluate the capability of repayment of customers is one of the most important issues for loan institutions. Due to the rapid development of machine learning techniques in computer science area and other related subjects, various classification methods have been proposed to facilitate the implementation of machine learning methods for characterizing the repayment behavior of customers. In this research, we use the XGBoost algorithm to identify the bad customers who do noe pay money back from the good customers. A comprehensive study is conducted to compare the performance of XGBoost algorithm with logistic regression. The results show that the XGBoost algorithm works much better.

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2023Credit Risk Prediction using Ensemble Machine Learning Algorithms 2022FAIR: Fair adversarial instance re-weighting 2022Exploitation of Machine Learning Algorithms for Detecting Financial Crimes Based on Customers’ Behavior 2024Effective Credit Risk Prediction Using Ensemble Classifiers With Model Explanation 2021A Credit Risk Model with Small Sample Data Based on G-XGBoost 2023Credit risk analysis using boosting methods 2021Improved credit scoring model using XGBoost with Bayesian hyper-parameter optimization 2022Application of Machine Learning Techniques for Credit Risk Management: A Survey

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