Research Article
Credit Risk Assessment - A Machine Learning Approach
@INPROCEEDINGS{10.1007/978-3-031-35081-8_4, author={Thumpala Archana Acharya and Pedagadi Veda Upasan}, title={Credit Risk Assessment - A Machine Learning Approach}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part II}, proceedings_a={ICISML PART 2}, year={2023}, month={7}, keywords={Artificial Intelligence Machine Learning Credit Risk KNN Logistic Regression XGBoost Default Risk}, doi={10.1007/978-3-031-35081-8_4} }- Thumpala Archana Acharya
Pedagadi Veda Upasan
Year: 2023
Credit Risk Assessment - A Machine Learning Approach
ICISML PART 2
Springer
DOI: 10.1007/978-3-031-35081-8_4
Abstract
Banks are foregoing their present reserves for future sources of Revenue. This source is associated with a risk called credit default risk which increases defaulting conditions called the Non-performing assets(loans) thus leading to the financial crisis. Machine Learning, a branch of Artificial Intelligence, is the upcoming technology with promising solutions to present limitations of the systems eliminating the human errors or emotions with precision by way of training and testing. The present study is focused on predicting defaulting loans using algorithms of Machine learning. The dataset is preprocessed for dropping the missing values. Further three models - Logistic Regression, KNN and XGBoost are applied for predicting defaulters based on precision, recall and F1-score. The findings of the research concluded that the XGBoost model performed best among the three models for assessment of credit risk which will waive off the crisis situation.
