A Deep Learning Approach for Loan Default Prediction Using Imbalanced Dataset

Author:

Owusu Ebenezer1ORCID,Quainoo Richard1,Mensah Solomon1,Appati Justice Kwame1ORCID

Affiliation:

1. University of Ghana, Ghana

Abstract

Lending institutions face key challenges in making accurate predictions of loan defaults. Large sums of money given as loans are defaulted and this causes a substantial loss in business. This study addresses loan default in online peer-to-peer lending activities. Data for the study was obtained from the online lending club on the Kaggle platform. The loan status was chosen as the dependent variable and was classified discretely into “default” and “fully paid” loans. The dataset is preprocessed to eliminate all irrelevant instances. Due to the imbalanced nature of the dataset, the adaptive synthetic (ADASYN) oversampling algorithm is used to balance the data by oversampling the minority class with synthetic data instances. Deep neural network (DNN) is used for prediction. A prediction accuracy of 94.1% is realized and this emerged as the highest score from several trials with variations in batch sizes and epochs. The result of the study clearly shows that the proposed procedure is very promising.

Publisher

IGI Global

Subject

Decision Sciences (miscellaneous),Information Systems

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Feature selection in peer-to-peer lending based on hybrid modified grey wolf optimization with optimized decision tree for credit risk assessment;International Journal of Management Science and Engineering Management;2024-07-31

2. Hybrid Model Based on Machine Learning for the Prediction of Consumer Credit Delinquency in the Banking Sector of Peru;2024 International Symposium on Intelligent Robotics and Systems (ISoIRS);2024-06-14

3. Loan Default Prediction Based on Logistic Regression and XGBoost Modeling;2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT);2024-04-26

4. Loan Default Prediction Using Machine Learning;2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO);2024-03-14

5. Using Machine Learning Models For Predicting Loan Status And Computation Of Interest Rate;2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE);2024-01-24

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