@inproceedings{eff30e2bec794df89c21d169965c81b4,
title = "Loan Default Predictive Analytics",
abstract = "Making accurate loan default projections is difficult for lending organizations. Large quantities of money are owed as loans, resulting in a significant loss to lending companies. The study delves into loan default in online peer-to-peer lending by introducing a model to classify a loan instance as default and fully paid. Due to the imbalance nature of the loan default dataset extracted from Kaggle, an Adaptive Synthetic Sampling (ADASYN) approach is used to balance the data by oversampling the minority class. A Deep Neural Network (DNN) is used for the training and validation needs. The prediction accuracy of 94.1% is obtained. This performance is the highest score across multiple experiments with different batch sizes and epochs. The findings clearly demonstrate that the proposed technique is very promising.",
keywords = "Adaptive synthetic sampling (ADASYN), Deep neural network (DNN), Imbalanced dataset, Loan-default",
author = "Ebenezer Owusu and Richard Quainoo and Appati, {Justice Kwame} and Solomon Mensah",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022 ; Conference date: 17-06-2022 Through 19-06-2022",
year = "2022",
doi = "10.1109/AIC55036.2022.9848906",
language = "English",
series = "Proceedings - 2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "617--622",
editor = "Tomar, {Geetam S.} and Jagdish Bansal",
booktitle = "Proceedings - 2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022",
}