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Prediction of Charged-off Loans for P2P Online Banking using Classification Models and Deep Neural Network

Bhardwaj, Bharat (2020) Prediction of Charged-off Loans for P2P Online Banking using Classification Models and Deep Neural Network. Masters thesis, Dublin, National College of Ireland.

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Peer-to-Peer (P2P) lending operation is an innovative way of lending activity to invest and borrow money than traditional banking operations. P2P online banking gives an internet platform where investors and borrowers meet directly without any middleman which could be a shared benefit of high return and low interest between investors and borrowers. Social lending operations are based on peers, where investors face the direct risk of damages in case borrowers does not pay the loan amount. Hence, accurate prediction of charged-off loan is necessary in P2P online banking. This research paper presents a study to predict the charged-off loans accurately on using unlabelled data of P2P LendingClub online platform. Our study utilises multi-dimensional loan data for mining activities and presents statistical observations for the data. In further research, the study applies modern Logistic Regression (LR), Random Forest Classifier (RFC), KNearest Neighbour (KNN) along with hyperparameter tuning and Artificial Neural Network (ANN), which uses real transactional data of LendingClub. The study compares the outcomes of the classification model and artificial neural network results to identify the suitable model in prediction of charged-off loans for LendingClub investors.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
H Social Sciences > HG Finance > Banking > E-banking
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 22 Jan 2021 10:36
Last Modified: 22 Jan 2021 10:36

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