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Journal Article

An Information Processing and Decision Support model for Credit Default Prediction in Emerging Internet Finance Markets

by Latifa Binti Zainal 1  and  Arijit Al-Masri 1
1
Universiti Malaysia Perlis, Faculty of Engineering Technology, Malaysia
*
Author to whom correspondence should be addressed.
EMGBS  2023 2(1):5; https://doi.org/10.xxxx/xxxxxx
Received: 13 July 2023 / Accepted: 8 November 2023 / Published Online: 31 December 2023
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Abstract

P2P (Peer to Peer) online lending is an emerging Internet finance mode that gathers small-amount fund lending to fund demander. This paper draws on the existing credit risk assessment research, combines rationality, science and other principles, according to the characteristics of the famous online loan platform, collects borrower information and combines computer technology to design a borrower credit risk assessment system. In this paper, we make improvements based on the famous LightGBM algorithm (Light Gradient Boosting Machine). Firstly, In the process of data input, the improved Convolutional Neural Network CNN model is adopted to extract features from the data. Specifically, the Global Average Pooling(GAP) layer is adopted to replace the full connection layer to improve the Convolutional Neural Network. This paper first proposes a P2P online loan default prediction model based on GCNN-LightgBM. The model integrates the advantages of the improved Convolutional Neural Network and LightGBM model, and realizes the efficient prediction of network loan default. Then, in order to improve the accuracy of P2P online loan borrower default prediction, this paper proposes a new model based on LightGBM and Bagging (LGB-BAG). LGB-BAG uses LightGBM as the base learner. With the help of LightGBM, which can effectively reduce the deviation of the model, and Bagging, which can reduce the variance of the model, the volatility of the prediction is further reduced (F1 variance), so that the LGB-BAG model has smaller deviation and variance, and the prediction effect is further improved. In our ablation experiment, the proposed model (GCNN-LGB-BAG) obtained an AUC of 0.86 and an accuracy of 0.97, both of which outperformed all benchmark models. This paper uses actual data to identify the loan risks of P2P online lending platforms, aiming to provide investment reference for investors and methodological support for relevant online lending regulators.

Keywords: Peer to Peer online lending; loan default prediction; Light Gradient Boosting Machine; Global Average Pooling; Bagging;
Copyright: © 2023 by Zainal and Al-Masri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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APA Style
Zainal, L. B., & Al-Masri, A. (2023). An Information Processing and Decision Support model for Credit Default Prediction in Emerging Internet Finance Markets. Economic Management & Global Business Studies, 2(1), 5. doi:10.xxxx/xxxxxx
ACS Style
Zainal, L. B.; Al-Masri, A. An Information Processing and Decision Support model for Credit Default Prediction in Emerging Internet Finance Markets. Economic Management & Global Business Studies, 2023, 2, 5. doi:10.xxxx/xxxxxx
AMA Style
Zainal L B, Al-Masri A. An Information Processing and Decision Support model for Credit Default Prediction in Emerging Internet Finance Markets. Economic Management & Global Business Studies; 2023, 2(1):5. doi:10.xxxx/xxxxxx
Chicago/Turabian Style
Zainal, Latifa B.; Al-Masri, Arijit 2023. "An Information Processing and Decision Support model for Credit Default Prediction in Emerging Internet Finance Markets" Economic Management & Global Business Studies 2, no.1:5. doi:10.xxxx/xxxxxx
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ACS Style
Zainal, L. B.; Al-Masri, A. An Information Processing and Decision Support model for Credit Default Prediction in Emerging Internet Finance Markets. Economic Management & Global Business Studies, 2023, 2, 5. doi:10.xxxx/xxxxxx
AMA Style
Zainal L B, Al-Masri A. An Information Processing and Decision Support model for Credit Default Prediction in Emerging Internet Finance Markets. Economic Management & Global Business Studies; 2023, 2(1):5. doi:10.xxxx/xxxxxx
Chicago/Turabian Style
Zainal, Latifa B.; Al-Masri, Arijit 2023. "An Information Processing and Decision Support model for Credit Default Prediction in Emerging Internet Finance Markets" Economic Management & Global Business Studies 2, no.1:5. doi:10.xxxx/xxxxxx
APA style
Zainal, L. B., & Al-Masri, A. (2023). An Information Processing and Decision Support model for Credit Default Prediction in Emerging Internet Finance Markets. Economic Management & Global Business Studies, 2(1), 5. doi:10.xxxx/xxxxxx

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