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Towards Repayment Prediction in Peer-to-Peer Social Lending Using Deep Learning

Author

Listed:
  • Ji-Yoon Kim

    (Department of Computer Science, Yonsei University, Seoul 03722, Korea)

  • Sung-Bae Cho

    (Department of Computer Science, Yonsei University, Seoul 03722, Korea)

Abstract

Peer-to-Peer (P2P) lending transactions take place by the lenders choosing a borrower and lending money. It is important to predict whether a borrower can repay because the lenders must bear the credit risk when the borrower defaults, but it is difficult to design feature extractors with very complex information about borrowers and loan products. In this paper, we present an architecture of deep convolutional neural network (CNN) for predicting the repayment in P2P social lending to extract features automatically and improve the performance. CNN is a deep learning model for classifying complex data, which extracts discriminative features automatically by convolution operation on lending data. We classify the borrower’s loan status by capturing the robust features and learning the patterns. Experimental results with 5-fold cross-validation show that our method automatically extracts complex features and is effective in repayment prediction on Lending Club data. In comparison with other machine learning methods, the standard CNN has achieved the highest performance with 75.86%. Exploiting various CNN models such as Inception, ResNet, and Inception-ResNet results in the state-of-the-art performance of 77.78%. We also demonstrate that the features extracted by our model are better performed by projecting the samples into the feature space.

Suggested Citation

  • Ji-Yoon Kim & Sung-Bae Cho, 2019. "Towards Repayment Prediction in Peer-to-Peer Social Lending Using Deep Learning," Mathematics, MDPI, vol. 7(11), pages 1-17, November.
  • Handle: RePEc:gam:jmathe:v:7:y:2019:i:11:p:1041-:d:283110
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    References listed on IDEAS

    as
    1. Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
    2. Xuchen Lin & Xiaolong Li & Zhong Zheng, 2017. "Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China," Applied Economics, Taylor & Francis Journals, vol. 49(35), pages 3538-3545, July.
    3. Riza Emekter & Yanbin Tu & Benjamas Jirasakuldech & Min Lu, 2015. "Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 54-70, January.
    4. Michal Polena & Tobias Regner, 2018. "Determinants of Borrowers’ Default in P2P Lending under Consideration of the Loan Risk Class," Games, MDPI, vol. 9(4), pages 1-17, October.
    5. Jiaqi Yan & Wayne Yu & J. Leon Zhao, 2015. "How signaling and search costs affect information asymmetry in P2P lending: the economics of big data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-11, December.
    6. Milne, Alistair & Parboteeah, Paul, 2016. "The Business Models and Economics of Peer-to-Peer Lending," ECRI Papers 11594, Centre for European Policy Studies.
    Full references (including those not matched with items on IDEAS)

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