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    政大典藏 > College of Commerce > Department of MIS > Theses >  Item 140.119/119335
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/119335

    Title: 應用類神經網路於學生微型信貸
    Application of Artificial Neural Networks to Student Microfinance
    Authors: 陳韋翰
    Chen, Wei-Han
    Contributors: 蔡瑞煌
    Tsaih, Rua-Huan
    Chen, Wei-Han
    Keywords: 微型信貸
    Inclusive finance
    Artificial neural networks
    Outlier detection
    Machine learning
    Date: 2018
    Issue Date: 2018-08-13 12:35:37 (UTC+8)
    Abstract: 普惠金融現在被視為金融業的重要領域,而小額信貸是普惠金融的基本形式。學生族群是處於金融領域的弱勢群體。人工神經網路是機器學習系統的其中之一。它具有學習能力,並且可以進一步推廣所預測的結果,它也適用於非線性問題的應用。

    Inclusive Finance is regarded as an important area of financial industry now day, and microfinance is a basic form of Inclusive Finance. Student group is an underprivileged group in financial field. Artificial Neural Networks is one of machine learning systems. It has the ability to learn, and it can further generalize the results to be predicted, and it is also suitable for applications in nonlinear problems.
    This study adapts the work of Tsaih and Wu (2017) to derive a mechanism for effective outlier detection and machine learning. To establish a neural network system using GPU equipment and machine learning tools - TensorFlow implementation. We sets up an experiment based on real dataset collected by online P2P Lending platform. We collect 200 students’ loan data from 2018/3/30~2018/4/7, then randomly choosing 140 data to do training, 60 data to be the testing set. The results show that the proposed mechanism is promising in outlier detection and machine learning.

    Index Terms — microfinance, Inclusive Finance, Artificial neural networks, outlier detection, machine learning
    Reference: English Reference
    1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., ... & Ghemawat, S. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.
    2. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Kudlur, M. (2016, November). TensorFlow: A System for Large-Scale Machine Learning. In OSDI (Vol. 16, pp. 265-283).
    3. Berger, S. C., & Gleisner, F. (2010). Emergence of financial intermediaries in electronic markets: The case of online P2P lending.
    4. Funk, B., Buerckner, D., Hilker, M., Kock, F., Lehmann, M., & Tiburtius, P. (1970). Online Peer-to-Peer Lending â   A Literature Review. The Journal of Internet Banking and Commerce, 16(2), 1-18.
    5. Christman, D. E. (2000). Multiple realities: Characteristics of loan defaulters at a two-year public institution. Community College Review, 27(4), 16-32.
    6. Freedman S., Jin, G.(2008), "Do Social Networks solve Information Problems for Peer-to-Peer Lending? Evidence from Prosper.com"
    7. Gross, J. P., Cekic, O., Hossler, D., & Hillman, N. (2009). What Matters in Student Loan Default: A Review of the Research Literature. Journal of Student Financial Aid, 39(1), 19-29.
    8. Hampshire, R. (2008). Group Reputation Effects in Peer-to-Peer Lending Markets: An Empirical Analysis from a Principle-Agent Perspective. mimeo.
    9. Harrast, S. A. (2004). Undergraduate borrowing: A study of debtor students and their ability to retire undergraduate loans. Journal of Student Financial Aid, 34(1), 21-37.
    10. Herr, E., & Burt, L. (2005). Predicting student loan default for the University of Texas at Austin. Journal of Student Financial Aid, 35(2), 27-49.
    11. Lavoie, F., Pozzebon, M., & Gonzalez, L. (2011). Challenges for inclusive finance expansion: The case of CrediAmigo, a Brazilian MFI. Management international/International Management/Gestión Internacional, 15(3), 57-69.
    12. Owens, J. D., Houston, M., Luebke, D., Green, S., Stone, J. E., & Phillips, J. C. (2008). GPU computing. Proceedings of the IEEE, 96(5), 879-899.
    13. Tsaih, R. H. and J. Z. Wu. (2017). Application of machine learning to predicting the returns of carry trade
    14. Tsaih, R. H. and M. C. Lian. (2017). Exploring the timeliness requirement of artificial neural networks in network traffic anomaly detection
    15. Tsaih, R. H. and T. C. Cheng. (2009). A resistant learning procedure for coping with outliers, Annals of Mathematics and Artificial Intelligence, vol. 57, no. 2, pp. 161-180.
    16. Rousseeuw, P. J., & Van Driessen, K. (2006). Computing LTS regression for large data sets. Data mining and knowledge discovery, 12(1), 29-45.
    17. Stiglitz, J. E., & Weiss, A. (1981). Credit rationing in markets with imperfect information. The American economic review, 71(3), 393-410.
    18. Topa, T., Karwowski, A., & Noga, A. (2011). Using GPU with CUDA to accelerate MoM-based electromagnetic simulation of wire-grid models. IEEE Antennas and Wireless Propagation Letters, 10, 342-345.
    19. Tsaih, R. R. (1993). The softening learning procedure. Mathematical and computer modelling, 18(8), 61-64.
    20. Volkwein, J. F., & Cabrera, A. F. (1998). Who defaults on student loans? The effects of race, class, and gender on borrower behavior. In R. Fossey & M. Bateman (Eds.), Condemning students to debt: College loans and public policy (pp. 105-126). New York: Teachers College Press.
    21. Volkwein, J. F., & Szelest, B. P. (1995). Individual and campus characteristics associated with student loan default. Research in Higher Education, 36(1), 41-72.
    22. Waller, G. M., & Woodworth, W. (2001). Microcredit as a Grass‐Roots Policy for International Development. Policy Studies Journal, 29(2), 267-282.
    Chinese Reference
    1. 李坤霖,(2017),應用倒傳遞類神經網路於P2P借貸投資報酬率預測之研究——以Lending Club為例,資訊管理學系碩士論文
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105356030
    Data Type: thesis
    DOI: 10.6814/THE.NCCU.MIS.016.2018.A05
    Appears in Collections:[Department of MIS] Theses

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