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    题名: 應用類神經網路於學生微型信貸
    Application of Artificial Neural Networks to Student Microfinance
    作者: 陳韋翰
    Chen, Wei-Han
    贡献者: 蔡瑞煌
    Tsaih, Rua-Huan
    陳韋翰
    Chen, Wei-Han
    关键词: 微型信貸
    普惠金融
    人工神經網路
    異常值檢測
    機器學習
    Microfinance
    Inclusive finance
    Artificial neural networks
    Outlier detection
    Machine learning
    日期: 2018
    上传时间: 2018-08-13 12:35:37 (UTC+8)
    摘要: 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。
    這項研究調整了蔡瑞煌教授以及吳佳真研究生的研究,以推導有效的異常值檢測和機器學習機制。使用GPU設備和機器學習工具建立神經網絡系統藉由TensorFlow實現。我們基於在線P2P借貸平台收集的真實數據集進行實驗。從2018/3/30〜2018/4/7中收集到200個學生的貸款數據,隨機選取140個數據做訓練,60個數據作為測試集。結果表明,所提出的機制在異常值檢測和機器學習方面是有前途的且有效果的。

    關鍵詞:微型信貸、普惠金融、人工神經網路、異常值檢測、機器學習
    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
    參考文獻: 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
    描述: 碩士
    國立政治大學
    資訊管理學系
    105356030
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0105356030
    数据类型: thesis
    DOI: 10.6814/THE.NCCU.MIS.016.2018.A05
    显示于类别:[資訊管理學系] 學位論文

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