Aviso: para depositar documentos, por favor, inicia sesión e identifícate con tu cuenta de correo institucional de la UCM con el botón MI CUENTA UCM. No emplees la opción AUTENTICACIÓN CON CONTRASEÑA Disculpen las molestias.
 

Risk-Return modelling in the P2P lending market: Trends, Gaps, Recommendations and future directions

Simple item page
dc.contributor.authorAriza Garzón, Miller Janny
dc.contributor.authorCamacho Miñano, Juana María Del Mar
dc.contributor.authorSegovia Vargas, María Jesús
dc.contributor.authorArroyo Gallardo, Javier
dc.date.accessioned2023-06-17T09:06:24Z
dc.date.available2023-06-17T09:06:24Z
dc.date.issued2020
dc.description.abstractThe proposal for new financial products has been accompanied by new tools for risk management and profit in the Peer-to-Peer (P2P) lending market, one market in evolution, as an alternative for traditional investment and financing. For understanding this development, a systematic literature review and a bibliometric analysis of 104 papers published in the Web of Science database in the last decade are carried out using Scimat software. Our aim is to identify methodological elements, modelling components, analysis of variables and business aspects that generate opportunities for deepening its development and application. Developments of algorithms of artificial intelligence (AI) and machine learning (ML) support most of new proposals. Regulators, supervisors and users tend to increasingly seek these new alternatives in a natural project of financial digitalization demanded by technological advances, innovation and market development. Based on this study, recommendations in future research directions are provided for researchers.
dc.description.departmentDepto. de Administración Financiera y Contabilidad
dc.description.departmentDepto. de Economía Financiera y Actuarial y Estadística
dc.description.departmentDepto. de Ingeniería de Software e Inteligencia Artificial (ISIA)
dc.description.facultyFac. de Estudios Estadísticos
dc.description.facultyFac. de Ciencias Económicas y Empresariales
dc.description.facultyFac. de Informática
dc.description.refereedTRUE
dc.description.sponsorshipEuropean Commission
dc.description.sponsorshipCOST (European Cooperation in Science and Technology)
dc.description.sponsorshipUniversidad Complutense de Madrid
dc.description.statussubmitted
dc.eprint.idhttps://eprints.ucm.es/id/eprint/65821
dc.identifier.urihttps://hdl.handle.net/20.500.14352/8182
dc.language.isoeng
dc.relation.projectIDFIN-TECH (825215)
dc.relation.projectID(COST Action 19130)
dc.relation.projectID(Grant PR87/19-22586)
dc.rights.accessRightsopen access
dc.subject.keywordRisk management modelling
dc.subject.keywordCredit risk modelling
dc.subject.keywordProfit and invest modelling
dc.subject.keywordP2P lending
dc.subject.keywordMachine learning
dc.subject.keywordLiterature review
dc.subject.keywordBibliometric analysis.
dc.subject.ucmInteligencia artificial (Informática)
dc.subject.ucmEconometría (Estadística)
dc.subject.ucmFinanzas
dc.subject.unesco1203.04 Inteligencia Artificial
dc.subject.unesco5302.04 Estadística Económica
dc.titleRisk-Return modelling in the P2P lending market: Trends, Gaps, Recommendations and future directions
dc.typejournal article
dcterms.referencesAhelegbey, D. F., Giudici, P., and Hadji-Misheva, B. (2019). Latent factor models for credit scoring in P2P systems. Physica A-Statistical Mechanics and Its Applications, 522, 112–121. https://doi.org/10.1016/j.physa.2019.01.130 Amalia, N., Dalimunthe, Z., and Triono, R. A. (2019). The Effect of Lender’s Protection on Online Peer-to-Peer Lending in Indonesia. In K. S. Soliman (Ed.), Education Excellence and Innovation Management through Vision 2020. Aria, M. and Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics. Elsevier., 11(4), 959–975. Ariza-Garzón, M. J., Arroyo, J., Caparrini, A., and Segovia-Vargas, M. (2020). Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending. IEEE Access, 8, 64873–64890. https://doi.org/10.1109/ACCESS.2020.2984412 Bachmann, A., Becker, A., Buerckner, D., Hilker, M., Kock, F., Lehmann, M., Tiburtius, P., and Funk, B. (2011). Online peer-to-peer lending - A literature review. In Journal of Internet Banking and Commerce. Bae, J. K. (2018). A Study on the Determinant Factors of P2P Loans and Activation Factors of P2P Lending Market - P2p. Logos Management Review, 16(2), 21–36. Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/https://doi.org/10.1016/j.inffus.2019.12.012 Bastani, K., Asgari, E., and Namavari, H. (2019). Wide and deep learning for peer-to-peer lending. Expert Systems with Applications, 134, 209–224. https://doi.org/10.1016/j.eswa.2019.05.042 Birkle, C., Pendlebury, D. A., Schnell, J., and Adams, J. (2020). Web of Science as a data source for research on scientific and scholarly activity. Quantitative Science Studies, 1(1), 363–376. https://doi.org/10.1162/qss_a_00018 Boiko Ferreira, L. E., Barddal, J. P., Enembreck, F., and Gomes, H. M. (2017). Improving Credit Risk Prediction in Online Peer-to-Peer {(P2P)} Lending Using Imbalanced Learning Techniques. In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (pp. 175–181). https://doi.org/10.1109/ictai.2017.00037 Bussmann, N., Giudici, P., Marinelli, D., and Papenbrock, J. (2020). Explainable AI in Credit Risk Management. Frontiers in Artifical Intelligence. Artifical Intelligence in Finance. https://doi.org/10.3389/frai.2020.00026 Byanjankar, A. (2017). Predicting Credit Risk in Peer-to-Peer Lending with Survival Analysis. In 2017 Ieee Symposium Series on Computational Intelligence. Byanjankar, A., Heikkila, M., and Mezei, J. (2015). Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach. In 2015 Ieee Symposium Series on Computational Intelligence. https://doi.org/10.1109/ssci.2015.109 Cai, S., and Zhang, J. (2020). Exploration of credit risk of P2P platform based on data mining technology. Journal of Computational and Applied Mathematics, 372. https://doi.org/10.1016/j.cam.2020.112718 Calabrese, R., Osmetti, S. A., and Zanin, L. (2019). A joint scoring model for peer-to-peer and traditional lending: a bivariate model with copula dependence. Journal of the Royal Statistical Society Series A-Statistics in Society, 182(4), 1163–1188. https://doi.org/10.1111/rssa.12523 Canfield R, C. E. (2018). Determinants of Default in P2P Lending: The Mexican Case. Independent Journal of Management and Production, 9(1), 1–24. https://doi.org/10.14807/ijmp.v9i1.537 Carvalho, D. V, Pereira, E. M., & Cardoso, J. S. (2019). Machine Learning Interpretability: A Survey on Methods and Metrics. Electronics, 8(8). https://doi.org/10.3390/electronics8080832 Chen, C., Dong, M. C., Liu, N., and Sriboonchitta, S. (2019). Inferences of default risk and borrower characteristics on P2P lending. North American Journal of Economics and Finance, 50. https://doi.org/10.1016/j.najef.2019.101013 Chen, D., Li, X., and Lai, F. (2016). Gender discrimination in online peer-to-peer credit lending: evidence from a lending platform in China. Electronic Commerce Research, 17(4), 553–583. https://doi.org/10.1007/s10660-016-9247-2 Chen, Y. (2017). Research on the Credit Risk Assessment of Chinese Online Peer-to-peer Lending Borrower on Logistic Regression Model. In 3rd Asian Pacific Conference on Energy, Environment and Sustainable Development (pp. 216–221). Cho, P., Chang, W., and Song, J. W. (2019). Application of Instance-Based Entropy Fuzzy Support Vector Machine in Peer-To-Peer Lending Investment Decision. IEEE Access, 7, 16925–16939. https://doi.org/10.1109/access.2019.2896474 Claessens, S., Frost, J., Turner, G., and Zhu, F. (2018). Fintech credit markets around the world: size, drivers and policy issues. BIS Quarterly Review. https://www.bis.org/publ/qtrpdf/r_qt1809e.pdf Cummins, M., Lynn, T., Mac an Bhaird, C., and Rosati, P. (2019). Addressing Information Asymmetries in Online Peer-to-Peer Lending. In Disrupting Finance. https://doi.org/10.1007/978-3-030-02330-0_2 Ding, H., Zhang, P., Lu, T., Gu, H., and Gu, N. (2017). Credit Scoring Using Ensemble Classification Based on Variable Weighting Clustering. In W. Shen, P. Antunes, N. H. Thuan, J. P. Barthes, J. Luo, and J. Yong (Eds.), 2017 Ieee 21st International Conference on Computer Supported Cooperative Work in Design. Duan, J. (2019). Financial system modeling using deep neural networks (DNNs) for effective risk assessment and prediction. Journal of the Franklin Institute-Engineering and Applied Mathematics, 356(8), 4716–4731. https://doi.org/10.1016/j.jfranklin.2019.01.046 Durovic, A. (2017). Estimating Probability of Default on Peer to Peer Market - Survival Analysis Approach. Journal of Central Banking Theory and Practice, 6(2), 149–167. https://doi.org/10.1515/jcbtp-2017-0017 Elango, B., and Rajendran, P. (2012). Authorship trends and collaboration pattern in the marine sciences literature: a scientometric study. International Journal of Information Dissemination and Technology. Emekter, R., Tu, Y., Jirasakuldech, B., and Lu, M. (2015). Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending. Applied Economics, 47(1), 54–70. https://doi.org/10.1080/00036846.2014.962222 Financial Stability Board. (2017). Artificial intelligence and machine learning in financial services. Market developments and financial stability implications. In FSB. Financial Stability Board. internal-pdf://0180047707/P011117.pdf Fu, X., Zhang, S., Chen, J., Ouyang, T., and Wu, J. (2019). A Sentiment-Aware Trading Volume Prediction Model for P2P Market Using LSTM. Ieee Access, 7, 81934–81944. https://doi.org/10.1109/access.2019.2923637 Gao, Y., Sun, J., and Zhou, Q. (2017). Forward looking vs backward looking An empirical study on the effectiveness of credit evaluation system in China’s online P2P lending market. China Finance Review International, 7(2), 228–248. https://doi.org/10.1108/cfri-07-2016-0089 Gao, Y., Yu, S., Chen, M., and Shiue, Y. (2020). A 2020 perspective on “The performance of the P2P finance industry in China.” Electronic Commerce Research and Applications. https://doi.org/10.1016/j.elerap.2020.100940 Ge, R., Feng, J., Gu, B., and Zhang, P. (2017). Predicting and Deterring Default with Social Media Information in Peer-to-Peer Lending. Journal of Management Information Systems, 34(2), 401–424. https://doi.org/10.1080/07421222.2017.1334472 Giudici, P., Hadji-Misheva, B., and Spelta, A. (2020). Network based credit risk models. Quality Engineering. https://doi.org/10.1080/08982112.2019.1655159 Gourieroux, C., and Lu, Y. (2019). Least impulse response estimator for stress test exercises. Journal of Banking and Finance, 103, 62–77. https://doi.org/10.1016/j.jbankfin.2019.03.021 Greiner, M. E., and Wang, H. (2010). Building Consumer-to-Consumer Trust in E-Finance Marketplaces: An Empirical Analysis. International Journal of Electronic Commerce, 15(2), 105–136. https://doi.org/10.2753/jec1086-4415150204 Guo, G., Zhu, F., Chen, E., Liu, Q., Wu, L., and Guan, C. (2016). From Footprint to Evidence: An Exploratory Study of Mining Social Data for Credit Scoring. Acm Transactions on the Web, 10(4). https://doi.org/10.1145/2996465 Hadji-Misheva, B. H., Giudici, P., Pediroda, V., and Ieee. (2018). Network-based models to improve credit scoring accuracy. In 2018 Ieee 5th International Conference on Data Science and Advanced Analytics (pp. 623–630). https://doi.org/10.1109/dsaa.2018.00080 Herzenstein, M., Sonenshein, S., and Dholakia, U. M. (2011). Tell Me a Good Story and I May Lend You Money: The Role of Narratives in Peer-to-Peer Lending Decisions. Journal of Marketing Research, 48, S138–S149. https://doi.org/10.1509/jmkr.48.SPL.S138 Hodge, D. R., and Lacasse, J. R. (2011). Ranking disciplinary journals with the Google Scholar h-index: A new tool for constructing cases for tenure, promotion, and other professional decisions. Journal of Social Work Education, 47(3), 579–596. https://doi.org/10.5175/jswe.2011.201000024 Ji, X., Yu, L., and Fu, J. (2020). Evaluating Personal Default Risk in P2P Lending Platform: Based on Dual Hesitant Pythagorean Fuzzy TODIM Approach. Mathematics, 8(1). https://doi.org/10.3390/math8010008 Jiang, C. Q., Wang, Z., Wang, R. Y., and Ding, Y. (2018). Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending. Annals of Operations Research, 266(1–2), 511–529. https://doi.org/10.1007/s10479-017-2668-z Jiang, C., Wang, Z., and Zhao, H. (2019). A prediction-driven mixture cure model and its application in credit scoring. European Journal of Operational Research, 277(1), 20–31. https://doi.org/10.1016/j.ejor.2019.01.072 Jin, Y., and Zhu, Y. (2015). A data-driven approach to predict default risk of loan for online Peer-to-Peer {(P2P)} lending. In G. Tomar (Ed.), 2015 Fifth International Conference on Communication Systems and Network Technologies (pp. 609–613). https://doi.org/10.1109/csnt.2015.25 Kim, A., and Cho, S.-B. (2019). An ensemble semi-supervised learning method for predicting defaults in social lending. Engineering Applications of Artificial Intelligence, 81, 193–199. https://doi.org/10.1016/j.engappai.2019.02.014 Kim, J.-Y., and Cho, S.-B. (2019a). Predicting repayment of borrows in peer-to-peer social lending with deep dense convolutional network. Expert Systems, 36(4). https://doi.org/10.1111/exsy.12403 Kim, J.-Y., and Cho, S.-B. (2019b). Towards Repayment Prediction in Peer-to-Peer Social Lending Using Deep Learning. Mathematics, 7(11). https://doi.org/10.3390/math7111041 Koseoglu, M. A. (2016). Growth and structure of authorship and co-authorship network in the strategic management realm: Evidence from the Strategic Management Journal. BRQ Business Research Quarterly, 19 (3), 153–170. https://doi.org/10.1016/j.brq.2016.02.001 Kumar, V. L., Natarajan, S., Keerthana, S., Chinmayi, K. M., Lakshmi, N., and Ieee. (2016). Credit Risk Analysis in Peer-to-Peer Lending System. In 2016 Ieee International Conference on Knowledge Engineering and Applications. Lee, Y.-W., Chen, S., Yu, T., and Ieee. (2017). Analysis of the Impact of Collateral on Peer-to-Peer Lending. In 2017 Ieee/Sice International Symposium on System Integration (pp. 77–82). Li, H., Zhang, Y., Zhang, N., and Jia, H. (2016). Detecting the abnormal lenders from P2P lending data. In H. Lee, Y. Shi, J. Lee, F. Cordova, I. Dzitac, G. Kou, and J. Li (Eds.), Promoting Business Analytics and Quantitative Management of Technology: 4th International Conference on Information Technology and Quantitative Management (Vol. 91, pp. 357–361). https://doi.org/10.1016/j.procs.2016.07.095 Li, L., Feng, Y., Lv, Y., Cong, X., Fu, X., and Qi, J. (2019). Automatically Detecting Peer-to-Peer Lending Intermediary Risk-Top Management Team Profile Textual Features Perspective. Ieee Access, 7, 72551–72560. https://doi.org/10.1109/access.2019.2919727 Li, W., Ding, S., Chen, Y., and Yang, S. (2018). Heterogeneous Ensemble for Default Prediction of Peer-to-Peer Lending in China. Ieee Access, 6, 54396–54406. https://doi.org/10.1109/access.2018.2810864 Li, W., Ding, S., Wang, H., Chen, Y., and Yang, S. (2020). Heterogeneous ensemble learning with feature engineering for default prediction in peer-to-peer lending in China. World Wide Web-Internet and Web Information Systems, 23(1), 23–45. https://doi.org/10.1007/s11280-019-00676-y Li, Y., Hao, A., Zhang, X., and Xiong, X. (2018). Network topology and systemic risk in Peer-to-Peer lending market. Physica A-Statistical Mechanics and Its Applications, 508, 118–130. https://doi.org/10.1016/j.physa.2018.05.083 Lin, X. C., Li, X. L., and Zheng, Z. (2017). Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China. Applied Economics, 49(35), 3538–3545. https://doi.org/10.1080/00036846.2016.1262526 Liu, C., and Yan, J. (2016). Researches on Risks and Precautions of Chinese P2P Lending. In M. Kuek and R. Zhao (Eds.), Proceedings of the 23rd International Business Annual Conference. Liu, H., Zhou, S., and Yang, W. (2019). Research on Intelligent Inter net Financial Investment Model. In R. Su (Ed.), 2019 International Conference on Image and Video Processing, and Artificial Intelligence (Vol. 11321). https://doi.org/10.1117/12.2539006 Liu, Y., Zhou, Q., Zhao, X., and Wang, Y. (2018). Can Listing Information Indicate Borrower Credit Risk in Online Peer-to-Peer Lending? Emerging Markets Finance and Trade, 54(13), 2982–2994. https://doi.org/10.1080/1540496x.2018.1427061 Ma, L., Zhao, X., Zhou, Z., and Liu, Y. (2018). A new aspect on P2P online lending default prediction using meta-level phone usage data in China. Decision Support Systems, 111, 60–71. https://doi.org/10.1016/j.dss.2018.05.001 Ma, X., Sha, J., Wang, D., Yu, Y., Yang, Q., and Niu, X. (2018). Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning. Electronic Commerce Research and Applications, 31, 24–39. https://doi.org/10.1016/j.elerap.2018.08.002 Malekipirbazari, M., and Aksakalli, V. (2015). Risk assessment in social lending via random forests. Expert Systems with Applications, 42(10), 4621–4631. https://doi.org/10.1016/j.eswa.2015.02.001 Milne, A., and Parboteeah, P. (2016). The Business Models and Economics of Peer-to-Peer Lending. Centre for European Policy Studies, 17, 36. internal-pdf://131.209.49.252/ECRI RR17 P2P Lending.pdf NV - Technical Report Namvar, A., Naderpour, M., and Ieee. (2018). Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model. In 2018 Ieee International Conference on Fuzzy Systems. Nguyen Truong, T., Khuat Thanh, S., Ngo Thi Thu, T., Nguyen Ha, N., and Tran Manh, D. (2019). Improve Risk Prediction in Online Lending (P2P) Using Feature Selection and Deep Learning. International Journal of Computer Science and Network Security, 19(11), 216–222. Niu, B., Ren, J., and Li, X. (2019). Credit Scoring Using Machine Learning by Combing Social Network Information: Evidence from Peer-to-Peer Lending. Information, 10(12). https://doi.org/10.3390/info10120397 Park, S., and Daeseon, C. (2019). A Study on P2P Lending Deadline Prediction Model based on Machine Learning. Journal of KIISE, 46(2), 174–183. Pierrakis, Y. (2019). Peer-to-peer lending to businesses: Investors’ characteristics, investment criteria and motivation. International Journal of Entrepreneurship and Innovation, 20(4), 239–251. https://doi.org/10.1177/1465750319842528 Pokorna, M., and Sponer, M. (2016). Social lending and its risks. In S. Kapounek and V. Krutilova (Eds.), 19th International Conference Enterprise and Competitive Environment 2016 (Vol. 220, pp. 330–337). https://doi.org/10.1016/j.sbspro.2016.05.506 Pur, S., Huesig, S., Mann, H.-G., and Schmidhammer, C. (2014). How to Analyze the Disruptive Potential of Business Model Innovation in Two-Sided Markets?: The Case of Peer to Peer Lending Marketplaces in Germany. In D. F. Kocaoglu, T. R. Anderson, T. U. Daim, D. C. Kozanoglu, K. Niwa, and G. Perman (Eds.), 2014 Portland International Conference on Management of Engineering and Technology (pp. 693–709). Ren, K., Malik, A., and Acm. (2019). Investment Recommendation System for Low-Liquidity Online Peer to Peer Lending (P2PL) Marketplaces. In Proceedings of the Twelfth Acm International Conference on Web Search and Data Mining. https://doi.org/10.1145/3289600.3290959 Rodrigues, D. S., Brasil, A. R. A., Costa, M. B., Komati, K. S., Pinto, L. A., and Acm. (2018). A Comparative Analysis of Loan Requests Classification Algorithms in a Peer-to-Peer Lending Platform. In Proceedings of the 14th Brazilian Symposium on Information Systems. https://doi.org/10.1145/3229345.3229390 ROFIEG, Expert Group on Regulatory Obstacles to Financial Innovation .(2019). Thirty recommendations on regulation, innovation and finance (Issue December). Final Report to the European Commission. https://ec.europa.eu/info/files/191113-report-expert-group-regulatory-obstacles-financial-innovation_en Rosavina, M., Rahadi, R. A., Kitri, M. L., Nuraeni, S., and Mayangsari, L. (2019). P2P lending adoption by SMEs in Indonesia. Qualitative Research in Financial Markets, 11(2), 260–279. https://doi.org/10.1108/qrfm-09-2018-0103 Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x Serrano-Cinca, C., and Gutierrez-Nieto, B. (2016). The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending. Decision Support Systems, 89, 113–122. https://doi.org/10.1016/j.dss.2016.06.014 Serrano-Cinca, C., Gutierrez-Nieto, B., and Lopez-Palacios, L. (2015). Determinants of Default in P2P Lending. Plos One, 10(10). https://doi.org/10.1371/journal.pone.0139427 Soo H., A. (2016). FinTech supporting Government’s Policy, its Implementing Measures and Legal Institution in UK- focused on the Payment Service Industry. Kangwon Law Review, 49, 179–219. https://doi.org/10.18215/kwlr.2016.49..179 Stofa, T. (2017). Analysis of Repayment Failures in P2P Lending. In B. Gavurova and M. Soltes (Eds.), Central European Conference in Finance and Economics. Sungbok, L. (2018). Study on the Financial Intermediary Role of P2P Lending Platform - P2p. Journal of Money and Finance, 32(2), 21–62. https://doi.org/10.21023/jmf.32.2.2 Tan, Y., Zheng, X., Zhu, M., Wang, C., Zhu, Z., and Yu, L. (2017). Investment Recommendation with Total Capital Value Maximization in Online P2P Lending. In O. Hussain, L. Jiang, X. Fei, C. W. Lan, and K. M. Chao (Eds.), 2017 Ieee 14th International Conference on E-Business Engineering. https://doi.org/10.1109/icebe.2017.32 Tao, Q., Dong, Y., and Lin, Z. (2017). Who can get money? Evidence from the Chinese peer-to-peer lending platform. Information Systems Frontiers, 19(3), 425–441. https://doi.org/10.1007/s10796-017-9751-5 Uddin, M. J., Vizzari, G., Bandini, S., and Imam, M. O. (2018). A case-based reasoning approach to rate microcredit borrower risk in online Kiva P2P lending model. Data Technologies and Applications, 52(1), 58–83. https://doi.org/10.1108/dta-02-2017-0009 Van-Sang, H., Dang-Nhac, L., Choi, G. S., Ha-Nam, N., and Yoon, B. (2019). Improving Credit Risk Prediction in Online Peer-to-Peer {(P2P)} Lending Using Feature selection with Deep learning. 2019 21st International Conference on Advanced Communication Technology, 6(1), 20–31. https://doi.org/10.23919/ICACT.2019.8701943 van Eck, N. J., and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84 (2), 523–538. https://doi.org/10.1007/s11192-009-0146-3 van Eck, N. J., and Waltman, L. (2014). Visualizing Bibliometric Networks. In Y. Ding, R. Rousseau, and D. Wolfram (Eds.), Measuring Scholarly Impact: Methods and Practice (pp. 285–320). Springer International Publishing. https://doi.org/10.1007/978-3-319-10377-8_13 van Eck, N. J., & Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111, 1053–1070. https://doi.org/10.1007/s11192-017-2300-7 Wan, J., Zhang, H., Zhu, X., Sun, X., and Li, G. (2019). Research on Influencing Factors of P2P Network Loan Prepayment Risk Based on Cox Proportional Hazards. In E. HerreraViedma, Y. Shi, D. Berg, J. Tien, F. J. Cabrerizo, and J. Li (Eds.), 7th International Conference on Information Technology and Quantitative Management (Vol. 162, pp. 842–848). https://doi.org/10.1016/j.procs.2019.12.058 Wang, C., Han, D., Liu, Q., and Luo, S. (2019). A Deep Learning Approach for Credit Scoring of Peer-to-Peer Lending Using Attention Mechanism LSTM. IEEE Access, 7, 2161–2168. https://doi.org/10.1109/access.2018.2887138 Wang, H., Kou, G., and Peng, Y. (2018). Cost-sensitive Classifiers in Credit Rating A Comparative Study on P2P Lending. In I. Dzitac, F. G. Filip, M. J. Manolescu, S. Dzitac, H. Oros, and D. Dzitac (Eds.), 2018 7th International Conference on Computers Communications and Control. Wang, L. (2018). Supervision of Peer-to-Peer Lending in China. In J. Liu and K. L. Teves (Eds.), Proceedings of the 2018 2nd International Conference on Education, Economics and Management Research (Vol. 182, pp. 291–293). Wang, S., Qi, Y., Fu, B., and Liu, H. (2016). Credit Risk Evaluation Based on Text Analysis. International Journal of Cognitive Informatics and Natural Intelligence, 10(1), 1–11. https://doi.org/10.4018/ijcini.2016010101 Wang, Z., Jiang, C., Ding, Y., Lyu, X., and Liu, Y. (2018). A Novel behavioral scoring model for estimating probability of default over time in peer-to-peer lending. Electronic Commerce Research and Applications, 27, 74–82. https://doi.org/10.1016/j.elerap.2017.12.006 Wang, Z., Jiang, C., Zhao, H., and Ding, Y. (2020). Mining Semantic Soft Factors for Credit Risk Evaluation in Peer-to-Peer Lending. Journal of Management Information Systems, 37(1), 282–308. https://doi.org/10.1080/07421222.2019.1705513 Wei, X., Gotoh, J., and Uryasev, S. (2018). Peer-To-Peer Lending: Classification in the Loan Application Process. Risks, 6(4). https://doi.org/10.3390/risks6040129 Wu, C., Zhang, D., and Wang, Y. (2018). Evaluating the risk performance of online peer-to-peer lending platforms in China. Journal of Risk Model Validation, 12(2), 63–87. https://doi.org/10.21314/jrmv.2018.187 Xia, L., and Li, J. (2016). Analysis on Credit Risk Assessment of P2P. In E. Qi, J. Shen, and R. Dou (Eds.), Proceedings of the 22nd International Conference on Industrial Engineering and Engineering Management: Core Theory and Applications of Industrial Engineering. https://doi.org/10.2991/978-94-6239-180-2_86 Xia, Y. (2019). A Novel Reject Inference Model Using Outlier Detection and Gradient Boosting Technique in Peer-to-Peer Lending. IEEE Access, 7, 92893–92907. https://doi.org/10.1109/access.2019.2927602 Xia, Y., He, L., Li, Y., Liu, N., and Ding, Y. (2019). Predicting loan default in peer-to-peer lending using narrative data. Journal of Forecasting. https://doi.org/10.1002/for.2625 Xia, Y., Liu, C., and Liu, N. (2017). Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending. Electronic Commerce Research and Applications, 24, 30–49. https://doi.org/10.1016/j.elerap.2017.06.004 Xia, Y., Yang, X., and Zhang, Y. (2018). A rejection inference technique based on contrastive pessimistic likelihood estimation for P2P lending. Electronic Commerce Research and Applications, 30, 111–124. https://doi.org/10.1016/j.elerap.2018.05.011 Xinmin, W., Peng, H., Akram, U., Yan, M., and Attiq, S. (2019). The effect of successful borrowing times on behavior of investors: An empirical investigation of the P2P online lending market. Human Systems Management, 38(4), 385–393. https://doi.org/10.3233/hsm-190517 Xiong, J. (2018). Risk Identification and Monitoring Model of Online P2P Lending. In J. Liu and K. L. Teves (Eds.), Proceedings of the 2018 2nd International Conference on Education, Economics and Management Research (Vol. 182, pp. 360–363). Xu, J., and Chau, M. (2018). Cheap Talk? The Impact of Lender-Borrower Communication on Peer-to-Peer Lending Outcomes. Journal of Management Information Systems, 35(1), 53–85. https://doi.org/10.1080/07421222.2018.1440776 Xu, J., Chen, D., and Chau, M. (2016). Identifying Features for Detecting Fraudulent Loan Requests on P2P Platforms. In L. Zhou, L. Kaati, W. Mao, and G. A. Wang (Eds.), Ieee International Conference on Intelligence and Security Informatics: Cybersecurity and Big Data. Xu, J. J., Lu, Y., and Chau, M. (2015). P2P Lending Fraud Detection: A Big Data Approach. In M. Chau, G. A. Wang, and H. Chen (Eds.), Intelligence and Security Informatics, Paisi 2015 (Vol. 9074, pp. 71–81). https://doi.org/10.1007/978-3-319-18455-5_5 Xu, L., and Zhang, Y. (2017). A Credit Rating Model for Online P2P Lending Based on Analytic Hierarchy Process. In J. Xu, A. Hajiyev, S. Nickel, and M. Gen (Eds.), Proceedings of the Tenth International Conference on Management Science and Engineering Management (Vol. 502, pp. 537–549). https://doi.org/10.1007/978-981-10-1837-4_46 Yan, Y., Lv, Z., and Hu, B. (2017). Building investor trust in the P2P lending platform with a focus on Chinese P2P lending platforms. Electronic Commerce Research, 18(2), 203–224. https://doi.org/10.1007/s10660-017-9255-x Yan, Y., Lv, Z., and Hu, B. (2016). Building Investor Trust in the P2P Lending Platform with a Focus on Chinese P2P Lending Platforms. 2016 International Conference on Identification, Information and Knowledge in the Internet of Things, 470–474. https://doi.org/10.1109/iiki.2016.15 Yang, X., Fan, W., Wang, L., Yang, S., and Wang, W. (2019). Risk Control of Online {P2P} Lending in {China} Based on Health Investment. Ekoloji, 28(107), 2013–2022. Yao, J., Chen, J., Wei, J., Chen, Y., and Yang, S. (2019). The relationship between soft information in loan titles and online peer-to-peer lending: evidence from RenRenDai platform. Electronic Commerce Research, 19(1), 111–129. https://doi.org/10.1007/s10660-018-9293-z Ye, X., Dong, L.-A., and Ma, D. (2018). Loan evaluation in P2P lending based on Random Forest optimized by genetic algorithm with profit score. Electronic Commerce Research and Applications, 32, 23–36. https://doi.org/10.1016/j.elerap.2018.10.004 Yuan, Z. N., Wang, Z. H., and Xu, H. (2018). Credit Risk Assessment of Peer-to-Peer Lending Borrower Utilizing {BP} Neural Network. In L. Barolli, M. Zhang, and X. A. Wang (Eds.), Advances in Internetworking, Data and Web Technologies, Eidwt-2017 (Vol. 6, pp. 22–33). https://doi.org/10.1007/978-3-319-59463-7_3 Zang, D., Qi, M., and Fu, Y. (2015). The credit risk assessment of P2P lending based on BP neural network. In G. Lee (Ed.), Industrial Engineering and Management Science. Zhang, Y., Geng, X., and Jia, H. (2017). The Scoring Matrix Generation Method and Recommendation algorithm in P2P Lending. In R. Bahsoon and Z. Chen (Eds.), 2017 13th Ieee World Congress on Services (pp. 86–89). https://doi.org/10.1109/services.2017.22 Zhang, Y., Jia, H., Diao, Y., Hai, M., and Li, H. (2016). Research on Credit Scoring by fusing social media information in Online Peer-to-Peer Lending. In H. Lee, Y. Shi, J. Lee, F. Cordova, I. Dzitac, G. Kou, and J. Li (Eds.), Promoting Business Analytics and Quantitative Management of Technology: 4th International Conference on Information Technology and Quantitative Management (Vol. 91, pp. 168–174). https://doi.org/10.1016/j.procs.2016.07.055 Zhang, Y., Wang, D., Chen, Y., Shang, H., and Tian, Q. (2017). Credit Risk Assessment Based on Long Short-Term Memory Model. In D. S. Huang, K. H. Jo, and J. C. FigueroaGarcia (Eds.), Intelligent Computing Theories and Application, Icic 2017, Pt Ii (Vol. 10362, pp. 700–712). https://doi.org/10.1007/978-3-319-63312-1_62 Zhang, Y., Wang, D., Chen, Y., Zhao, Y., Shao, P., and Meng, Q. (2017). Credit Risk Assessment Based on Flexible Neural Tree Model. In F. Cong, A. Leung, and Q. Wei (Eds.), Advances in Neural Networks, Pt I (Vol. 10261, pp. 215–222). https://doi.org/10.1007/978-3-319-59072-1_26 Zhang, Y., Wang, X., Qian, Y., and Jia, H. (2016). The Research of Recommendation Algorithms in {P2P} Lending. Zhao, J. (2015). Research on Mathematical Model P2P Online Credit Risk Evaluation Based on Data Processing. In J. Wang and Y. Qin (Eds.), Proceedings of the 2015 International Conference on Education Technology, Management and Humanities Science (Vol. 27, pp. 897–900). Zhou, G., Zhang, Y., and Luo, S. (2018). P2P Network Lending, Loss Given Default and Credit Risks. Sustainability, 10(4). https://doi.org/10.3390/su10041010 Zhou, J., Li, W., Wang, J., Ding, S., and Xia, C. (2019). Default prediction in P2P lending from high-dimensional data based on machine learning. Physica A-Statistical Mechanics and Its Applications, 534. https://doi.org/10.1016/j.physa.2019.122370 Zhu, L., Qiu, D., Ergu, D., Ying, C., and Liu, K. (2019). A study on predicting loan default based on the random forest algorithm. In E. HerreraViedma, Y. Shi, D. Berg, J. Tien, F. J. Cabrerizo, and J. Li (Eds.), 7th International Conference on Information Technology and Quantitative Management (Vol. 162, pp. 503–513). https://doi.org/10.1016/j.procs.2019.12.017 Zhu, Z. (2018). Safety promise, moral hazard and financial supervision: Evidence from peer-to-peer lending. Finance Research Letters, 27, 1–5. https://doi.org/10.1016/j.frl.2018.07.002
dspace.entity.typePublication
relation.isAuthorOfPublication576c7b36-d6d7-4a3c-8f76-8d9d53d8eb48
relation.isAuthorOfPublicationce97b4c1-b2f9-47ba-80ef-29e0f4a261cd
relation.isAuthorOfPublication44aad0f9-4f64-46ee-a6b7-e9a317fa42fd
relation.isAuthorOfPublication4776976f-8d88-4992-bc6d-eea957d11041
relation.isAuthorOfPublication.latestForDiscovery576c7b36-d6d7-4a3c-8f76-8d9d53d8eb48

Download

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Paper p2p LR_First Version_mod.pdf
Size:
1.59 MB
Format:
Adobe Portable Document Format
downoload Download

Collections

Artículos