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Identifying Fintech risk through machine learning: analyzing the Q&A text of an online loan investment platform

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Abstract

Financial risks associated with Fintech have been increasing with its significant growth in recent years. Aiming at addressing the problem of identifying risks in online lending investment under a financial technology platform, we develop a Q&A text risk recognition model based on attention mechanism and Bi-directional Long Short-Term Memory. First, the Q&A pairing on the text data set is carried out, and the matching data set is selected for the next analysis. Secondly, the online loan investment platform is assessed by the named entity recognition of the question text. Finally, the risk level of the corresponding investment platform is evaluated based on the answer text. The experimental results show that the proposed model has achieved improved precision, recall, F1-score, and accuracy compared with other models. Our proposed model can be applied to identify the risks from the text posted on online loan investment platforms and can be used to guide investors’ investment and improve the management of financial technology platforms.

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References

  • Ahelegbey, D. F., Giudici, P., & Misheva, D. H. (2019). Latent factor models for credit scoring in P2P systems. Physica A: Statistical Mechanics and its Applications, 522, 112–121.

    ADS  Google Scholar 

  • Bag, S., Tiwari, M. K., & Chan, F. T. S. (2019). Predicting the consumer’s purchase intention of durable goods: An attribute-level analysis. Journal of Business Research, 94, 408–419.

    Google Scholar 

  • Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.

  • Brunner-Kirchmair, T. M., & Wiener, M. (2019). Knowledge is power-conceptualizing collaborative financial risk assessment. Journal of Risk Finance, 20(3), 226–248.

    Google Scholar 

  • Cai, L. Q., Zhou, S. T., Yan, X., & Yuan, R. D. (2019). A stacked BiLSTM neural network based on Coattention mechanism for question answering. Computational Intelligence and Neuroscience, 2019, 1–12.

    Google Scholar 

  • Chao, X. R., Kou, G., Peng, Y., & Alsaadi, F. E. (2018). Behavior monitoring methods for trade-based money laundering integrating macro and micro prudential regulation: A case from China. Technological and Economic Development of Economy, 25(6), 1081–1096.

    Google Scholar 

  • Chen, J. H., & Tsai, Y. C. (2020). Encoding candlesticks as images for pattern classification using convolutional neural networks. Financial Innovatio, 6, 1–19.

    CAS  Google Scholar 

  • Chen, M. A., Wu, Q. X., & Yang, B. Z. (2019). How valuable is Fintech innovation? Review of Financial Studies, 32(5), 2062–2106.

    Google Scholar 

  • Cheng, M. M., & Jin, X. (2019). What do Airbnb users care about? An analysis of online review comments. International Journal of Hospitality Management, 76, 58–70.

    Google Scholar 

  • Damel, P., Le Thi, H. A., & Peltre, N. (2016). The challenge in managing new financial risks: Adopting an heuristic or theoretical approach. Annals of Operations Research, 247(2), 581–598.

    MathSciNet  Google Scholar 

  • Delis, M., Iosifidi, M., & Tsionas, M. G. (2017). Endogenous bank risk and efficiency. European Journal of Operational Research, 260(1), 376–387.

    MathSciNet  Google Scholar 

  • Deng, D., Jing, L. P., Yu, J., & Sun, S. L. (2019). Sparse self-attention LSTM for sentiment lexicon construction. IEEE-ACM Transactions on Audio Speech and Language Processing, 27(11), 1777–1790.

    Google Scholar 

  • Dia, M., Takouda, P. M., & Golmohammadi, A. (2020). Assessing the performance of Canadian credit unions using a three-stage network bootstrap DEA. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03612-w.

    Article  Google Scholar 

  • Dranev, Y., Frolova, K., & Ochirova, E. (2019). The impact of Fintech M&A on stock returns. Research in International Business and Finance, 48, 353–364.

    Google Scholar 

  • Fan, H. J., Ma, Z. Y., Li, H. Q., Wang, D. S., & Liu, J. F. (2019). Enhanced answer selection in CQA using multi-dimensional features combination. Tsinghua Science and Technology, 24(3), 346–359.

    Google Scholar 

  • Franklin, S. L. (2015). Investment decisions in mobile telecommunications networks applying real options. Annals of Operations Research, 226(1), 201–220.

    MathSciNet  Google Scholar 

  • Grau-Carles, P., Doncel, L. M., & Sainz, J. (2019). Stability in mutual fund performance rankings: A new proposal. International Review of Economics & Finance, 61, 337–346.

    Google Scholar 

  • Guo, B., Zhang, C. X., Liu, J. M., & Ma, X. Y. (2019). Improving text classification with weighted word embeddings via a multi-channel TextCNN model. Neurocomputing, 363, 366–374.

    Google Scholar 

  • Haddad, C., & Hornuf, L. (2019). The emergence of the global Fintech market: Economic and technological determinants. Small Business Economics, 53(1), 81–105.

    Google Scholar 

  • Hu, R. C., Liu, M., He, P. P., & Ma, Y. (2019a). Can investors on P2P lending platforms identify default risk? International Journal of Electronic Commerce, 23(1), 63–84.

    CAS  Google Scholar 

  • Hu, Y., Mao, H., & McKenzie, G. (2019b). A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements. International Journal of Geographical Information Science, 33(4), 714–738.

    Google Scholar 

  • Huang, L. W., Jlang, B. T., Lv, S. Y., Liu, Y. B., & Li, D. Y. (2018). Survey on deep learning based recommender systems. Chinese Journal of Computers, 41(7), 1619–1647.

    MathSciNet  Google Scholar 

  • Jang, M., Seo, S., & Kang, P. (2019). Recurrent neural network-based semantic variational autoencoder for sequence-to-sequence learning. Information Sciences, 490, 59–73.

    Google Scholar 

  • Jiang, C., Wang, Z., Wang, R., & 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.

    MathSciNet  Google Scholar 

  • Jones, E., & Knaack, P. (2019). Global financial regulation: Shortcomings and reform options. Global Policy, 10(2), 193–206.

    Google Scholar 

  • Kou, G., Chao, X. R., Peng, Y., Alsaadi, F. E., & Herrera-Viedma, E. (2019). Machine learning methods for systemic risk analysis in financial sectors. Technological and Economic Development of Economy, 25(5), 716–742.

    Google Scholar 

  • Kumar, A., Singh, J. P., Dwivedi, Y. K., & Rana, N. P. (2020). A deep multi-modal neural network for informative Twitter content classification during emergencies. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03514-x.

    Article  Google Scholar 

  • Lee, S. (2017). Evaluation of mobile application in user’s perspective: Case of P2P lending apps in FinTech Industry. KSII Transactions on Internet and Information Systems, 11(2), 1105–1117.

    Google Scholar 

  • Lee, C., Kim, Y., Kim, Y. S., & Jang, J. (2019). Automatic disease annotation from radiology reports using artificial intelligence implemented by a recurrent neural network. Medical Physics and Informatics Original Research, 202(4), 734–740.

    Google Scholar 

  • Liu, Y., & Huang, L. H. (2020). Supply chain finance credit risk assessment using support vector machine-based ensemble improved with noise elimination. International Journal of Distributed Sensor Networks, 16(1), 1550147720903631.

    Google Scholar 

  • Liu, H., Li, C. X., & Yu, H. L. (2019). Agricultural Q&A system based on LSTM-CNN and Word2vec. Revista de la Facultad de Agronomia, 36(3), 543–551.

    Google Scholar 

  • Liu, L., & Wang, D. B. (2018). A review on named entity recognition. Journal of the China Society for Scientific and Technical Information, 37(03), 329–340.

    CAS  Google Scholar 

  • Lonkani, R., Changchit, C., Klaus, T., & Sampet, J. (2020). A comparative study of trust in mobile banking: An analysis of US and thai customers. Journal of Global Information Management (JGIM), 28(4), 95–119.

    Google Scholar 

  • Makarenkov, V., Rokach, L., & Shapira, B. (2019). Choosing the right word: Using bidirectional LSTM tagger for writing support systems. Engineering Applications of Artificial Intelligence, 84, 1–10.

    Google Scholar 

  • Marcotte, C. D., & Grigoriev, R. O. (2018). Systemic risk, financial markets, and performance of financial institutions. Annals of Operations Research, 262(2), 579–603.

    MathSciNet  Google Scholar 

  • Mishra, B. K., Rolland, E., Satpathy, A., & Moore, M. (2019). A framework for enterprise risk identification and management: The resource-based view. Managerial Auditing Journal, 34(2), 162–188.

    Google Scholar 

  • Mnih, V., Heess, N., Graves, A., & Kavukcuoglu, K. (2014). Recurrent models of visual attention. In Proceedings of the 27th international conference on neural information processing systems Vol. 2 (pp. 2204–2212). Cambridge, MA: MIT Press.

  • Morelli, G. (2019). Liquidity drops. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03285-0.

    Article  Google Scholar 

  • Mosteanu, N. R., & Faccia, A. (2020). Digital systems and new challenges of financial management: FinTech, XBRL, Blockchain and Cryptocurrencies. Quality-Access to Success, 21(174), 159–166.

    Google Scholar 

  • Na, S. H., Kim, H., Min, J., & Kim, K. (2019). Improving LSTM CRFs using character-based compositions for Korean named entity recognition. Computer Speech & Language, 54, 106–121.

    Google Scholar 

  • Pasricha, P., Selvamuthu, D., D’Amico, G., & Manca, R. (2020). Portfolio optimization of credit risky bonds: A semi-Markov process approach. Financial Innovatio, 6(3), 31–48.

    Google Scholar 

  • Peng, M., Yao, Y., Xie, Q. Q., & Gao, W. (2019). Knowledge representation learning for joint structural and textual embedding via attention-based CNN. Journal of Chinese Information Processing, 33(2), 51–58.

    Google Scholar 

  • Priem, R. (2020). Distributed ledger technology for securities clearing and settlement: Benefits, risks, and regulatory implications. Financial Innovation, 6(28), 3–13.

    Google Scholar 

  • Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30–40.

    Google Scholar 

  • Shrivastava, K., Kumar, S., & Jain, D. K. (2019). An effective approach for emotion detection in multimedia text data using sequence based convolutional neural network. Multimedia Tools and Applications, 78(20), 29607–29639.

    Google Scholar 

  • Singh, J. P., Irani, S., & Rana, N. P. (2017). Predicting the “helpfulness” of online consumer reviews. Journal of Business Research, 70, 346–355.

    Google Scholar 

  • Slusarczyk, B., & Grondys, K. (2019). Parametric conditions of high financial risk in the SME sector. Risks, 7(3), 84.

    Google Scholar 

  • Song, H. J., Kim, H. K., Kim, J. D., Park, C. Y., & Kim, Y. S. (2019). Inter-sentence segmentation of YouTube subtitles using long-short term memory (LSTM). Applied Sciences, 9(7), 1504.

    Google Scholar 

  • Tsionas, M. G. (2016). Parameters measuring bank risk and their estimation. European Journal of Operational Research, 250(1), 291–304.

    MathSciNet  Google Scholar 

  • Wang, X. Q., Shi, L. M., Wang, B., & Kan, M. Y. (2020). A method to evaluate credit risk for banks under PPP project finance. Engineering Construction and Architectural Management, 2(2), 483–501.

    Google Scholar 

  • Wang, Z. F., Xu, G. Y., Lin, R. J., Wang, H., & Ren, J. Z. (2019a). Energy performance contracting, risk factors, and policy implications: Identification and analysis of risks based on the best-worst network method. Energy, 170, 1–13.

    Google Scholar 

  • Wang, L., Zhang, L., Li, S. S., & Zhou, G. D. (2019b). An attention based contextual QA pairing method. Journal of Chinese Information Processing, 33(01), 125–132.

    Google Scholar 

  • Wei, L., Li, G. W., Zhu, X. Q., Sun, X. L., & Li, J. P. (2019). Developing a hierarchical system for energy corporate risk factors based on textual risk disclosures. Energy Economics, 80, 452–460.

    Google Scholar 

  • Wen, F. H., Xu, L. H., Ouyang, G. D., & Kou, G. (2019). Retail investor attention and stock price crash risk: Evidence from China. International Review of Financial Analysis, 65, 101376.

    Google Scholar 

  • Xiao, L., & Li, Y. (2019). Examining the effect of positive online reviews on consumers’ decision making: The valence framework. Journal of Global Information Management (JGIM), 27(3), 159–181. https://doi.org/10.4018/JGIM.2019070109.

    Article  Google Scholar 

  • Xu, Y., Luo, C., Chen, D., & Zheng, H. (2015). What influences the market outcome of online P2P lending marketplace?: A cross-country analysis. Journal of Global Information Management (JGIM), 23(3), 23–40. https://doi.org/10.4018/JGIM.2015070102.

    Article  Google Scholar 

  • Xu, G., Meng, Y., Qiu, X. Y., Yu, Z. H., & Wu, X. (2019a). Sentiment analysis of comment texts based on BiLSTM. IEEE Access, 7, 51522–51532.

    Google Scholar 

  • Xu, K., Yang, Z. G., Kang, P. P., Wang, Q., & Liu, W. Y. (2019b). Document-level attention-based BiLSTM-CRF incorporating disease dictionary for disease named entity recognition. Computers in Biology and Medicine, 108, 122–132.

    PubMed  Google Scholar 

  • Yang, D., & Li, M. (2018). Evolutionary approaches and the construction of technology-driven regulations. Emerging Markets Finance and Trade, 54(14), 3256–3271.

    Google Scholar 

  • Yang, L., Wu, Y. X., Wang, J. L., & Liu, Y. L. (2018). Research on recurrent neural network. Journal of Computer Applications, 38(S2), 1–26.

    Google Scholar 

  • Yuan, H., Xu, W., Li, Q., & Lau, R. (2018). Topic sentiment mining for sales performance prediction in e-commerce. Annals of Operations Research, 270(1–2), 553–576.

    Google Scholar 

  • Zeb, S., & Rashid, A. (2019). Systemic risk in financial institutions of BRICS: Measurement and identification of firm-specific determinants. Risk Management, 21(4), 243–264.

    Google Scholar 

  • Zhao, Y., Wu, Fan., Wang, Z. Q., Li, S. S., & Zhou, G. D. (2019). User relation extraction via text information and attention mechanism. Journal of Chinese Information Processing, 33(3), 87–93.

    Google Scholar 

  • Zhong, X., & Zhou, S. (2020). Risk analysis method of bank microfinance based on multiple genetic artificial neural networks. Neural Computing and Applications, 32, 5367–5377.

    Google Scholar 

  • Zhou, F. Y., Jin, L. P., & Dong, J. (2017). Review of convolutional neural network. Chinese Journal of Computers, 40(6), 1229–1251.

    MathSciNet  Google Scholar 

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Funding

Funding was provided by National Natural Science Foundation of China (Grant Nos. 71871172; 71571139), Research Center of Enterprise Decision Support, Key Research Institute of Humanities and Social Sciences in Universities of Hubei Province (Grant No. DSS20180204).

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Authors and Affiliations

  1. School of Management, Wuhan Textile University, Wuhan, 430000, Hubei, China

    Huosong Xia & Jing Liu

  2. Coggin College of Business, University of North Florida, Jacksonville, FL, 32224, USA

    Zuopeng Justin Zhang

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  1. Huosong Xia
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  2. Jing Liu
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  3. Zuopeng Justin Zhang
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Correspondence to Zuopeng Justin Zhang.

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Xia, H., Liu, J. & Zhang, Z.J. Identifying Fintech risk through machine learning: analyzing the Q&A text of an online loan investment platform. Ann Oper Res 333, 579–599 (2024). https://doi.org/10.1007/s10479-020-03842-y

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