IDEAS home Printed from https://ideas.repec.org/a/spr/opmare/v15y2022i3d10.1007_s12063-022-00293-5.html
   My bibliography  Save this article

RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models

Author

Listed:
  • Guansan Du

    (University of Southern Queensland
    Liaoning University)

  • Frank Elston

    (Liaoning University)

Abstract

A sound credit assessment mechanism has been explored for many years and is the key to internet finance development, and scholars divide credit assessment mechanisms into linear assessment and nonlinear assessment. The purpose is to explore the role of two important data analytics models including machine learning and deep learning in internet credit risk assessment and improve the accuracy of financial prediction. First, the problems in the current internet financial risk assessment are understood, and data of MSE (Micro small Enterprises) are chosen for analysis. Then, a feature extraction method based on machine learning is proposed to solve data redundancy and interference in enterprise credit risk assessment. Finally, to solve the data imbalance problem in the credit risk assessment system, a credit risk assessment system based on the deep learning DL algorithm is introduced, and the proposed credit risk assessment system is verified through a fusion algorithm in different models with specific enterprise data. The results show that the credit risk assessment model based on the machine learning algorithm optimizes the standard algorithm through the global optimal solution. The credit risk assessment model based on deep learning can effectively solve imbalanced data. The algorithm generalization is improved through layer-by-layer learning. Comparison analysis shows that the accuracy of the proposed fusion algorithm is 25% higher than that of the latest CNN (Convolutional Neural Network) algorithm. The results can provide a new research idea for the assessment of internet financial risk, which has important reference value for preventing financial systemic risk.

Suggested Citation

  • Guansan Du & Frank Elston, 2022. "RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models," Operations Management Research, Springer, vol. 15(3), pages 925-940, December.
  • Handle: RePEc:spr:opmare:v:15:y:2022:i:3:d:10.1007_s12063-022-00293-5
    DOI: 10.1007/s12063-022-00293-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12063-022-00293-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12063-022-00293-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yali Cao & Yue Shao & Hongxia Zhang, 2022. "Study on early warning of E-commerce enterprise financial risk based on deep learning algorithm," Electronic Commerce Research, Springer, vol. 22(1), pages 21-36, March.
    2. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
    3. Ouyang, Zi-sheng & Yang, Xi-te & Lai, Yongzeng, 2021. "Systemic financial risk early warning of financial market in China using Attention-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    4. Dong Yang & Pu Chen & Fuyuan Shi & Chenggong Wen, 2018. "Internet Finance: Its Uncertain Legal Foundations and the Role of Big Data in Its Development," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(4), pages 721-732, March.
    5. Hung Nguyen & George Onofrei & Norma Harrison & Dothang Truong, 2020. "The influence of cultural compatibility and product complexity on manufacturing flexibility and financial performance," Operations Management Research, Springer, vol. 13(3), pages 171-184, December.
    6. Cui, Zhenyu & Kirkby, J. Lars & Nguyen, Duy, 2021. "A data-driven framework for consistent financial valuation and risk measurement," European Journal of Operational Research, Elsevier, vol. 289(1), pages 381-398.
    7. Guoxiang Xu & Wangfeng Gao, 2019. "Financial Risk Contagion in Stock Markets: Causality and Measurement Aspects," Sustainability, MDPI, vol. 11(5), pages 1-20, March.
    8. Zhuming Chen & Yushan Li & Yawen Wu & Junjun Luo, 2017. "The transition from traditional banking to mobile internet finance: an organizational innovation perspective - a comparative study of Citibank and ICBC," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-16, December.
    9. Xuechao Sui & Xianhui Geng, 2021. "Continuous usage intention to e-transaction cards in wholesale markets of agriproducts: empirical evidence from China," Future Business Journal, Springer, vol. 7(1), pages 1-13, December.
    10. Mahour Mellat Parast, 2021. "An assessment of the impact of corporate social responsibility on organizational quality performance: Empirical evidence from the petroleum industry," Operations Management Research, Springer, vol. 14(1), pages 138-151, June.
    11. Kwee Keong Choong & Sardar M. Islam, 2020. "A new approach to performance measurement using standards: a case of translating strategy to operations," Operations Management Research, Springer, vol. 13(3), pages 137-170, December.
    12. Duan, Yuejiao & Goodell, John W. & Li, Haoran & Li, Xinming, 2022. "Assessing machine learning for forecasting economic risk: Evidence from an expanded Chinese financial information set," Finance Research Letters, Elsevier, vol. 46(PA).
    13. Shen, Feng & Zhao, Xingchao & Li, Zhiyong & Li, Ke & Meng, Zhiyi, 2019. "A novel ensemble classification model based on neural networks and a classifier optimisation technique for imbalanced credit risk evaluation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tianlei Pi & Haoxuan Hu & Jingyi Lu & Xue Chen, 2022. "The Analysis of Fintech Risks in China: Based on Fuzzy Models," Mathematics, MDPI, vol. 10(9), pages 1-13, April.
    2. Zhao, Zichao & Li, Dexuan & Dai, Wensheng, 2023. "Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    3. Zhu Yongjie, 2023. "Enterprise life cycle, financial technology and digital transformation of banks—Evidence from China," Australian Economic Papers, Wiley Blackwell, vol. 62(3), pages 486-500, September.
    4. Silvia Garc'ia-M'endez & Francisco de Arriba-P'erez & Ana Barros-Vila & Francisco J. Gonz'alez-Casta~no, 2024. "Detection of Temporality at Discourse Level on Financial News by Combining Natural Language Processing and Machine Learning," Papers 2404.01337, arXiv.org.
    5. Burka, Dávid & Puppe, Clemens & Szepesváry, László & Tasnádi, Attila, 2022. "Voting: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1003-1017.
    6. Anna Eugenia Omarini, 2018. "Banks and Fintechs: How to Develop a Digital Open Banking Approach for the Bank’s Future," International Business Research, Canadian Center of Science and Education, vol. 11(9), pages 23-36, September.
    7. Amal Alnamrouti & Husam Rjoub & Hale Ozgit, 2022. "Do Strategic Human Resources and Artificial Intelligence Help to Make Organisations More Sustainable? Evidence from Non-Governmental Organisations," Sustainability, MDPI, vol. 14(12), pages 1-23, June.
    8. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
    9. Yuanying Chi & Mingjian Yan & Yuexia Pang & Hongbo Lei, 2022. "Financial Risk Assessment of Photovoltaic Industry Listed Companies Based on Text Mining," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    10. Panagiotis Reklitis & Damianos P. Sakas & Panagiotis Trivellas & Giannis T. Tsoulfas, 2021. "Performance Implications of Aligning Supply Chain Practices with Competitive Advantage: Empirical Evidence from the Agri-Food Sector," Sustainability, MDPI, vol. 13(16), pages 1-21, August.
    11. Yadgar Taha M. Hamakhan, 2020. "The effect of individual factors on user behaviour and the moderating role of trust: an empirical investigation of consumers’ acceptance of electronic banking in the Kurdistan Region of Iraq," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-29, December.
    12. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    13. Gillmann, Niels & Kim, Alisa, 2021. "Quantification of Economic Uncertainty: a deep learning approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242421, Verein für Socialpolitik / German Economic Association.
    14. Arindam Kundu & Sumit Kumar & Nutan Kumar Tomar, 2024. "A Semi-Closed Form Approximation of Arbitrage-Free Call Option Price Surface," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1431-1457, April.
    15. Wang, Haijun & Mao, Kunyuan & Wu, Wanting & Luo, Haohan, 2023. "Fintech inputs, non-performing loans risk reduction and bank performance improvement," International Review of Financial Analysis, Elsevier, vol. 90(C).
    16. Shi, Feifen & Zhao, Chuanjun, 2023. "Enhancing financial fraud detection with hierarchical graph attention networks: A study on integrating local and extensive structural information," Finance Research Letters, Elsevier, vol. 58(PB).
    17. Zhang, Wen & Yan, Shaoshan & Li, Jian & Tian, Xin & Yoshida, Taketoshi, 2022. "Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    18. Fan He & Meitao Wang & Peng Zhou, 2022. "Evaluation of market risk and resource allocation ability of green credit business by deep learning under internet of things," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-20, April.
    19. An, Hui & Wang, Hao & Delpachitra, Sarath & Cottrell, Simon & Yu, Xiao, 2022. "Early warning system for risk of external liquidity shock in BRICS countries," Emerging Markets Review, Elsevier, vol. 51(PA).
    20. Ouyang, Zisheng & Lu, Min & Lai, Yongzeng, 2023. "Forecasting stock index return and volatility based on GAVMD- Carbon-BiLSTM: How important is carbon emission trading?," Energy Economics, Elsevier, vol. 128(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:opmare:v:15:y:2022:i:3:d:10.1007_s12063-022-00293-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.