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EXPLORATION AND PLAN OF CREDIT RISK APPRAISAL SYSTEM USING BIG DATA AND AI

Swastik Rout

21-28

Vol 16, Jul-Dec, 2022

Date of Submission: 2022-08-02 Date of Acceptance: 2022-10-01 Date of Publication: 2022-10-12

Abstract

Since the flare-up of Coronavirus, little and medium-sized undertakings have been significantly impacted. To adapt to the trouble of capital turnover for little and medium-sized endeavours, the public authority has progressively presented a progression of monetary strategies to increment credit support furthermore, decrease support expenses. The fast improvement of innovation has likewise provoked further advancements in the working models of banks and other credit stages. In any case, banks and credit stages should think about down-to-earth issues like their own capital expenses and hazard evaluation while they help little and medium-sized endeavours to lessen funding costs. This paper intends to study what's more, plan a credit risk evaluation framework given enormous information innovation and AI calculations. It is trusted that the framework will upgrade the bank's capacity to recognize the credit risks of little and medium-sized endeavours, to tackle the issue of troublesome and costly funding for little and medium-sized ventures. Simultaneously, it will decrease the bank's awful advance proportion and increment overall revenues. Accomplishing a mutual benefit circumstance for small and medium-sized undertakings and banks, it's vital to advance mutually the improvement of the economy.

References

  1. A. Mittal, A. Shrivastava, A. Saxena and M. Manoria, 'A Study on Credit Risk Assessment in Banking Sector using Data Mining Techniques,' 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), Bhopal, India, 2018, pp. 1-5, doi: 10.1109/ICACAT.2018.8933604.
  2. Wiginton J C.A note on the comparison of logit and discriminant models of consumer credit behavior[J].Journal of Financial & Quantitative Analysis,1980,15(3):757-770.
  3. Harris T.Credit scoring using the clustered support vector machine[J].Expert Systems with Applications,2015,42(2):741-750.
  4. Y. Li, X. Lin, X. Wang, F. Shen and Z. Gong, 'Credit Risk Assessment Algorithm Using Deep Neural Networks with Clustering and Merging,' 2017 13th International Conference on Computational Intelligence and Security (CIS), Hong Kong, 2017, pp. 173-176, doi: 10.1109/CIS.2017.00045.
  5. Marqués A I,García V,Sánchez J S.Exploring the behavior of base classifiers in credit scoring ensembles[J]. Expert Systems with Applications,2012,39(11):10244-10250.
  6. Abellán J,Mantas C J.Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring[J].Expert Systems with Applications,2014,41(8):3825-3830.
  7. S. Kurt, J. Heitz, N. Bundi and W. Breymann, 'Large-Scale Data-Driven Financial Risk Modeling Using Big Data Technology,' 2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT), Zurich, 2018, pp. 206-207, doi: 10.1109/BDCAT.2018.00033.
  8. D. Li, Y. Gong, M. Ren and D. Li, 'The Research and Design of Trust Business Management and Analysis System Based on Big Data Technology,' 2020 5th IEEE International Conference on Big Data Analytics (ICBDA), Xiamen, China, 2020, pp. 68-72, doi: 10.1109/ICBDA49040.2020.9101191.
  9. A. V. Bataev, 'Analysis of the Application of Big Data Technologies in the Financial Sphere,' 2018 IEEE International Conference 'Quality Management, Transport and Information Security, Information Technologies' (IT&QM&IS), St. Petersburg, 2018, pp. 568-572, doi: 10.1109/ITMQIS.2018.8525121.
  10. Y. Lee, M. Lee, M. Lee, S. J. Hur and O. Min, 'Design of a scalable data stream channel for big data processing,' 2015 17th International Conference on Advanced Communication Technology (ICACT), Seoul, 2015, pp. 537-540, doi: 10.1109/ICACT.2015.7224857.
  11. Huang Xiao Tao, Cai Liang, Wu Chi and Huang Li Qun, 'The research of information security risk assessment method based on fault tree,' The 6th International Conference on Networked Computing and Advanced Information Management, Seoul, 2010, pp. 370-375.
  12. Liu and F. Jiang, 'Based on HHM of the coal mine safety risk assessment methods,' 2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, Chengdu, 2012, pp. 592-594, doi: 10.1109/ICQR2MSE.2012.6246303.
  13. Xu Zhandong and G. Chi, 'Bank-enterprise project risk assessment model based on the information entropy method,' 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), Dengleng, 2011, pp. 998-1001, doi: 10.1109/AIMSEC.2011.6010675.
  14. X. Liu and J. Huang, 'Genetic algorithm-based feature selection method for credit risk analysis,' Proceedings of 2012 2nd International Conference on Computer Science and Network Technology, Changchun, 2012, pp. 2233-2236, doi: 10.1109/ICCSNT.2012.6526362.
  15. K. Yu et al., 'Information-Centric Networking: Research and Standardization Status,' in IEEE Access, vol. 7, pp. 126164-126176, 2019, doi: 10.1109/ACCESS.2019.2938586
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