Towards novel financial risk prediction method with machine learning model
DOI:
https://doi.org/10.54097/xjegpc38Keywords:
Financial risk, machine learning, big data, risk prediction, data processing.Abstract
Among the multiple risks faced by enterprises, financial risk is particularly prominent in the big data environment. Serious data imbalance has become a major challenge in the analysis of corporate financial risks. Aiming at the sample imbalance problem in enterprise competitive intelligence analysis, this paper proposes an enterprise risk identification method oriented to unbalanced samples, taking credit risk prediction of financial enterprises as a starting point. The method utilizes intelligent analysis means such as feature selection, unbalanced sample balance processing and integrated learning in the field of artificial intelligence, aiming to provide a solution to the problem of enterprise risk identification in enterprise competitive intelligence under the big data environment.
Downloads
References
T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system", Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., pp. 785-794, Aug. 2016.
A. Dorogush, V. Ershov and A. Gulin, "Boost algorithm: Gradient boosting with categorical features support", Proc. Workshop ML Syst. Neural Inf. Process. Syst. (NIPS), pp. 1-7, 2017.
G. Ke et al., "LightGBM: A highly efficient gradient boosting decision tree", Proc. 31st Conf. Neural Inf. Process. Syst. (NIPS), pp. 3146-3154, 2017.
H. B. He and E. A. Garcia, "Learning from imbalanced data", IEEE Transactions on Knowledge and Data Engineering, no. 9, pp. 1263-1284, 2009.
Y. M. Sun, A. K. C. Wong and M. S. Kamel, "Classification of imbalanced data: a review", International Journal of Pattern Recognition and Artificial Intelligence, vol. 23, no. 04, pp. 687-719, 2009.
X. Y. Liu, J. Wu and Z. H. Zhou, "Exploratory undersampling for class-imbalance learning", IEEE Transactions on Systems Man and Cybernetics- Part B, vol. 39, no. 2, pp. 539-550, 2009.
P. Ray and A. Chakrabarti, "A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis", Appl. Comput. Informatics, 2019.
J. Wang, L.-C. Yu, K. R. Lai and X. Zhang, "Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model", Proc. 54th Annu. Meet. Assoc. Comput. Linguist. (Volume 2 Short Pap., vol. 2, pp. 225-230, 2016.
Feng Xia, "Label Oriented Hierarchical Attention Neural Network for Short Text Classification", Academic Journal of Engineering and Technology Science, pp. 5-8, 2022.
J. Kiefer and K. Dorer, "Double Deep Reinforcement Learning," 2023 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Tomar, Portugal, 2023, pp. 17-22, doi: 10.1109/ICARSC58346.2023.10129640.
Junhong He and Ke Ma. 2021. Enterprise Financial Risk Management and Control. In 2021 2nd Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC2021). Association for Computing Machinery, New York, NY, USA, 393–396. https://doi.org/10.1145/3452446.3452547
Olga Arkadeva and Natalia Berezina. 2021. Digitalization in state financial risk management. In Proceedings of the 2nd International Scientific Conference on Innovations in Digital Economy (SPBPU IDE '20). Association for Computing Machinery, New York, NY, USA, Article 5, 1–7. https://doi.org/10.1145/3444465.3444491
Ramit Sawhney, Puneet Mathur, Ayush Mangal, Piyush Khanna, Rajiv Ratn Shah, and Roger Zimmermann. 2020. Multimodal Multi-Task Financial Risk Forecasting. In Proceedings of the 28th ACM International Conference on Multimedia (MM '20). Association for Computing Machinery, New York, NY, USA, 456–465. https://doi.org/10.1145/3394171.3413752
Xianping Yuan and Yue Zhang. 2021. Analysis of Bank Loan Risk Management Based on BP Neural Network. In 2021 4th International Conference on Information Systems and Computer Aided Education (ICISCAE 2021). Association for Computing Machinery, New York, NY, USA, 2457–2461. https://doi.org/10.1145/3482632.3487450
V. T. and J. L. 2019. The Blend of Credit Scoring Model for Individual in the Dmaic Process for Reducing Non-Performing Loan Risk. In Proceedings of the 2019 International Conference on Management Science and Industrial Engineering (MSIE '19). Association for Computing Machinery, New York, NY, USA, 195–202. https://doi.org/10.1145/3335550.3335583
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
