Discussion on the Enterprise Financial Risk Management Framework Based on AI Fintech

Yu Liu

doi:10.31181/dmame712024942

Authors

Yu Liu School of Intelligent Finance, Henan Institute of Economics and Trade, Zhengzhou, 450000, China https://orcid.org/0009-0001-6407-4397

DOI:

https://doi.org/10.31181/dmame712024942

Keywords:

Decision tree algorithm, Random forest algorithm, Finance, Risk prediction

Abstract

Deep learning algorithms lack interpretability and interpretability in the decision-making process. This makes it difficult to understand the judgment basis and decision-making process of financial risks based on algorithms, which may reduce the trust and acceptance of risk decisions by enterprises. To address this issue, this study introduces the improved random forest algorithm based on the decision tree algorithm to discuss its framework. Through analysis of the PR curve in the experiment, it was determined that the AP value of the enhanced random forest algorithm is 0.9919, a significant improvement over the RF algorithm's previous value of 0.9237. It also has a good balance between the precision rate and the recall rate. By introducing the data set to compare and analyze the three algorithms of SVM, CRAT, and the improved random forest algorithm, it is found that the improved random forest algorithm has a higher test value. Through cluster analysis, it is found that the clustering accuracy of the improved random forest algorithm is about 81%. The final analysis of a company’s financial data sample showed an accuracy rate of 69.6% on the enterprise access index. The improved random forest algorithm achieves a good balance between accuracy and recall, resulting in the potential for high accuracy in the domain of risk assessment. In addition, compared with other algorithms such as SVM and CRAT, the improved random forest algorithm has higher test values, indicating its excellent performance in financial risk prediction. To sum up, this study firstly verified the feasibility and accuracy of the improved random forest algorithm for financial risk prediction. Additionally, it validated the method's predictive capability using factual enterprise data samples and ultimately established a foundation for the enterprise's financial risk management framework. Construction offers a new theoretical direction.

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References

Radhachandran, A., Garikipati, A., Zelin, N., Pellegrini, E., Ghandian, S., Hoffman, J., Mao, Q., & Das, R. (2021). A Gradient-Boosted Decision-Tree Algorithm for the Prediction of Short-Term Mortality in Acute Heart Failure Patients. Cardiovascular Revascularization Medicine, 28, S19.

Singh, J., & Tripathi, P. (2021). Sentiment analysis of Twitter data by making use of SVM, Random Forest and Decision Tree algorithm. In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT) (pp. 193-198). IEEE. https://doi.org/10.1109/CSNT51715.2021.9509679

Si, Y. (2022). Construction and application of enterprise internal audit data analysis model based on decision tree algorithm. Discrete Dynamics in Nature and Society, 2022. https://doi.org/10.1155/2022/4892046

Liu, Q., & Xia, X. (2022). Construction of classification model of academic library websites in Jiangsu based on decision tree algorithm and link analysis method. Open Access Library Journal, 9(1), 1-9. https://doi.org/10.4236/oalib.1108324

Wu, F., Liu, X., Wang, Y., Li, X., & Zhou, M. (2022). Research on evaluation model of hospital informatization level based on decision tree algorithm. Security and Communication Networks, 2022, 1-9. https://doi.org/10.1155/2022/3777474

Wang, L., Wang, H., Zhang, H., Akemujiang, N., & Xiao, A. (2020). Somatotype identification of middle-aged women based on decision tree algorithm. International Journal of Clothing Science and Technology, 33(3), 402-420. https://doi.org/10.1108/IJCST-12-2019-0193

Ariyati, I., Rosyida, S., Ramanda, K., Riyanto, V., & Faizah, S. (2020, November). Optimization of the decision tree algorithm used particle swarm optimization in the selection of digital payments. In Journal of Physics: Conference Series (Vol. 1641, No. 1, p. 012090). IOP Publishing. https://doi.org/10.1088/1742-6596/1641/1/012090

Muditomo, A., & Broto, A. S. (2021). IPO performance prediction during Covid-19 pandemic in Indonesia using decision tree algorithm. Jurnal Keuangan dan Perbankan, 25(1), 132-143. https://doi.org/10.26905/jkdp.v25i1.5137

Saadoon, Y. A., & Abdulamir, R. H. (2021, May). Improved random forest algorithm performance for big data. In Journal of Physics: Conference Series (Vol. 1897, No. 1, p. 012071). IOP Publishing. https://doi.org/10.1088/1742-6596/1897/1/012071

Ning, F., Cheng, Z., Meng, D., & Wei, J. (2021). A framework combining acoustic features extraction method and random forest algorithm for gas pipeline leak detection and classification. Applied Acoustics, 182, 108255. https://doi.org/10.1016/j.apacoust.2021.108255

Song, M., Jung, H., Lee, S., Kim, D., & Ahn, M. (2021). Diagnostic classification and biomarker identification of Alzheimer’s disease with random forest algorithm. Brain Sciences, 11(4), 453. https://doi.org/10.3390/brainsci11040453

Zhang, Z., & Cai, Z. (2021). Permeability prediction of carbonate rocks based on digital image analysis and rock typing using random forest algorithm. Energy & Fuels, 35(14), 11271-11284. https://doi.org/10.1021/acs.energyfuels.1c01331

Liu, X. (2021). Empirical analysis of financial statement fraud of listed companies based on logistic regression and random forest algorithm. Journal of Mathematics, 2021, 1-9. https://doi.org/10.1155/2021/9241338

Sonza, R. L., & Tumibay, G. M. (2020). Decision tree algorithm in identifying specific interventions for gender and development issues. Journal of Computer and Communications, 8(2), 17-26. https://doi.org/10.4236/jcc.2020.82002

Mao, L., & Zhang, W. (2021). Analysis of entrepreneurship education in colleges and based on improved decision tree algorithm and fuzzy mathematics. Journal of Intelligent & Fuzzy Systems, 40(2), 2095-2107. https://doi.org/10.3233/JIFS-189210

Munawar, H. S., Mojtahedi, M., Hammad, A. W., Kouzani, A., & Mahmud, M. P. (2022). Disruptive technologies as a solution for disaster risk management: A review. Science of the total environment, 806, 151351. https://doi.org/10.1016/j.scitotenv.2021.151351

Hentzen, J. K., Hoffmann, A., Dolan, R., & Pala, E. (2022). Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research. International Journal of Bank Marketing, 40(6), 1299-1336. https://doi.org/10.1108/IJBM-09-2021-0417

Dinesh, T., & Rajendran, T. (2021). Higher classification of fake political news using decision tree algorithm over naive Bayes algorithm. Revista Geintec-Gestao Inovacao E Tecnologias, 11(2), 1084-1096.

Zhang, Y., Balochian, S., Agarwal, P., Bhatnagar, V., & Housheya, O. J. (2014). Artificial intelligence and its applications. Mathematical problems in Engineering, 2014, Article ID 840491. https://doi.org/10.1155/2014/840491

Li, J., Li, X., & Zhang, Z. (2021, April). Dynamic prediction model of bridge project life cycle cost investment based on decision tree algorithm. In IOP Conference Series: Earth and Environmental Science (Vol. 760, No. 1, p. 012049). IOP Publishing. https://doi.org/10.1088/1755-1315/760/1/012049

Toker, D., Sommer, F. T., & D'Esposito, M. (2019). The chaos decision tree algorithm: A fully automated tool for the experimental study of chaotic dynamics. arXiv.