<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the continuous development of big data technology, the application of big data prediction in enterprise risk management is getting more and more attention. After proposing a framework for applying big data prediction in accounting management risk, this study builds an accounting management risk prediction model based on the optimized particle swarm and random forest algorithms.Company X’s accounting management risk-related data is taken as a research sample, and the effect of the constructed accounting management risk prediction model is investigated by comparing the models and assessing the risk prediction accuracy. The study shows that the PSO-random forest model built in this paper has faster convergence and higher accuracy than the ordinary random forest, and the overall accuracy of accounting management risk prediction is 12% higher. The model’s accuracy in predicting the overall and various types of risks in Company X’s accounting management is more than 85%. The PSO-Random Forest model is a reliable tool for predicting accounting management risks, which is of great practical importance.</p>
</abstract>

With the continuous development of big data technology, the application of big data prediction in enterprise risk management is getting more and more attention. After proposing a framework for applying big data prediction in accounting management risk, this study builds an accounting management risk prediction model based on the optimized particle swarm and random forest algorithms.Company X’s accounting management risk-related data is taken as a research sample, and the effect of the constructed accounting management risk prediction model is investigated by comparing the models and assessing the risk prediction accuracy. The study shows that the PSO-random forest model built in this paper has faster convergence and higher accuracy than the ordinary random forest, and the overall accuracy of accounting management risk prediction is 12% higher. The model’s accuracy in predicting the overall and various types of risks in Company X’s accounting management is more than 85%. The PSO-Random Forest model is a reliable tool for predicting accounting management risks, which is of great practical importance.

A Study of Big Data Prediction in Accounting Management Risks

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"A Study of Big Data Prediction in Accounting Management Risks"}

With the continuous development of big data technology, the application of big data prediction in enterprise risk management is getting more and more...

A Study of Big Data Prediction in Accounting Management Risks

Online veröffentlicht: 03. Mai 2024

Seitenbereich: -

Eingereicht: 08. Apr. 2024

Akzeptiert: 27. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-0913

Schlüsselwörter
Particle swarm algorithm, Random forest, Risk prediction, Accounting management risk, Big data prediction

© 2024 Jinhua Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Study of Big Data Prediction in Accounting Management Risks

Online veröffentlicht: 03. Mai 2024

Seitenbereich: -

Eingereicht: 08. Apr. 2024

Akzeptiert: 27. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-0913

SchlüsselwörterParticle swarm algorithm, Random forest, Risk prediction, Accounting management risk, Big data prediction

© 2024 Jinhua Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Particle swarm algorithm, Random forest, Risk prediction, Accounting management risk, Big data prediction