Empirical Analysis of Financial Statement Fraud of Listed Companies Based on Logistic Regression and Random Forest Algorithm

Liu, Xinchun

doi:10.1155/2021/9241338

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…When Liu conducted research on financial market regulators, they found that there were a lot of financial risks, so they proposed a random forest algorithm to analyze and predict them. The experimental results show that the prediction accuracy of the algorithm is as high as 97%, which shows the accuracy and efficiency of this algorithm [13]. Through previous research, it has been found that the random forest algorithm has been used in various fields and has shown extremely strong performance.…”

Section: Related Workmentioning

confidence: 83%

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

Liu

2023

Decis. Mak. Appl. Manag. Eng.

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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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Section: Related Workmentioning

confidence: 83%

“…The larger the value, the richer the diversity among the classifiers. Kappa statistic is more comprehensive, it can be used to measure the accuracy of the classifier; It can also indicate the consistency of the prediction results of different models, as shown in equation(13).…”

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confidence: 99%

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

Liu

2023

Decis. Mak. Appl. Manag. Eng.

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An Analysis on Financial Statement Fraud Detection for Chinese Listed Companies Using Deep Learning

2022

IEEE Access

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Financial fraud has extremely damaged the sustainable growth of financial markets as a serious problem worldwide. Nevertheless, it is fairly challenging to identify frauds with highly imbalanced dataset because ratio of non-fraud companies is very high compared to fraudulent ones. Intelligent financial statement fraud detection systems have therefore been developed to support decision-making for the stakeholders. However, most of current approaches only considered the quantitative part of the financial statement ratios while there has been less usage of the textual information for classifying, especially those related comments in Chinese. As such, this paper aims to develop an enhanced system for detecting financial fraud using a state-of-the-art deep learning models based on combination of numerical features that derived from financial statement and textual data in managerial comments of 5130 Chinese listed companies' annual reports. First, we construct financial index system including both financial and non-financial indices that previous researches usually excluded. Then the textual features in MD&A section of Chinese listed company's annual report are extracted using word vector. After that, powerful deep learning models are employed and their performances are compared with numeric data, textual data and combination of them, respectively. The empirical results show great performance improvement of the proposed deep learning methods against traditional machine learning methods, and LSTM, GRU approaches work with testing samples in a correct classification rate of 94.98% and 94.62%, indicating that the extracted textual features of MD&A section exhibit promising classification results and substantially reinforce financial fraud detection.

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Enterprise violation risk deduction combining generative AI and event evolution graph

Zhong,

Li,

Wang

et al. 2024

Expert Systems

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In the current realms of scientific research and commercial applications, the risk inference of regulatory violations by publicly listed enterprises has attracted considerable attention. However, there are some problems in the existing research on the deduction and prediction of violation risk of listed enterprises, such as the lack of analysis of the causal logic association between violation events, the low interpretability and effectiveness of the deduction and the lack of training data. To solve these problems, we propose a framework for enterprise violation risk deduction based on generative AI and event evolution graphs. First, the generative AI technology was used to generate a new text summary of the lengthy and complex enterprise violation announcement to realize a concise overview of the violation matters. Second, by fine‐tuning the generative AI model, an event entity and causality extraction framework based on automated data augmentation are proposed, and the UIE (Unified Structure Generation for Universal Information Extraction) event entity extraction model is used to create the event entity extraction for listed enterprises ‘violations. Then, a causality extraction model CDDP‐GAT (Event Causality Extraction Based on Chinese Dictionary and Dependency Parsing of GAT) is proposed. This model aims to identify and analyse the causal links between corporate breaches, thereby deepening the understanding of the event logic. Then, the merger of similar events was realized, and the causal correlation weights between enterprise violation‐related events were evaluated. Finally, the listed enterprise's violation risk event evolution graph was constructed, and the enterprise violation risk deduction was carried out to form an expert system of financial violations. The deduction results show that the method can effectively reveal signs of enterprise violations and adverse consequences.

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Empirical Analysis of Financial Statement Fraud of Listed Companies Based on Logistic Regression and Random Forest Algorithm

Cited by 4 publications

References 32 publications

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

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

An Analysis on Financial Statement Fraud Detection for Chinese Listed Companies Using Deep Learning

Enterprise violation risk deduction combining generative AI and event evolution graph

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