Analysis of financial fraud based on manager knowledge graph

Wen, Shigang; Li, Jianping; Zhu, Xiaoqian; Liu, Mingxi

doi:10.1016/j.procs.2022.01.096

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…Secondly, the forged business certificates lead to an inflated rate of return on net assets; in addition, part of the funds is transferred to the accounts of related parties for stock transactions, reflecting the illusion of a closed-loop capital flow. In addition, in terms of financial and accounting supervision, in the annual report, Company X reported that the company had major deficiencies in internal control, and its internal control audit agency maintained a negative audit opinion on the company's internal control in 2020 [19][20][21][22].…”

Section: B Practical Application and Analysis Of The Modelmentioning

confidence: 99%

Research on the Model of Preventing Corporate Financial Fraud under the Combination of Deep Learning and SHAP

Wang¹

2023

IJACSA

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Preventing financial fraud in listed companies is conducive to improving the healthy development of China's accounting industry and the securities market, is conducive to promoting the improvement of the internal control system of China's enterprises, and is conducive to promoting stability. Based on the combination of SHAP (Shapley Additive explanation), a prediction and identification model should be built to determine the possibility of financial fraud and the risk of fraud for the company. The research model has effectively improved the identification accuracy of financial fraud in listed companies, and the research model has effectively dealt with the gray sample problem that is common in the forecasting model through the LOF algorithm and the IF algorithm. When conducting comparative experiments on the models, the overall accuracy rate of the research model is over 85%, the recall rate is 78.5%, the precision rate is 42%, the AUC reaches 0.896, the discrimination degree KS reaches 0.652, and the model stability PSI is 0.088, compared with traditional financial fraud Forecasting models FS model and CS model has a higher predictive effect. In the empirical analysis, selecting a company's fraud cases in 2020 can effectively analyze the characteristic contribution in the fraud process and the focus on fraud risks. The established model can effectively monitor the company's finance and prevent fraud.

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Section: B Practical Application and Analysis Of The Modelmentioning

confidence: 99%

Research on the Model of Preventing Corporate Financial Fraud under the Combination of Deep Learning and SHAP

Wang¹

2023

IJACSA

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“…In recent years, knowledge graph has been used in various areas in the industry, e.g., medical, financial, manufacturing, etc. Specifically, financial fraud is able to be analysed and detected by based on manager knowledge graph [5].…”

Section: Knowledge Graph and Its Application In The Industrymentioning

confidence: 99%

Intelligent extrusion die design based on knowledge graph

Qin

2023

International Conference on Artificial Intelligence and Industrial Design (AIID 2022)

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Aluminium profiles have an increasing demand since its excellent physical properties. At the same time, the crosssection shape of the profile is becoming more and more complex, which makes the design of its extrusion die more difficult and time consuming. To fill this gap, this paper has proposed an intelligent design method based on capturing and reusing the design knowledge of the extrusion die. Specifically, a knowledge representation model is created to reveal the key design knowledge generated during the design process. Then, a multi-modal knowledge graph is built to systematically structure and store the design information and knowledge. Finally, a smart design system is developed for the application of knowledge capturing and rueing by semantic retrieval and intelligent recommendation. Through applying and evaluating in a leading aluminium profile enterprise, this method has shown its ability in helping engineering designer in design knowledge reuse and improving design efficiency.

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“…Due to its unique capacity for representing and managing extensive knowledge, the knowledge graph has found application in diverse domains, including information retrieval (Reinanda et al., 2020), intelligent question‐answering (Xiong et al., 2021), data integration (Kalaycı et al., 2021), and financial analysis (Wen et al., 2022). However, there appears to be a limited exploration of the amalgamation of the knowledge graph with three‐dimensional (3D) scene modeling, especially concerning the creation of high‐speed railway scenes.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Knowledge Graphmentioning

confidence: 99%

Knowledge‐guided digital twin modeling method of generating hierarchical scenes for a high‐speed railway

Zhang,

Zhao,

Han

et al. 2023

Transactions in GIS

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China's railway construction is rapidly transitioning toward integrated management of “stakeholders, management elements, and management processes”. Therefore, comprehensive and whole‐process digital twin scene modeling is urgently needed for intelligent railway construction. However, the requirements of three‐dimensional scenes in different stages vary hierarchically, resulting in a lack of construction semantics and limited universality in modeling. This article proposes a knowledge‐guided digital twin modeling method of hierarchical scenes for a high‐speed railway. We first build a knowledge graph of “knowledge‐model‐data” to achieve an accurate and hierarchical description of railway scenes. We then establish a parameter‐driven modeling method that integrates knowledge guidance and primitive combination to generate a display scene and a virtual design scene automatically. Third, we propose joint linkage and model growth methods for construction action modeling, which are used to generate a virtual construction scene. Finally, in response to the hierarchical scene‐generating requirements in different stages, we conduct intelligent modeling experiments for the entire design and construction process. The knowledge graph of the hierarchical semantic description mode significantly improves the flexibility and universality of the modeling method. The proposed modeling method for the entire process contributes to the rapid representation of design data, in‐depth design, visual exploration, and dynamic optimization of the construction process. This article provides a reliable digital twin modeling solution for the entire process to improve design and construction quality.

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Analysis of financial fraud based on manager knowledge graph

Cited by 8 publications

References 23 publications

Research on the Model of Preventing Corporate Financial Fraud under the Combination of Deep Learning and SHAP

Research on the Model of Preventing Corporate Financial Fraud under the Combination of Deep Learning and SHAP

Intelligent extrusion die design based on knowledge graph

Knowledge‐guided digital twin modeling method of generating hierarchical scenes for a high‐speed railway

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