Intelligent financial fraud detection practices in post-pandemic era

Zhu, Xiaoqian; Ao, Xiang; Qin, Zidi; Chang, Yanpeng; Liu, Yang; He, Qing; Li, Jian Ping

doi:10.1016/j.xinn.2021.100176

Cited by 82 publications

(51 citation statements)

References 159 publications

(223 reference statements)

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“…e output gate controls the effect of the memory cell c on the current output value, i.e., which part of the memory cell will be output at time step t. e value of the output gate is shown in equation ( 4), and the output of the LSTM unit at time f' can be obtained using equation (5), respectively:…”

Section: Computational Modelsmentioning

confidence: 99%

“…e existing company business management system plays an important role in serving front-line supervision, but there are still shortcomings in supporting technology supervision, mainly in three aspects: First, the database needs to be enhanced, the data are not comprehensive and complete, and the data lack effective integration. Second, the lack of new technology support is caused by the limited ability to analyze information [5]. In the face of the huge amount, scattered sources and diverse formats of company data, statistical reports, regulatory inquiries, manual search and processing of information, manual identification and disposal of traditional methods have gradually become unsuitable, the ability of machine processing and analysis of data needs to be improved [6].…”

Section: Introductionmentioning

confidence: 99%

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Design and Application of Intelligent Financial Accounting Model Based on Knowledge Graph

Hou

2022

Mobile Information Systems

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With the continuous progress of science and technology, economic globalization has become an important direction for the development of enterprises. In the process of enterprise development, artificial intelligence and machine learning techniques have greatly improved the efficiency of enterprise accounting and financial management and have gradually shifted traditional financial accounting to modern financial management and accounting. Owing to the existing problems of inefficiency, large consumption of time and resources, and low degree of intelligence in the existing computerized financial data prediction systems, this study proposes an intelligent financial accounting and a financial risk monitoring and early warning model based on knowledge graph and deep learning techniques. To validate its performance, the model is trained using financial data of 120 listed companies, and the model is applied to establish the prediction of whether the listed companies are facing a financial crisis, using another 60 companies as the test sample. Results show that the use of deep learning and knowledge graph techniques can significantly improve the regulatory model, enhance regulatory penetration, and alleviate regulatory time lag, thus improving the ability of regulation to detect enterprise problems and prevent risks.

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Section: Computational Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Application of Intelligent Financial Accounting Model Based on Knowledge Graph

Hou

2022

Mobile Information Systems

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“…and we get the corresponding optimal objective value by substituting − G j H j +λ for w * j in Equation (8).…”

Section: Xgboostmentioning

confidence: 99%

“…In the real world, the frequency of default cases is usually much smaller than that of non-default ones. It is challenging to develop an effective default forecasting model if the class distribution is imbalanced, as rare default instances are harder to be identified compared with common non-default instances [7,8]. For instance, assume the imbalance ratio of the two-class dat set is 99, with the majority non-default class accounting for 99% and the minority default class accounting for 1%.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Algorithm-Level Ensemble Model for Imbalanced Credit Default Prediction in the Energy Industry

Wang

et al. 2022

Energies

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Credit default prediction for the energy industry is essential to promoting the healthy development of the energy industry in China. While previous studies have constructed various credit default prediction models with brilliant performance, the class-imbalance problem in the credit default dataset cannot be ignored, where the numbers of credit default cases are usually much smaller than the number of non-default ones. To address the class-imbalance problem, we proposed a novel CT-XGBoost model, which adds to XGBoost with two algorithm-level methods for class imbalance, including the cost-sensitive strategy and threshold method. Based on the credit default dataset consisting of energy corporates in western China, which suffers from the class-imbalance problem, the CT-XGBoost model achieves better performance than the conventional models. The results indicate that the proposed model can efficiently alleviate the inherent class-imbalance problem in the credit default dataset. Moreover, we analyze how the prediction performance is influenced by different parameter settings in the cost-sensitive strategy and threshold method. This study can help market investors and regulators precisely assess the credit risk in the energy industry and provides theoretical guidance to solving the class-imbalance problem in credit default prediction.

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“…More recently, Zhu et al (2021) provided a literature review of artificial intelligence studies on the practices of financial fraud detections. Furthermore, after rigorous quantitative investigations, Tsuji (2022) derived many significant views for risk management using artificial intelligence.…”

Section: Short Literature Reviewmentioning

confidence: 99%

Exchange Rate Forecasting via a Machine Learning Approach

Tsuji

2022

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This paper attempts to forecast exchange rates by applying a machine learning approach. More specifically, in this study, we attempt to forecast the dynamic evolutions of the four exchange rates of Canadian dollars, Australian dollars, Great Britain pounds, and euros, which are all against the US dollar, by using random forest methodology. Evaluating the effectiveness, we find that the predictive performance of random forest approach in exchange rate forecasting is rather high.

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Intelligent financial fraud detection practices in post-pandemic era

Cited by 82 publications

References 159 publications

Design and Application of Intelligent Financial Accounting Model Based on Knowledge Graph

Design and Application of Intelligent Financial Accounting Model Based on Knowledge Graph

A Hybrid Algorithm-Level Ensemble Model for Imbalanced Credit Default Prediction in the Energy Industry

Exchange Rate Forecasting via a Machine Learning Approach

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