A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain

Zhou, Huicheng; Sun, Guodao; Sha, Fei; Fan, Xiaoping; Jiang, Wangdong; Hu, Shuting; Li, Lingjiao

doi:10.32604/cmc.2020.09834

Cited by 34 publications

(36 citation statements)

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“…Supply chain financial credit evaluation is also beneficial for banks to control their risks and gain more profit. Zhou et al ( 2020 ) proposed a distributed approach for supply chain financial fraud detection using a DL model. The approach discovers fraudulent financing behaviors and thus reduces supply chain losses from such behaviors.…”

Section: Systematic Review Resultsmentioning

confidence: 99%

“…Tosida et al ( 2020 ) used a CNN model to classify telematics companies based on their need for assistance analyzing their general information, financial information, business obstacles, and prospects. Zhou et al ( 2020 ) applied CNN on a supply chain financial dataset for fraud detection. Guo ( 2020 ) constructed a CNN to extract commodities’ features from their images to be used for price forecasting in e-commerce.…”

Section: Systematic Review Resultsmentioning

confidence: 99%

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Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Shavaki

Ghahnavieh

2022

Artif Intell Rev

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In today’s complex and ever-changing world, Supply Chain Management (SCM) is increasingly becoming a cornerstone to any company to reckon with in this global era for all industries. The rapidly growing interest in the application of Deep Learning (a class of machine learning algorithms) in SCM, has urged the need for an up-to-date systematic review on the research development. The main purpose of this study is to provide a comprehensive vision by reviewing a set of 43 papers about applications of Deep Learning (DL) methods to the SCM, as well as the trends, perspectives, and potential research gaps. This review uses content analysis to answer three research questions namely: 1- What SCM problems have been solved by the use of DL techniques? 2- What DL algorithms have been used to solve these problems? 3- What alternative algorithms have been used to tackle the same problems? And do DL outperform these methods and through which evaluation metrics? This review also responds to this call by developing a conceptual framework in a value-adding perspective that provides a full picture of areas on where and how DL can be applied within the SCM context. This makes it easier to identify potential applications to corporations, in addition to potential future research areas to science. It might also provide businesses a competitive advantage over their competitors by allowing them to add value to their data by analyzing it quickly and precisely.

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Section: Systematic Review Resultsmentioning

confidence: 99%

Section: Systematic Review Resultsmentioning

confidence: 99%

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Shavaki

Ghahnavieh

2022

Artif Intell Rev

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“…In addition, f i,j indicates the number of times the secondary keyword K j appears in file d j . Then, F i,j represents the occurrence frequency of the keyword K j in d j , which is defined as [24]:…”

Section: Content-based Recommendation Algorithmmentioning

confidence: 99%

Enterprise Decision-Making and Analysis Based on E-Commerce Data Mining

Wang

Huang

2022

Wireless Communications and Mobile Computing

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Enterprises have established a number of business processing systems and business websites according to their own characteristics and business needs, such as e-commerce websites and shopping websites. Over time, a large number of sales transaction data and customer purchase information have been generated, but no useful information has been generated stored in the database. Therefore, the management and decision-making levels of enterprises try to get useful information from huge and complex data. With the development of network technology and database, data mining technology has emerged. Nowadays, data mining technology has become one of the most concerned technologies of e-commerce. It can select appropriate data mining methods according to the characteristics of commodities, carry out effective statistical analysis and decision support for data, predict and analyze future market trends, greatly improve the business intelligence analysis of e-commerce enterprises, and make enterprises have greater advantages in market competition. This paper explains some important methods related to data mining and designs a data mining system. One of the systems can help e-commerce decision-makers analyze and predict data. Through experiments, it is concluded that the average absolute percentage errors of prediction data are 8.6% and 5.3%, respectively, with small errors and high accuracy. Second, make better recommendations for users. After investigation and analysis, the highest satisfaction has increased by 25% after using the system.

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“…The results showed that the Bayesian belief network was superior to the other five models. Huangjun Zhou et al 24 proposed the use of big data mining technology based on distributed algorithms to identify and analyze fraud in the supply chain. Spakr and Hadoop evaluated a deep learning method based on a convolutional neural network, which was reported to significantly reduce the time required for the recognition process and also improved the accuracy of financial fraud detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Financial Statement Fraud with Interpretable Machine Learning

Liu

2021

Preprint

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In this study, we explored a stable and explainable model in the detection of financial fraud. To effectively handle imbalanced datasets, we selected the Smote oversampling algorithm with the highest AUC value and compared it with Borderline Smote and ADASYN algorithms. Using the MCB method, we found that the Adaptive Lasso algorithm had higher stability than SCAD, MCP, Stepwise, and SQRT Lasso algorithms. Moreover, the AUC value was improved by WoE encoding and IV value testing of the features. Finally, we ranked the fraud factors based on the importance of the features, and the partial dependence function was used to make the model interpretable. By comparing the AUC and KS values, the integrated models XGBoost, LightGBM, and RF showed better ability to identify financial fraud compared with traditional models such as SVM and LR.

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A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain

Cited by 34 publications

References 0 publications

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Enterprise Decision-Making and Analysis Based on E-Commerce Data Mining

Detecting Financial Statement Fraud with Interpretable Machine Learning

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