Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data
At present, there are more and more frauds in the financial field. The detection and prevention of financial frauds are of great significance for regulating and maintaining a reasonable financial order. Deep learning algorithms are widely used because of their high recognition rate, good robustness,...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6685888 |
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| author | Jian Liu Xin Gu Chao Shang |
| author_facet | Jian Liu Xin Gu Chao Shang |
| author_sort | Jian Liu |
| collection | DOAJ |
| description | At present, there are more and more frauds in the financial field. The detection and prevention of financial frauds are of great significance for regulating and maintaining a reasonable financial order. Deep learning algorithms are widely used because of their high recognition rate, good robustness, and strong implementation. Therefore, in the context of e-commerce big data, this paper proposes a quantitative detection algorithm for financial fraud based on deep learning. First, the encoders are used to extract the features of the behaviour. At the same time, in order to reduce the computational complexity, the feature extraction is restricted to the space-time volume of the dense trajectory. Second, the neural network model is used to transform features into behavioural visual word representations, and feature fusion is performed using weighted correlation methods to improve feature classification capabilities. Finally, sparse reconstruction errors are used to judge and detect financial fraud. This method builds a deep neural network model with multiple hidden layers, learns the characteristic expression of the data, and fully depicts the rich internal information of the data, thereby improving the accuracy of financial fraud detection. Experimental results show that this method can effectively learn the essential characteristics of the data, and significantly improve the detection rate of fraud detection algorithms. |
| format | Article |
| id | doaj-art-6fa2fe8ba4b04d24b096726cccfc4419 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-6fa2fe8ba4b04d24b096726cccfc44192025-02-03T05:52:25ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/66858886685888Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big DataJian Liu0Xin Gu1Chao Shang2School of Business, Sichuan University, Chengdu 610064, ChinaSchool of Business, Sichuan University, Chengdu 610064, ChinaChengdu Soft Innovation Intelligence Association, Chengdu 610023, ChinaAt present, there are more and more frauds in the financial field. The detection and prevention of financial frauds are of great significance for regulating and maintaining a reasonable financial order. Deep learning algorithms are widely used because of their high recognition rate, good robustness, and strong implementation. Therefore, in the context of e-commerce big data, this paper proposes a quantitative detection algorithm for financial fraud based on deep learning. First, the encoders are used to extract the features of the behaviour. At the same time, in order to reduce the computational complexity, the feature extraction is restricted to the space-time volume of the dense trajectory. Second, the neural network model is used to transform features into behavioural visual word representations, and feature fusion is performed using weighted correlation methods to improve feature classification capabilities. Finally, sparse reconstruction errors are used to judge and detect financial fraud. This method builds a deep neural network model with multiple hidden layers, learns the characteristic expression of the data, and fully depicts the rich internal information of the data, thereby improving the accuracy of financial fraud detection. Experimental results show that this method can effectively learn the essential characteristics of the data, and significantly improve the detection rate of fraud detection algorithms.http://dx.doi.org/10.1155/2020/6685888 |
| spellingShingle | Jian Liu Xin Gu Chao Shang Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data Complexity |
| title | Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data |
| title_full | Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data |
| title_fullStr | Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data |
| title_full_unstemmed | Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data |
| title_short | Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data |
| title_sort | quantitative detection of financial fraud based on deep learning with combination of e commerce big data |
| url | http://dx.doi.org/10.1155/2020/6685888 |
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