Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search
The detection of corporate accounting fraud is a critical challenge in the financial industry, where traditional models such as neural networks, logistic regression, and support vector machines often fall short in achieving high accuracy due to the complex and evolving nature of fraudulent activitie...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-11-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2532.pdf |
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| _version_ | 1850065172974010368 |
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| author | Peifeng Wu Yaqiang Chen |
| author_facet | Peifeng Wu Yaqiang Chen |
| author_sort | Peifeng Wu |
| collection | DOAJ |
| description | The detection of corporate accounting fraud is a critical challenge in the financial industry, where traditional models such as neural networks, logistic regression, and support vector machines often fall short in achieving high accuracy due to the complex and evolving nature of fraudulent activities. This paper proposes an enhanced approach to fraud detection by integrating convolutional neural networks (CNN) and long short-term memory (LSTM) networks, complemented by an attention mechanism to prioritize relevant features. To further improve the model’s performance, the sparrow search algorithm (SSA) is employed for parameter optimization, ensuring the best configuration of the CNN-LSTM-Attention framework. Experimental results demonstrate that the proposed model outperforms conventional methods across various evaluation metrics, offering superior accuracy and robustness in recognizing fraudulent patterns in corporate accounting data. |
| format | Article |
| id | doaj-art-e8c760ce34834d5780d1c7e232c3b79c |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-e8c760ce34834d5780d1c7e232c3b79c2025-08-20T02:49:05ZengPeerJ Inc.PeerJ Computer Science2376-59922024-11-0110e253210.7717/peerj-cs.2532Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow searchPeifeng Wu0Yaqiang Chen1School of Statistics, Jilin University of Finance and Economics, Changchun, Jilin, ChinaSchool of Statistics, Jilin University of Finance and Economics, Changchun, Jilin, ChinaThe detection of corporate accounting fraud is a critical challenge in the financial industry, where traditional models such as neural networks, logistic regression, and support vector machines often fall short in achieving high accuracy due to the complex and evolving nature of fraudulent activities. This paper proposes an enhanced approach to fraud detection by integrating convolutional neural networks (CNN) and long short-term memory (LSTM) networks, complemented by an attention mechanism to prioritize relevant features. To further improve the model’s performance, the sparrow search algorithm (SSA) is employed for parameter optimization, ensuring the best configuration of the CNN-LSTM-Attention framework. Experimental results demonstrate that the proposed model outperforms conventional methods across various evaluation metrics, offering superior accuracy and robustness in recognizing fraudulent patterns in corporate accounting data.https://peerj.com/articles/cs-2532.pdfAccounting fraudPredictionNeural networksAttentionSparrow search |
| spellingShingle | Peifeng Wu Yaqiang Chen Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search PeerJ Computer Science Accounting fraud Prediction Neural networks Attention Sparrow search |
| title | Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search |
| title_full | Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search |
| title_fullStr | Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search |
| title_full_unstemmed | Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search |
| title_short | Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search |
| title_sort | enhanced detection of accounting fraud using a cnn lstm attention model optimized by sparrow search |
| topic | Accounting fraud Prediction Neural networks Attention Sparrow search |
| url | https://peerj.com/articles/cs-2532.pdf |
| work_keys_str_mv | AT peifengwu enhanceddetectionofaccountingfraudusingacnnlstmattentionmodeloptimizedbysparrowsearch AT yaqiangchen enhanceddetectionofaccountingfraudusingacnnlstmattentionmodeloptimizedbysparrowsearch |