Application of Computer Technology in Financial Investment
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916338691211264 |
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| author | Sha, Xinye |
| author_facet | Sha, Xinye |
| contents | In order to understand the application of computer technology in financial investment, the author proposes a research on the application of computer technology in financial investment. The author used user transaction data from a certain online payment platform as a sample, with a total of 284908 sample records, including 593 positive samples (fraud samples) and 285214 negative samples (normal samples), to conduct an empirical study on user fraud detection based on data mining. In this process, facing the problem of imbalanced positive and negative samples, the author proposes to use the Under Sampling method to construct sub samples, and then perform feature scaling, outlier detection, feature screening and other processing on the sub samples. Then, four classification models, logistic regression, K-nearest neighbor algorithm, decision tree, and support vector machine, are trained on the processed sub samples. The prediction results of the four models are evaluated, and the results show that the recall rate, Fl score, and AUC value of the logistic regression model are the highest, indicating that the detection method based on computer data mining is practical and feasible. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19684 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Application of Computer Technology in Financial Investment Sha, Xinye Computational Engineering, Finance, and Science In order to understand the application of computer technology in financial investment, the author proposes a research on the application of computer technology in financial investment. The author used user transaction data from a certain online payment platform as a sample, with a total of 284908 sample records, including 593 positive samples (fraud samples) and 285214 negative samples (normal samples), to conduct an empirical study on user fraud detection based on data mining. In this process, facing the problem of imbalanced positive and negative samples, the author proposes to use the Under Sampling method to construct sub samples, and then perform feature scaling, outlier detection, feature screening and other processing on the sub samples. Then, four classification models, logistic regression, K-nearest neighbor algorithm, decision tree, and support vector machine, are trained on the processed sub samples. The prediction results of the four models are evaluated, and the results show that the recall rate, Fl score, and AUC value of the logistic regression model are the highest, indicating that the detection method based on computer data mining is practical and feasible. |
| title | Application of Computer Technology in Financial Investment |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2407.19684 |