Research on the digital transformation path of banks in the era of big data
This paper proposes a weighted large-scale data subspace clustering algorithm to enable it to adapt to the mega-customer environment for financial banks to respond quickly to customer data. Firstly, based on the K-means combined with a genetic algorithm, an improved method for the sensitivity proble...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
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| Online Access: | https://doi.org/10.2478/amns.2023.2.00033 |
| _version_ | 1797340845355040768 |
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| author | Xing Tong |
| author_facet | Xing Tong |
| author_sort | Xing Tong |
| collection | DOAJ |
| description | This paper proposes a weighted large-scale data subspace clustering algorithm to enable it to adapt to the mega-customer environment for financial banks to respond quickly to customer data. Firstly, based on the K-means combined with a genetic algorithm, an improved method for the sensitivity problem of initial clustering center selection of K-means algorithm is proposed. By weighting the variables and streaming data batch processing method as a guide, the improvement method is proposed for the problem that the mean algorithm cannot identify the correct clustering center caused by the ultra-large-scale data environment, leading to the iteration number approaching infinity. The accuracy of the K-mean algorithm, the optimized initial clustering center algorithm, and the algorithm in this paper are 89.61%, 94.37% and 96.94%, respectively. In terms of running time, the highest running time of this algorithm is 10.96 seconds, which is faster than the running time of the other two algorithms. Finally, the financial analysis of the financial bank that completed the digital transformation with the help of the algorithm in this paper, the bank achieved a business of 150.832 billion yuan in 2021, an increase of 11% compared with the end of last year. Net profit achieved 44.883 billion yuan, an increase of 25.8% compared to the end of last year. Therefore, the algorithm in this paper has high advantages in terms of accuracy, efficiency, and practicality, proving that digital transformation can improve bank profits. It also provides a path and direction of transformation for various urban and agricultural commercial banks and other small credit unions. |
| first_indexed | 2024-03-08T10:09:14Z |
| format | Article |
| id | doaj.art-e84e27695f674083ba9a5ae5cf55216d |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:09:14Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-e84e27695f674083ba9a5ae5cf55216d2024-01-29T08:52:28ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.00033Research on the digital transformation path of banks in the era of big dataXing Tong0School of Finance, Shandong University of Finance and Economics, Jinan, Shandong,250001, ChinaThis paper proposes a weighted large-scale data subspace clustering algorithm to enable it to adapt to the mega-customer environment for financial banks to respond quickly to customer data. Firstly, based on the K-means combined with a genetic algorithm, an improved method for the sensitivity problem of initial clustering center selection of K-means algorithm is proposed. By weighting the variables and streaming data batch processing method as a guide, the improvement method is proposed for the problem that the mean algorithm cannot identify the correct clustering center caused by the ultra-large-scale data environment, leading to the iteration number approaching infinity. The accuracy of the K-mean algorithm, the optimized initial clustering center algorithm, and the algorithm in this paper are 89.61%, 94.37% and 96.94%, respectively. In terms of running time, the highest running time of this algorithm is 10.96 seconds, which is faster than the running time of the other two algorithms. Finally, the financial analysis of the financial bank that completed the digital transformation with the help of the algorithm in this paper, the bank achieved a business of 150.832 billion yuan in 2021, an increase of 11% compared with the end of last year. Net profit achieved 44.883 billion yuan, an increase of 25.8% compared to the end of last year. Therefore, the algorithm in this paper has high advantages in terms of accuracy, efficiency, and practicality, proving that digital transformation can improve bank profits. It also provides a path and direction of transformation for various urban and agricultural commercial banks and other small credit unions.https://doi.org/10.2478/amns.2023.2.00033financial banksdigital transformationgenetic algorithmclustering algorithm |
| spellingShingle | Xing Tong Research on the digital transformation path of banks in the era of big data Applied Mathematics and Nonlinear Sciences financial banks digital transformation genetic algorithm clustering algorithm |
| title | Research on the digital transformation path of banks in the era of big data |
| title_full | Research on the digital transformation path of banks in the era of big data |
| title_fullStr | Research on the digital transformation path of banks in the era of big data |
| title_full_unstemmed | Research on the digital transformation path of banks in the era of big data |
| title_short | Research on the digital transformation path of banks in the era of big data |
| title_sort | research on the digital transformation path of banks in the era of big data |
| topic | financial banks digital transformation genetic algorithm clustering algorithm |
| url | https://doi.org/10.2478/amns.2023.2.00033 |
| work_keys_str_mv | AT xingtong researchonthedigitaltransformationpathofbanksintheeraofbigdata |