Study on Efficiency Improvement of Data Analysis in Customer Asset Allocatior
DOI:
https://doi.org/10.71222/bzvdpn81Keywords:
data analysis, customer asset allocation, efficiency improvement, real-time data, algorithm optimizationAbstract
With the continuous development of the financial market, customers' investment preferences and purchasing behaviors are evolving rapidly. Leveraging data analysis to enhance the efficiency and accuracy of customers' asset allocation decisions has become a crucial technical approach in modern finance. This paper primarily focuses on the practical application and existing challenges of data analysis in customer asset management. Key issues, including fragmented data integration, inconsistent data quality, limited real-time processing capabilities, and suboptimal algorithm models, are thoroughly analyzed. In response, optimization strategies are proposed, such as establishing a unified data integration platform, strengthening data quality management mechanisms, and improving real-time data processing capabilities. By addressing these challenges, financial institutions can significantly enhance decision-making effectiveness, increase investment returns, and provide more reliable guidance for customers' asset allocation. This study offers valuable insights for the development and refinement of data-driven strategies in the financial industry.
References
1. P. Koyan, and J. Tronicke, "3D ground-penetrating radar data analysis and interpretation using attributes based on the gradient structure tensor," Geophysics, vol. 89, no. 4, pp. B289-B299, 2024. doi: 10.1190/geo2023-0670.1
2. J. Wrona, P. Hardy, C. Youssef, S. Adeleke, M. A. Martin, L. B. Gerald, and A. A. Pappalardo, "Stock inhalers: a qualitative data analysis of Illinois health policy trials and triumphs," Journal of School Health, vol. 94, no. 10, pp. 918-928, 2024. doi: 10.1111/josh.13500
3. M. Irfan, and W. Y. Lau, "Asset allocation and performance of Malaysian civil service pension fund," Australasian Accounting, Business and Finance Journal, vol. 18, no. 1, 2024.
4. Y. Xiao, "Financial availability and rural household asset allocation," Finance Research Letters, vol. 62, p. 105256, 2024. doi: 10.1016/j.frl.2024.105256
5. G. Chen, H. Wang, and C. Zhang, "Mobile cellular network security vulnerability detection using machine learning," International Journal of Information and Communication Technology, vol. 22, no. 3, pp. 327-341, 2023.
6. H. Shim, J. Back, Y. Eun, G. Park, and J. Kim, "Zero-dynamics attack, variations, and countermeasures," In Security and Resilience of Control Systems: Theory and Applications, 2022, pp. 31-61. doi: 10.1007/978-3-030-83236-0_2
7. N. Hussein, and A. Nhlabatsi, "Living in the dark: Mqtt-based exploitation of iot security vulnerabilities in zigbee networks for smart lighting control," IoT, vol. 3, no. 4, pp. 450-472, 2022. doi: 10.3390/iot3040024
8. R. Caviglia, "Novel Approaches to Standard Based Cybersecurity Risk Management in OT Environment," 2025.
9. A. Odlyzko, "Cybersecurity is not very important," Ubiquity, vol. 2019, no. June, pp. 1-23, 2019. doi: 10.1145/3333611
10. Z. Zhang, M. Liu, M. Sun, R. Deng, P. Cheng, D. Niyato, and J. Chen, "Vulnerability of machine learning approaches applied in iot-based smart grid: A review," IEEE Internet of Things Journal, vol. 11, no. 11, pp. 18951-18975, 2024. doi: 10.1109/jiot.2024.3349381
11. O. Nasir, W. Amer, A. Hamarsheh, A. M. Ali, and A. A. Zaid, "AI-Based Algorithm for Zero-Day Attack Detection Using Reinforcement Learning," In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA), April, 2025, pp. 1-6. doi: 10.1109/icciaa65327.2025.11013576
12. S. S. Yoon, D. Y. Kim, G. G. Kim, and I. C. Euom, "Vulnerability assessment based on real world exploitability for prioritizing patch applications," In 2023 7th Cyber Security in Networking Conference (CSNet), October, 2023, pp. 62-66.
13. Z. Chen, S. Kommrusch, and M. Monperrus, "Neural transfer learning for repairing security vulnerabilities in c code," IEEE Transactions on Software Engineering, vol. 49, no. 1, pp. 147-165, 2022. doi: 10.1109/tse.2022.3147265
14. O. Friman, "Agile and DevSecOps oriented vulnerability detection and mitigation on public cloud," 2024.
15. N. Schelehoff, "Securing Agile Development: Analysing the Intersection of Cybersecurity and Agile Methodologies-A Systematic Literature Review," 2025.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Chenyang An (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.







