As social insurance systems expand and become more complex, traditional management methods struggle to meet the challenges posed by large data volumes and complicated processes. This paper explores the specific applications of modern statistical techniques in the transformation of social insurance administration, with a focus on the roles of big data, cloud computing, and machine learning in data collection and processing, cloud storage, and intelligent analysis and forecasting. By employing modern statistical methods, social insurance management can achieve more efficient personalized services, automated process optimization, and provide scientific support for policy-making and risk monitoring. Additionally, dynamic monitoring and early warning systems enhance the safety and stability of fund management, improving the precision and timeliness of fraud detection.

Liu Mei. The Application of Modern Statistical Techniques in the Transformation of Social Insurance Administration. Academic Journal of Business & Management (2025), Vol. 7, Issue 1: 239-243. https://doi.org/10.25236/AJBM.2025.070132.

[1] Li X .How Do Medical Insurance and New Rural Social Pension Insurance Affect the Mental Health of the Rural Elderly?——An Empirical Study Based on China Health and Retirement Longitudinal Study[C]//Wuhan Zhicheng Times Cultural Development Co., Ltd..Proceedings of 3rd International Symposium on Frontiers of Economics and Management Science (FEMS 2022).School of Public Administration,Central South University;,2022:11.DOI:10.26914/c.cnkihy.2022.013101.

[2] Zeng K .The Effect of the New Rural Social Pension Insurance Program on the Choice of Medical Institutions for Rural Elderly——Evidence from Chinese Family Panel Studies[C]//Wuhan Zhicheng Times Cultural Development Co., Ltd..Proceedings of 5th International Conference on Economics and Management, Education, Humanities and Social Sciences (EMEHSS 2021).Hainan University, 2021:16.DOI:10.26914/c.cnkihy.2021.050141.