IBCA: An Intelligent Platform for Social Insurance Benefit Qualification Status Assessment

Authors

  • Yuliang Shi School of Software, Shandong University (SDU), Jinan, China Dareway Software Co. Ltd, Jinan, China Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University (SDU), Jinan, China
  • Lin Cheng School of Software, Shandong University (SDU), Jinan, China
  • Cheng Jiang Dareway Software Co. Ltd, Jinan, China
  • Hui Zhang School of Software, Shandong University (SDU), Jinan, China Dareway Software Co. Ltd, Jinan, China
  • Guifeng Li Dareway Software Co. Ltd, Jinan, China
  • Xiaoli Tang Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University (SDU), Jinan, China School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore
  • Han Yu Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University (SDU), Jinan, China School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore
  • Zhiqi Shen Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University (SDU), Jinan, China School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore
  • Cyril Leung Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University (SDU), Jinan, China Department of Electrical and Computer Engineering, The University of British Columbia (UBC), Vancouver, BC, Canada

DOI:

https://doi.org/10.1609/aaai.v38i21.30316

Keywords:

Health, Medical & Medicine , Track: Deployed Applications, Data Mining , Deep Learning and Neural Networks

Abstract

Social insurance benefits qualification assessment is an important task to ensure that retirees enjoy their benefits according to the regulations. It also plays a key role in curbing social security frauds. In this paper, we report the deployment of the Intelligent Benefit Certification and Analysis (IBCA) platform, an AI-empowered platform for verifying the status of retirees to ensure proper dispursement of funds in Shandong province, China. Based on an improved Gated Recurrent Unit (GRU) neural network, IBCA aggregates missing value interpolation, temporal information, and global and local feature extraction to perform accurate retiree survival rate prediction. Based on the predicted results, a reliability assessment mechanism based on Variational Auto-Encoder (VAE) and Monte-Carlo Dropout (MC Dropout) is executed to perform reliability assessment. Deployed since November 2019, the IBCA platform has been adopted by 12 cities across the Shandong province, handling over 50 terabytes of data. It has empowered human resources and social services, civil affairs, and health care institutions to collaboratively provide high-quality public services. Under the IBCA platform, the efficiency of resources utilization as well as the accuracy of benefit qualification assessment have been significantly improved. It has helped Dareway Software Co. Ltd earn over RMB 50 million of revenue.

Published

2024-03-24

How to Cite

Shi, Y., Cheng, L., Jiang, C., Zhang, H., Li, G., Tang, X., Yu, H., Shen, Z., & Leung, C. (2024). IBCA: An Intelligent Platform for Social Insurance Benefit Qualification Status Assessment. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 22815-22823. https://doi.org/10.1609/aaai.v38i21.30316

Issue

Section

IAAI Technical Track on Deployed Highly Innovative Applications of AI