Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
@article{193214,
author = {Prof. Aarti Rathod and Ayush Bais and Bhavesh Dahake and Bhagyashree Tangade and Bhavana pahukar and Chaitanya Deshpande and Bhumika Rathod and Chaitali Wakulkar},
title = {Survey of “AI-Driven Performance Analytics Framework for Monitoring Public Welfare Schemes”.},
journal = {International Journal of Innovative Research in Technology},
year = {2026},
volume = {12},
number = {9},
pages = {4233-4239},
issn = {2349-6002},
url = {https://ijirt.org/article?manuscript=193214},
abstract = {Public welfare schemes are essential for promoting social equity and economic development, yet assessing their effectiveness remains a significant challenge due to fragmented data, large beneficiary populations, and limited real-time monitoring mechanisms. This study proposes an AI-driven performance analytics framework designed to improve the monitoring and evaluation of public welfare programs through intelligent data analysis. The framework integrates diverse data sources, including beneficiary information, financial records, and program outcomes, to generate meaningful insights using machine learning techniques. Advanced analytical models are employed to identify performance trends, detect irregularities, and predict potential risks in scheme implementation. Additionally, the system incorporates interactive dashboards to support policymakers in making informed, evidence-based decisions. By enabling automated analysis and proactive monitoring, the proposed approach aims to enhance transparency, accountability, and resource optimization in welfare program management. The research highlights the potential of artificial intelligence to transform traditional governance processes into more efficient and data-driven systems, ultimately improving the impact of public welfare initiatives.},
keywords = {Artificial Intelligence, Machine Learning, Public Welfare Schemes, Performance Analytics, Policy Monitoring, Data Analytics, Smart Governance, Anomaly Detection, Decision Support Systems, Predictive Analytics, Government Programs, Social Impact Assessment},
month = {February},
}
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