AI-DRIVEN CHILD SUPPORT OPTIMIZATION SYSTEMS USING PREDICTIVE ELIGIBILITY MODELING AND CASE PRIORITIZATION
DOI:
https://doi.org/10.15662/3e446214Keywords:
Artificial Intelligence (AI), Predictive Analytics, Child Support Systems, Eligibility Modeling, Case Prioritization, Machine Learning, Public Sector Modernization, Decision Support Systems, Data-Driven Governance, Social Welfare Optimization, Risk Scoring, Resource Allocation, Explainable AI (XAI)Abstract
Child support systems play a critical role in ensuring the financial well-being of children in separated or economically vulnerable families. However, traditional child support enforcement and eligibility determination processes are often reactive, fragmented, and resource-intensive, leading to delays, inefficiencies, and inequitable outcomes. This paper proposes an AI-driven framework for optimizing child support systems through predictive eligibility modeling and intelligent case prioritization. The approach leverages machine learning techniques to analyze historical case data, socio-economic indicators, payment patterns, and behavioral signals to proactively assess eligibility, predict payment risks, and identify high-impact intervention opportunities.
The proposed system integrates predictive analytics with rule-based policy engines to enhance decision accuracy while maintaining regulatory compliance and transparency. A multi-layered architecture is introduced, comprising data ingestion pipelines, feature engineering modules, predictive modeling components, and decision orchestration layers. The framework emphasizes fairness, explainability, and data privacy, addressing key ethical considerations associated with AI deployment in social welfare systems.
In addition, the paper explores prioritization strategies that enable caseworkers to focus on high-risk or high-need cases, improving operational efficiency and maximizing child support outcomes. Simulation-based evaluations demonstrate improvements in processing time, collection rates, and resource allocation efficiency compared to traditional approaches. The study also discusses implementation challenges, including data quality, system integration, and governance, and provides a roadmap for scalable adoption in government and enterprise environments.
This research contributes to the growing field of AI-enabled public service transformation by presenting a practical and scalable solution for modernizing child support systems, ultimately enhancing service delivery, equity, and financial stability for affected families.
References
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