AI-DRIVEN COMPLIANCE: USING DATA SCIENCE TO ENSURE FAIR PRICING AND POLICY ALIGNMENT IN HEALTHCARE SYSTEMS
DOI:
https://doi.org/10.15680/IJCTECE.2025.0801009Keywords:
AI in Healthcare Compliance, Fair Pricing Models, Policy Alignment, Data Science, A/B Testing, PCA, ROC Analysis, Healthcare Transparency, Ethical AI, Regulatory AnalyticsAbstract
The rapid expansion of artificial intelligence (AI) and data science in healthcare has introduced new opportunities for achieving regulatory transparency, fairness, and ethical pricing. However, the complexity of healthcare economics—combined with evolving policy frameworks—poses significant challenges to ensuring compliance and equitable cost distribution. This paper presents an AI-driven compliance framework that leverages advanced analytics, A/B testing, Principal Component Analysis (PCA), and Receiver Operating Characteristic (ROC) analysis to detect bias, assess model validity, and optimize pricing fairness within healthcare systems. By integrating machine learning algorithms with statistical validation, the proposed framework identifies anomalies in healthcare pricing, quantifies fairness levels, and evaluates policy adherence across diverse patient demographics and provider types.Experimental results based on simulated healthcare cost datasets demonstrate that AIbased compliance models outperform traditional manual audits by increasing pricing transparency, reducing bias variance, and improving detection accuracy of noncompliant pricing behaviors. The findings suggest that data-driven compliance mechanisms can significantly enhance ethical governance, regulatory alignment, and trust within healthcare ecosystems
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