Integrating AI-Driven Compliance Frameworks to Automate Regulatory Monitoring across U.S. Healthcare, Finance and Institutional Governance Systems

Аннотация

This paper explores how Artificial Intelligence (AI)-based compliance systems can be used to automate regulation monitoring in three major sectors in the U.S. healthcare, finance, and institutional governance. It will seek to determine the awareness, perceived benefits, implementation difficulties, effectiveness, and future prospects of AI application in compliance management. The study provides empirical data to the knowledge of improving compliance efficiency, transparency, and accountability of AI technologies in complex regulatory settings. A quantitative method of conducting a research was used and a set of questions was measured with structured questionnaire and was sent to a sample of 300 professionals representing healthcare, finance and governance institutions. The research employed the descriptive, correlation, and inferential statistics to analyze the relationships between the core constructs. The SPSS was used to analyze data with the involvement of reliability testing, correlation, multiple regression, ANOVA, t-tests, and exploratory factor analysis (EFA) to prove the measurement model and evaluate the dynamics between variables. The findings also indicated that, all constructs had high internal consistency with Cronbach Alpha values varying between 0.84 and 0.93, which was a strong instrument reliability. Descriptive statistics revealed that the overall perception of the AI adoption is positive, with the highest means being Effectiveness and Impact and Perceived Benefits. Correlation, regression analyses indicated that Perceived Benefits, Future Prospects, and Awareness and Adoption significantly and positively predicted Effectiveness and Impacts whereas the Challenges and Barriers had a negative effect. It was found that the model explains a strong 69 percent of the variance in effectiveness. Also, global sectoral differences in perceived effectiveness were significant based on the results of ANOVA, and the results of the t-test based on gender showed that female respondents were slightly more confident about the benefits of AI. The EFA found four different but related factors that include AI Effectiveness, Compliance Benefits, Implementation Barriers, and Future Readiness that explain 70% of the total variance. The results highlight the importance of strategic preparedness, sufficient infrastructure, and ethical governance as the elements that ensure success in the process of AI integration into compliance. To achieve a sustainable adoption, organizations need to deal with issues of data privacy, transparency, and skills. The research proposes capacity-building programs, cross-sectional coordination, and the creation of uniform AI governance systems to facilitate accountability and predictability in regulation automation. The study offers one of the limited empirical evaluations of AI-based compliance systems in a wide variety of regulated sectors in the U.S. It builds upon the existing works of literature by connecting the efficacy of technology to the organizational preparedness and perceived value, which provides practical implications to policymakers, compliance professionals, and institutional leaders. The study can add value to the developing debate on intelligent governance and ethical automation by confirming a robust framework of assessing AI compliance performance.