Published October 30, 2025 | Version v1
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Artificial Intelligence Driven Compliance Automation Improving Audit Readiness and Fraud Detection within Healthcare Revenue Cycle Management Systems

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The integration of Artificial Intelligence (AI) into healthcare revenue cycle management (RCM) systems is revolutionizing compliance automation, audit readiness, and fraud detection across the healthcare enterprise. This review explores how AI-driven compliance automation frameworks leverage machine learning (ML), natural language processing (NLP), and robotic process automation (RPA) to ensure real-time regulatory adherence, minimize billing anomalies, and enhance audit transparency. By analyzing data across claim submissions, coding accuracy, denial management, and payment reconciliation,
AI systems enable predictive risk scoring and anomaly detection to identify irregular claim patterns indicative of fraudulent activities or noncompliance. Furthermore, explainable AI (XAI) models are increasingly used to provide interpretability in compliance decision pathways, supporting auditors in tracing logic-based evidence trails during regulatory reviews. The study also examines the role of generative AI in automating documentation compliance, particularly in aligning electronic health records (EHRs) with Centers for Medicare & Medicaid Services (CMS) and Health Insurance Portability and Accountability
Act (HIPAA) standards. A comparative assessment of legacy compliance models versus AI-augmented systems highlights significant reductions in falsepositive fraud alerts, audit preparation time, and operational overhead. This paper highlightss the convergence of AI analytics, data governance frameworks, and healthcare informatics in shaping a resilient, transparent, and fraud-resilient RCM ecosystem. Future research directions include the standardization of AI auditing protocols, ethical governance in algorithmic decision-making, and the integration of federated learning for privacy-preserving fraud analytics across multi-institutional datasets.

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Artificial+Intelligence+Driven+Compliance+Automation+Improving+Audit+Readiness+and+Fraud+Detection+within+Healthcare+Revenue+Cycle+Management+Systems.pdf

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References

  • Amebleh, J., & Idika, C. N. (2022). Automated audit trail preservation using RPA-driven compliance bots in healthcare revenue cycle systems. International Journal of Scientific Research in Modern Technology, 8(5), 144–159.
  • Amebleh, J., & Igba, E. (2021). Graph-based fraud detection in open-loop gift cards: Heterogeneous GNNs, streaming feature stores, and near-zero-lag anomaly alerts. International Journal of Scientific Research in Science, Engineering and Technology, 8(6), 576–591.
  • Amebleh, J., & James, U. U. (2022). Outlier detection in automated healthcare auditing using hybrid supervised–unsupervised learning models. International Journal of Scientific Research in Science and Technology, 8(5), 331–345.
  • Amebleh, J., & Okoh, O. F. (2023). Explainable risk controls for digital health payments: SHAP-constrained gradient boosting with policy-based access, audit trails, and chargeback mitigation. International Journal of Scientific Research and Modern Technology, 2(4), 13–28. https://doi.org/10.38124/ijsrmt.v2i4.746
  • Amebleh, J., & Oyekan, F. T. (2022). Enhancing medical claims integrity through hybrid machine learning models for automated coding verification in healthcare revenue systems. International Journal of Scientific Research in Science and Technology, 8(6), 310–325.
  • Arshad, K., Ali, R. F., Muneer, A., Aziz, I. A., Naseer, S., Khan, N. S., & Taib, S. M. (2022). Deep reinforcement learning for anomaly detection: A systematic review. Ieee Access, 10, 124017-124035.
  • Bazoge, A., Morin, E., Daille, B., & Gourraud, P. A. (2023). Applying natural language processing to textual data from clinical data warehouses: systematic review. JMIR medical informatics, 11, e42477. https://doi.org/10.2196/42477
  • Boda, V. V. R., & Allam, H. (2021). Automating Compliance in Healthcare: Tools and Techniques You Need. International Journal of Emerging Trends in Computer Science and Information Technology, 2(3), 38-48.
  • Chalamala, S. R., Kummari, N. K., Singh, A. K., Saibewar, A., & Chalavadi, K. M. (2022). Federated learning to comply with data protection regulations. CSI Transactions on ICT, 10(1), 47-60.
  • Charles, V., Rana, N. P., & Carter, L. (2022). Artificial Intelligence for datadriven decision-making and governance in public affairs. Government Information Quarterly, 39(4), 101742.
  • Dako, O. F., Onalaja, T. A., Nwachukwu, P. S., Ajoke, F., & Bankole, T. L. (2021). Predictive Risk-Based Auditing Utilizing Data Models to Proactively Identify Organizational Vulnerabilities and Mitigate Losses.
  • De Almeida, P. G. R., Dos Santos, C. D., & Farias, J. S. (2021). Artificial intelligence regulation: a framework for governance. Ethics and Information Technology, 23(3), 505-525.
  • Fagbohungbe, T., Gayawan, E. & Akeboi, O. S. (2020). Spatial prediction of childhood malnutrition across space in Nigeria based on point-referenced data: an SPDE approach Journal of Public Health Policy 41(3) DOI: 10.1057/s41271-020-00246-x
  • Gayawan, E. & Fagbohungbe, T. (2023). Continuous Spatial Mapping of the Use of Modern Family Planning Methods in Nigeria Global Social Welfare 10(2):1-11 DOI: 10.1007/s40609-023-00264-z
  • Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial intelligence in healthcare (pp. 295-336). Academic Press
  • Greenleaf, G. (2017). Global data privacy laws: 120 national data privacy laws, including Indonesia and Turkey. Privacy Laws & Business International Report, 145, 10–13.
  • Idika, C. N., & Amebleh, J. (2021). Evaluating deterministic models in fraud analytics: A comparative study of pattern-based versus adaptive machine learning detection. International Journal of Scientific Research and Modern Technology, 7(2), 45–59. https://doi.org/10.38124/IJSRMT7214