Adaptive Machine Learning Driven Compliance Scoring Models for Automated Risk Detection, Quality Validation of AI-Generated Content in Regulated Industries
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I1P112Keywords:
Compliance scoring, regulated industries, AI-generated content, automated risk detection, policy-aware review, risk classification modelsAbstract
Controlled industries are using generative AI to write customer messages, summarize cases, and generate operational documents with increasing use, yet all of these may bring compliance risk by hallucinating facts, omitting required disclosures, breaching privacy, using language biased to a purpose, and lack of auditability. The proposed paper suggests an adaptive machine learning-based compliance scoring model which will automatically detect risks and verify the quality of AI written content in the context of finance, healthcare and pharmaceutical. The method is a mixture of deterministic regulatory mechanisms (policy rules, forbidden terms, obligatory templates of the disclaimer, and mapping of jurisdiction) and monitored learning and anomaly detection on semantic and structural characteristics. Each artifact is assigned (i) a continuous compliance index and (ii) a discrete risk class (approve/escalate/block), accompanied by explainable rationales that link flagged spans to relevant controls and evidence sources. To maintain performance in the shifting regulations and evolving patterns of language, the framework has drift monitoring, active learning based on reviewer feedback, and versioned updates of the rules and models based on change-control governance. Experiments are also planned to achieve across various content types and channels, high-risk recall, false-positive reduction, calibration, and auditability indicators, reproducible scoring and traceable hits on rules. Findings encourage a combined rule-ML solution as a viable way to scaleable, defensible AI-content governance to ensure organizations can speed up content creation and operational risk and enhance consistency of reviews
References
[1] Van Liebergen, Bart. Machine Learning: A Revolution in Risk Management and Compliance? The Capco Institute Journal of Financial Transformation, vol. 61, 2018, Institute of International Finance, https://www.iif.com/portals/0/Files/private/32370132_van_liebergen_-_machine_learning_in_compliance_risk_management.pdf.
[2] Leo, M., Sharma, S., & Maddulety, K. (2019). Machine Learning in Banking Risk Management: A Literature Review. Risks, 7(1), 29. https://doi.org/10.3390/risks7010029
[3] Sirangi, A. (2021). AI-Driven Risk Scoring Engine for Financial Compliance in Multi-Cloud Environments. Journal of Electrical Systems, 17(1), 138-150. https://journal.esrgroups.org/jes/article/download/8887/5939/16148
[4] Deloitte. (2021). Artificial Intelligence in Compliance: The Future is Now. Deloitte Insights. https://www2.deloitte.com/content/dam/Deloitte/cn/Documents/risk/deloitte-cn-ra-artificial-intelligence-in-compliance-en-210401.pdf
[5] Hashmi, M., Governatori, G., Lam, H. P., & Wynn, M. T. (2018). Are We Done with Business Process Compliance: State-of-the-Art and Challenges. Knowledge and Information Systems, 57(1), 79-133. https://doi.org/10.1007/s10115-017-1142-6
[6] Alzaidi, A. A. (2018). Impact of Artificial Intelligence on Performance of Banking Industry in Middle East. International Journal of Computer Science and Network Security, 18(10), 140-148
[7] Hansen, E. B., & Bøgh, S. (2021). Artificial Intelligence and Internet of Things in Small and Medium-Sized Enterprises: A Systematic Literature Review. International Journal of Production Research, 59(17), 5136-5154. https://doi.org/10.1080/00207543.2020.1802241
[8] Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. A. (2020). The Ethical Implications of Using Artificial Intelligence in Auditing. Journal of Business Ethics, 167(2), 209-234. https://doi.org/10.1007/s10551-019-04407-1
[9] Cui, M., & Zhang, D. Y. (2021). Artificial intelligence and computational pathology. Laboratory Investigation, 101(4), 412–422. https://doi.org/10.1038/s41374-020-00514-0
[10] Dwivedi, Y. K., Hughes, L., Ismagilova, E. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
[11] Gupta, M., Abdelsalam, M., Khorsandroo, S., & Mittal, S. (2020a). Security and privacy in smart farming: Challenges and opportunities. IEEE Access, 8, 34564–34584. https://doi.org/10.1109/access.2020.2975142
[12] Kute, D. V., Pradhan, B., Shukla, N., & Alamri, A. (2021). Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review. IEEE Access, 9, 82300–82317. https://doi.org/10.1109/access.2021.3086230
[13] Patel, V., Chesmore, A., Legner, C. M., & Pandey, S. (2021a). Trends in workplace wearable technologies and Connected‐Worker solutions for Next‐Generation occupational safety, health, and productivity. Advanced Intelligent Systems, 4(1). https://doi.org/10.1002/aisy.202100099
[14] Pusic, M. V., Boutis, K., Hatala, R., & Cook, D. A. (2015). Learning curves in Health Professions education. Academic Medicine, 90(8), 1034–1042. https://doi.org/10.1097/acm.0000000000000681
[15] Barredo Arrieta, A., et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. Information Fusion, 58, 82-115. https://doi.org/10.1016/j.inffus.2019.12.012
[16] Tang, Y., Chen, M., Wang, C., Luo, L., Li, J., Lian, G., ... & Zou, X. (2020). Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review. Frontiers in Plant Science, 11, 510. https://doi.org/10.3389/fpls.2020.00510
[17] Thompson, John R. J., et al. “Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions.” Journal of Risk and Financial Management, vol. 14, no. 2, 2021, pp. 1–29.
[18] Onoja, J. P., Hamza, O., Collins, A., Chibunna, U. B., Eweja, A., & Daraojimba, A. I. (2021). Digital transformation and data governance: Strategies for regulatory compliance and secure AI-driven business operations. J. Front. Multidiscip. Res, 2(1), 43-55.
[19] Milana, C., & Ashta, A. (2021). Artificial Intelligence Techniques in Finance and Financial Markets: A Survey of Literature. Strategic Change, 30(2), 189-209. https://doi.org/10.1002/jsc.2403
[20] Zekos GI. AI risk management. In Economics and Law of Artificial Intelligence: Finance, Economic Impacts, Risk Management and Governance 2021 Jan 12 (pp. 233-288). Cham: Springer International Publishing.
[21] Alouffi, B., Hasnain, M., Alharbi, A., Alosaimi, W., Alyami, H., & Ayaz, M. (2021). A Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies. IEEE Access, 9, 142732-142757. https://doi.org/10.1109/ACCESS.2021.3121374
[22] Khamis, K., & Daniya, E. (2021). Artificial Intelligence in Disaster Management. International Journal of Innovative Science and Research Technology, 6(6), 1279-1284. https://doi.org/10.38124/ijisrt21jun612
