AI-Based Risk/Compliance Decision Framework
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I2P113Keywords:
Artificial Intelligence, Risk Assessment, Compliance Management, Predictive Analytics, Machine Learning, Explainable Ai, Decision Support SystemsAbstract
The growing regulatory demands, the ambiguity in the field of operation, the safety of the information and networksecurity challenges have led to the emergence of the dynamic and intelligent risk and compliance management measures. The more conservative traditional risk assessment frameworks tend to utilize fixed rules, manual reviews and fragmented data, constraining their capability to discern arising risks and aid in timely compliance decisions. The hypothesis is the study has a risk/compliance decision structure based on models of artificial intelligence, predictive analytics, mapping of regulatory rules, and risk-prioritization to improve the way organizations make decisions. The framework includes data collecting and standardization, risk detection, AI-powered risk classification, compliance requisites mapping, decision recommendation, and ongoing monitoring. The pattern of the risk, the occurrence of anomalies and the estimation of the potential compliance deviation are identified using machine learning methods and the compliance with the relevant regulatory requirements, as well as the organizational policies, is maintained by rule-based validation. Human-in-the-loop mechanism is implemented in an attempt to enhance transparency, accountability as well as expert view in high impact choices. Conceptual evaluation of the framework is done based on such aspects as risk detecting capabilities, and consistency in decision making, regulatory traceability, adaptability and explainability. The suggested methodology offers a systemized background of incorporating smart risk assessment and compliance governance, which will allow the organization to put more emphasis on the mitigation activities, streamline the effort of manual assessment, and enhance audit preparedness. This study adds a flexible decision-support architecture to organizations aiming at proactive and transparent, risk, and compliance management based on data.
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