PROACTIVE DATA GOVERNANCE: USING AI AND ML TO ANTICIPATE AND MITIGATE RISKS

Abstract

The purpose of this research was to determine how well AI systems perform in predicting risks and how they help with business continuity planning. Disasters, cyberattacks, and economic swings are just a few of the many risks that businesses face. The purpose of this study was to identify the ways in which several AI components—including incident response planning, AI-powered data analytics, and natural language processing (NLP)—improve risk assessment and bolster business continuity in such a setting. Using AI and ML to foresee and lessen the impact of potential risks has grown more important in this age of investment sector uncertainty. This paper outlines a comprehensive methodology architecture, detailing each step from data collection and preprocessing to model development, deployment, and continuous monitoring. We explore the frequency of various AI/ML techniques, with supervised learning and time series analysis emerging as predominant methods. Performance metrics indicate that neural networks and gradient boosting models excel in accuracy, precision, and recall. Resource allocation analysis reveals significant emphasis on model development and data preprocessing. This integrated approach ensures robust risk management by employing diverse techniques and strategically distributing resources, thereby enhancing the ability to predict and mitigate risks effectively.