RISKAI: Predictive Risk Assessment and Visualization Using Machine Learning

Abstract

One crucial method for spotting and assessing possible risks before they have a big influence is predictive risk assessment. The accuracy, scalability, and real-time responsiveness of traditional risk assessment methods are constrained by their reliance on manual data processing, rule- based systems, and historical reporting. In order to evaluate past risk datasets and anticipate future risk levels, this study introduces RISKAI, a machine learning-based predictive risk assessment and visualization system. Modules for data collection, preprocessing, feature extraction, classification, and visualization are all integrated into the suggested system. To divide hazards into low, medium, and high categories, supervised machine learning methods like Support Vector Machines, Random Forest, and Decision Trees are used. Results are presented in an interactive and user-friendly way using visualization dashboards. In risk- sensitive settings, the system facilitates proactive decision- making, lowers manual labor, and increases prediction accuracy.