Quantitative Risk Analysis with AI based Prediction Model

With Artificial Intelligence based algorithm, proposed Predictive Risk Analysis can be applied to perform comprehensive risk assessment and can depict serious gaps in existing control measures and the risk footprint of the organization. Risk Assessment and evaluation is core engine of Risk Lifecycle Management for an organization, driving its risk control and mitigation strategy. The prevailing approach to Risk analysis, due to methodologies applied across the industries, is manual, static and very specific to problem at hand. A generalized quantitative model for risk analysis is vital so that a planning, analysis and evaluation framework can be developed that can be used across the industry/sectors for better decision making. We undertook detailed analyses of data regarding hazards and its effect and that indicated necessity for stochastic model for such risk modeling and analysis. For Risk Analysis, a probabilistic model based on fat tailed Gaussian Distribution is used in this research for risk modeling and Artificial Intelligence based algorithm is employed to predict the Probabilistic Risk Value/Level (PRV), which can precisely generate the risk profile of any organization. The model is based on minimalist feature set covering risk assessment comprehensively paving the way for exact prescription for risk mitigation and control. Data regarding the risk metrices were collected from across the sectors/industries. Model validation and analysis shows that this model can be used for quantitative, automatic and dynamic risk analysis and evaluation in any industries/sectors.