Generative AI in Predictive Risk Modeling

Predictive risk modeling can be defined as the practice of using statistics and machine learning techniques to assess and evaluate the likelihood of risk occurrence or non-occurrence in the future. Accurate prediction of risk is critical for most contemporary decision-making in both the private and public sectors.

Recent years have witnessed a boom in the development of generative artificial intelligence, which refers to a category of AI models used to generate new content akin to human creation. Full reservoir simulation is a crucial component of the oil and gas industry, yet it is beset with several challenges: expensive costs, lengthy periods, and complex operations. To meet evolving requirements, the adoption of predictive analytics in the oil and gas sector is on the rise. This approach seeks to leverage historical, current, and real-time data to generate risk estimates, striving to create a predictive risk model capable of outperforming humans in predictive efficiency. Generative AI currently ranks among the most potent weapons against fraud, indicative, for instance, of how generative pre-trained transformer technology offers advanced tools to the financial industry for analysis and crime prevention.