How Generative AI Can Solve a Traditional Risk Modeling Problem
How Generative AI Can Solve a Traditional Risk Modeling Problem
Financial risk modelers are now using generative AI to work more efficiently. Though the technology is still not yet fully developed, modelers can use it today, for example, to build proper sector-driven correlations, without overrelying on so-called expert human judgment.
Developing sector-driven correlation matrices is no small task for risk modelers. Traditionally, these modelers have used a combination of equity return data and expert judgment to measure correlations between different sectors – but this approach is imperfect.
Fortunately, thanks to the emergence of generative AI, there is now an alternative for building correlation matrices. ChatGPT, for example, can already provide relevant information on sector correlations, as well as technical assistance for the necessary “cleaning up” of the matrix.
Dr. Marco Folpmers
Generative AI is not yet fully evolved, but when it matures, it has the potential to significantly improve the process of building sector-driven correlation matrices – via eliminating, or at least decreasing, the current over-reliance on expert judgment.
We’ll examine the modeling benefits of generative AI more closely in a minute. First, though, let’s provide a bit of background about the obstacles risk modelers face in the traditional process for developing sector-based correlations.
