Fast Approximation Methods for Credit Portfolio Risk Calculations

Abstract

Credit risk is one of the main risks financial institutions are exposed to. Within the last two decades simulation-based credit portfolio models became extremely popular and replaced closed analytical ones as computers became more powerful. However, especially for non-homogenous and non-granular portfolios a full simulation of a credit portfolio model is still time consuming, which can be disadvantage within some use cases like credit pricing or within stress testing situations where results must be available very quickly. For this purpose, we investigate if methods based on artificial intelligence (AI) can be helpful to approximate a credit portfolio model. We compare the performance of AI-based methods within three different use cases with those of classical regression methods. As a result, we see that generally AI-based methods are able to capture portfolio characteristics and to and speed-up calculations but depending on the specific use case and the availability of training data they are not necessarily always the best choice. Particularly, considering the time and costs for collecting data and training of the complex algorithms, classical methods can be as good as or even better as AI-based ones with less effort.