Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/309477 
Year of Publication: 
2023
Citation: 
[Journal:] Digital Finance [ISSN:] 2524-6186 [Volume:] 5 [Issue:] 3 [Publisher:] Springer International Publishing [Place:] Cham [Year:] 2023 [Pages:] 689-716
Publisher: 
Springer International Publishing, Cham
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-form 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 disadvantageous 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 suitable non AI-based regression methods. As a result, we see that AI-based methods can generally capture portfolio characteristics 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, non-AI-based methods can be as good as or even better than AI-based ones, while requiring less computational effort.
Subjects: 
Credit risk
AI
Credit portfolio model
Approximation
JEL: 
C63
G32
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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