AI and Machine Learning for Credit Rating Models: Part III - The power of machine learning
In this third part of our series on AI and Machine Learning (ML) for Credit Rating Models, we look at how ML techniques can be used either exclusively or combined with traditional approaches to tackle some of the common risk-related challenges facing banks today. Furthermore, we demonstrate in our brief analysis how ML algorithms have stronger predictive capabilities versus the traditional logistic regression approach. However, the adoption of ML requires careful consideration and balancing the benefits versus costs of implementation.
Read our published briefing here.
If you missed our previous posts, please follow the links below:
Part I - A brief overview of the regulatory landscape
Part II - The foundations of machine learning
For more information or if you have any questions please contact:
Dr. Andreas Peter
+49 160 583 40 66
Dr. Tobias Kesselring
Fintegral Schweiz AG
+41 79 271 19 00
Fintegral UK Ltd.
+44 7496 363 298