Prediction of Default for Financial Institutions with Machine Learning

Abstrakt

This paper investigates if Artificial Intelligence techniques can be used as an adequate model to calculate a standalone probability of default for counter parties. We start with introducing the legislative framework for the internal rating-based approach, Basel. Furthermore, before introducing the applied methods, we present the elementary concept of machine learning. In pursuance of the best applicable model we have conducted training and testing with three different models. For the chosen models, Neural Networks, Support Vector Machines and Random Forest underlying theory and mechanisms is introduced. With regards to performance assessment of the aforementioned models, several statistical evaluation and comparisons is conducted. The best performing model is the advanced option for decision trees, Random Forest. Nonetheless, the more complex Neural Networks and Support Vector Machines shows disappointing results, which is in conflict with some previous research. In contrary to previous findings this paper concludes that none of the tests can significantly outperform the comparative benchmark - logistic model. We do not wish to neglect the models entirely. Rather, this paper presents the challenges and importance of a satisfactory dataset.