Artificial Intelligence Approach to Credit Risk
|Author:||Mgr. Jan Říha|
|Year:||2016 - winter|
|Leaders:|| doc. PhDr. Jozef Baruník Ph.D.
|Work type:|| Finance, Financial Markets and Banking
|Awards and prizes:|
|Abstract:||This thesis focuses on application of artificial intelligence techniques in credit risk management.
Moreover, these modern tools are compared with the current industry standard – Logistic Regression. We
introduce the theory underlying Neural Networks, Support Vector Machines, Random Forests and
Logistic Regression. In addition, we present methodology for statistical and business evaluation and
comparison of the aforementioned models. We find that models based on Neural Networks approach
(specifically Multi-Layer Perceptron and Radial Basis Function Network) are outperforming the Logistic
Regression in the standard statistical metrics and in the business metrics as well. The performance of the
Random Forest and Support Vector Machines is not satisfactory and these models do not prove to be
superior to Logistic Regression in our application.