Antonio Dichev
DISCRIMINATIVE ABILITY IN ESTIMATING PROBABILITY OF DEFAULT WITH CERTAIN MACHINE LEARNING ALGORITHMS
Summary:
The article highlights the importance and added value of some machine learning algorithms in assessing default probability. The results of the research highlight the discriminative ability added to many other essential aspects of machine learning in assessing credit risk. These aspects can be identified as specific opportunities and challenges. As for the discriminative ability regarding the analysed sample, the results prove the superiority of machine learning over the traditionally established and known models. For individual business organizations with exposures to credit risk, machine learning could contribute to reducing the credit losses with larger volumes of business transactions.
