Artificial Intelligence and Machine Learning usage in credit risk management - A study from the Swedish financial services industry

Abstract

Credit risk management is a fundamental process established in almost every financial institution. There are various tools and methods that financial institutions can use in order to mitigate risks among their loan takers. Credit scoring is a standard method used to evaluate risks among loan applicants, and it can be done by traditional statistical methods as well as Artificial Intelligence- and Machine Learning methods. This thesis presents a survey result among different Swedish financial institutions on their use of Artificial Intelligence and Machine Learning solutions in credit risk management. The results find that Artificial Intelligence and Machine Learning is moderately used with ambitions to further increase the use. The credit risk management process still heavily relies on traditional statistical credit scoring methods as there are regulations, end user perspectives, ethical dilemmas, and IT aspects that still need to be addressed in order to fully enable an implementation of Artificial Intelligence and Machine Learning in this area. A combination of traditional statistical and Artificial Intelligence and Machine Learning solutions is seen as an optimal way forward.