A0497
Title: eXplainable AI for credit risk management
Authors: Joerg Osterrieder - ZHAW (Switzerland)
Ali Hirsa - Columbia University (United States)
Branka Hadji Misheva - ZHAW Zurich University of Applied Sciences (Switzerland) [presenting]
Abstract: Artificial Intelligence (AI) has created the single biggest technology revolution the world has ever seen. For the finance sector, it provides great opportunities to enhance customer experience, democratize financial services, ensure consumer protection and significantly improve risk management. While it is easier than ever to run state-of-the-art machine learning models, designing and implementing systems that support real-world finance applications have been challenging. In large part, this is due to the lack of transparency and explainability which in turn represent important factors in establishing reliable technology. The research on this topic with a specific focus on applications in credit risk management has been limited. We implement different advanced post-hoc model agnostic explainability techniques to machine learning (ML)-based credit scoring models applied to loan performance data. We present multiple comparison scenarios and we discuss in detail the practical challenges associated with the implementation of these state-of-art eXplainable AI (XAI) methods.
