Type:

• Bachelor Thesis Business Information Systems

Status:

offered

Tutor:

• M.Sc. Luca Gemballa

Abstract

Among the high-stakes decision-making contexts that use artificial intelligence (AI), finance is one of the fields that sticks out. However, applications such as fraud detection, credit scoring, and stock price forecasting still require insight into the black box of modern deep learning models. Even if poor decisions in finance, for instance, due to bias or poor data quality, do not directly harm people, they can negatively impact human well-being. Hence, AI explanations to foster trust and improve decision making are required. We intend to research explainable AI (XAI) in finance, which involves an analysis of use cases, methods, and previous experimental evaluations. 

To develop a better understanding of XAI in finance, the Bachelor student carries out a systematic literature review (SLR). This SLR is followed by a series of expert interviews to assess the current state of AI and XAI usage in the financial industry. The interviews must be recorded, transcribed, and analyzed (e.g., via tools like MAXQDA).