Technical Report On Explainability Of Ai (XAI) For Non-expert Users

Abstract

The adoption of artificial intelligence systems and techniques in the financial sector has significantly increased. These advancements in AI have brought numerous benefits, such as faster and more efficient data analysis, improved risk assessment, and enhanced customer experiences. However, the rapid integration of AI in FinTech also brings about challenges related to transparency of the methods (Misheva et al., 2021). In order to address these challenges, researchers and academics have been working on developing transparent AI methods (Bussmann et al., 2020). These methods can be easily understood and interpreted by industry professionals. However, there are still issues related to intelligibility of the models for end users when applied in the financial field. Therefore, it is crucial to study the reasons behind the black box problem and explore effective solutions to make AI models in FinTech more interpretable, especially for end users (Ashta & Herrmann, 2021).

This study aims to address this critical issue by leveraging the latest advancements in machine learning, explainable AI (XAI), and natural language processing with GPT-4o. By utilizing GPT-4o, a multimodal AI capable of processing both numerical-text data and visualizations, we can translate complex XAI outputs into easily understandable language and graphics. This translation is crucial for empowering users to understand their credit scores, identify areas for improvement, and make informed financial decisions.