Explainable Automated Machine Learning for Credit Decisions: Enhancing
  Human Artificial Intelligence Collaboration in Financial Engineering
Marc Schmitt
This paper explores the integration of Explainable Automated Machine Learning
(AutoML) in the realm of financial engineering, specifically focusing on its
application in credit decision-making. The rapid evolution of Artificial
Intelligence (AI) in finance has necessitated a balance between sophisticated
algorithmic decision-making and the need for transparency in these systems. The
focus is on how AutoML can streamline the development of robust machine
learning models for credit scoring, while Explainable AI (XAI) methods,
particularly SHapley Additive exPlanations (SHAP), provide insights into the
models' decision-making processes. This study demonstrates how the combination
of AutoML and XAI not only enhances the efficiency and accuracy of credit
decisions but also fosters trust and collaboration between humans and AI
systems. The findings underscore the potential of explainable AutoML in
improving the transparency and accountability of AI-driven financial decisions,
aligning with regulatory requirements and ethical considerations.