Explainable Automated Machine Learning for Credit Decisions: Enhancing Human Artificial Intelligence Collaboration in Financial Engineering

TL;DR

This paper investigates how combining Explainable AutoML with XAI techniques like SHAP can improve transparency, efficiency, and trust in AI-driven credit decision systems in financial engineering.

Contribution

It introduces a framework integrating AutoML and XAI for credit scoring, enhancing model interpretability and collaboration between humans and AI.

Findings

AutoML streamlines credit model development

SHAP explanations improve transparency

Enhanced trust and regulatory compliance

Abstract

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.