The Role of Audit Partner Characteristics in Assignments and Audit Quality --An Explainable AI Approach

Abstract

This study explores the role of a broad set of audit partner characteristics in deciding engagement partner assignments and audit outcomes in the U.S. Using machine learning and explainable AI, results show that partners' industry expertise, busyness, attractiveness, work experience, and previous failure rate are the primary factors influencing partner assignments. A novel measure of client-partner matching quality is introduced, which demonstrates significant predictive power for audit quality, highlighting its potential for explaining and enhancing audit quality. However, no evidence suggests that poor matching quality leads to voluntary partner rotations or improved subsequent matching. By integrating ML and XAI into auditing research, this study provides a nuanced understanding of partner assignments, introduces a novel framework for assessing matching quality, and offers practical insights for audit firms, regulators, and stakeholders.

Keywords: Audit Partner, Audit Quality, Partner Assignment, Machine Learning, Explainable AI, LinkedIn, Individual-level Characteristics, Client-Partner Matching Quality JEL Classification: C18, C55, M42