Core Viewpoint - The auditing industry is experiencing collective anxiety due to the rapid advancement of technology, while traditional auditing methods remain stagnant, necessitating the integration of AI to enhance efficiency and accuracy in auditing processes [1][2]. Group 1: Challenges in Traditional Auditing - Traditional auditing relies heavily on manual processes, leading to inefficiencies and high labor costs, with a significant risk of missing critical data due to outdated sampling methods [2][3]. - The industry faces challenges such as subjective judgment affecting audit standards and the inability to thoroughly verify internal control systems [2][3]. Group 2: AI Empowerment in Auditing - AI technology offers solutions by enabling comprehensive data processing, risk identification, and automation of repetitive tasks, thus improving efficiency and precision in audits [2][3]. - Examples of successful AI applications include the use of algorithms for anomaly detection in large-scale audits, as demonstrated by a project completed by Elon Musk's team in just three days [3]. Group 3: Industry Adoption and Differentiation - Larger accounting firms are more inclined to adopt AI due to their resources and client base, allowing them to achieve economies of scale in AI applications [3][4]. - Smaller firms exhibit caution in adopting AI due to the lack of publicly available data and standardized information from their clients, which limits the effectiveness of AI tools [4]. Group 4: Real-World Challenges in AI Implementation - The implementation of AI in auditing faces several obstacles, including high initial costs, ongoing maintenance expenses, and the need for data integration [5][6]. - Data quality issues and the lack of standardized systems hinder effective model training, while the complexity of AI algorithms raises concerns about transparency and understanding among auditors [5][6]. Group 5: Collaborative Efforts for AI Integration - A multi-faceted approach involving policy, regulation, industry collaboration, and educational institutions is essential for the successful integration of AI in auditing [6][7]. - Recommendations include establishing a compliance review platform for AI in auditing, creating a data-sharing platform for historical financial data, and enhancing talent development through collaboration between educational institutions and the industry [7][8].
审计行业期待AI赋能 多重瓶颈如何突破？
Zheng Quan Shi Bao·2025-08-13 17:45