Financial Audit Assistance using Misinformation Detection and Explanation

2026-07-20

AI Summary

This study addresses the high cost and inefficiency of manual auditing in identifying misleading information in financial statements. It proposes an unsupervised approach that, for the first time, integrates historical financial statements with a corpus of audit reports to automatically detect potential misstatements and generate interpretable recommendations tracing suspicious signals back to relevant financial variables—all without requiring labeled data. By synergizing unsupervised learning, financial data association analysis, and insights extracted from audit narratives, the method significantly enhances auditors’ ability to focus on high-risk areas with greater efficiency and transparency. The effectiveness and practicality of the approach are validated through experiments on a large-scale dataset comprising 11,460 financial statements and their corresponding audit reports spanning five years.

Abstract

Financial statements (FS) such as Balance Sheet (BS), Income Statement (IS) and Cash-flow Statement (CS) summarize the annual financial performance of a company. FS are widely used for evaluating corporate governance, credit appraisal, risk analysis, validate taxation, make investment decisions etc. Financial auditing is a complex and knowledge-intensive discipline whose one important aim is ensuring integrity, accuracy, fairness and absence of material misstatement in the published FS. Given the importance of FS, there are incentives to hide, omit or falsify information to misrepresent the true financial health of the company; e.g., reduce tax liabilities, or increase investor confidence. Given the complex, time-consuming and expertise-dependent nature of auditing, auditors would benefit from an AI-assisted system that automatically detects instances of misinformation in the given FS and identify likely sources of this misinformation in the financial data. In this paper, we present unsupervised techniques to identify misinformation in FS, and also generate explanations as to the financial variables that are likely sources of misinformation. The auditor can then explore in more detail the associated data sources and business processes to validate these suggestions. A crucial feature of our approach is the use of past corpus of FS and associated audit reports to generate insights, which help in providing assistance. We demonstrate the efficacy of these techniques on a large corpus of 11,460 FS over 5 years and associated audit reports. This paper integrates and adds more novel contributions over the previously reported research (Shinde et al., 2022)\cite{SVAP22}, (Vaishampayan et al., 2022)\cite{VSPP22}, (Pawar et al., 2023)\cite{PAPV23}, which we have used as the foundation for our AI-assisted Auditor Assistance system.

Problem

• financial audit
• misinformation detection
• financial statements
• material misstatement
• audit assistance

Innovation

• misinformation detection
• unsupervised learning
• explainable AI
• financial auditing
• audit assistance