发表机构
TCS Research（塔塔咨询服务公司研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对财务审计问题，提出无监督技术识别财务报表错误信息及来源，借助过去财务报表语料库和审计报告提供协助，经大量数据验证技术有效性，为财务审计提供新方法。
AI 中文摘要
财务报表（如资产负债表、损益表和现金流量表）总结公司年度财务表现，广泛用于多方面。财务审计旨在确保报表完整性等，但存在信息被隐藏、省略或伪造的情况。鉴于审计复杂耗时且依赖专业知识，本文提出无监督技术来识别财务报表中的错误信息并解释可能的来源，通过使用过去的财务报表语料库和审计报告生成见解以提供协助，还在大量语料库上验证了技术有效性。
英文摘要
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.
