Financial statement fraud is an area of significant consternation for potential investors, auditing companies, and state regulators. The paper proposes an approach for detecting statement fraud through the combination of information from financial ratios and managerial comments within corporate annual reports. We employ a hierarchical attention network (HAN) to extract text features from the Management Discussion and Analysis (MD&A) section of annual reports. The model is designed to offer two distinct features. First, it reflects the structured hierarchy of documents, which previous approaches were unable to capture. Second, the model embodies two different attention mechanisms at the word and sentence level, which allows content to be differentiated in terms of its importance in the process of constructing the document representation. As a result of its architecture, the model captures both content and context of managerial comments, which serve as supplementary predictors to financial ratios in the detection of fraudulent reporting. Additionally, the model provides interpretable indicators denoted as “red-flag” sentences, which assist stakeholders in their process of determining whether further investigation of a specific annual report is required. Empirical results demonstrate that textual features of MD&A sections extracted by HAN yield promising classification results and substantially reinforce financial ratios.

财务报表舞弊对于潜在的投资者，审计公司和州监管机构而言是一个重大问题。本文提出了一种通过结合财务比率信息和公司年度报告中的管理评论来检测对账单欺诈的方法。我们采用了分级关注网络（HAN）从年度报告的“管理讨论和分析（MD＆A）”部分提取文本特征。该模型旨在提供两个不同的功能。首先，它反映了以前的方法无法捕获的结构化的文档层次结构。其次，该模型在单词和句子级别体现了两种不同的注意力机制，这使得内容在构建文档表示过程中的重要性方面有所不同。由于其体系结构，该模型可以捕获管理评论的内容和上下文，这些信息可以作为检测欺诈性报告时财务比率的补充指标。此外，该模型还提供了以“红色标记”语句表示的可解释的指标，可帮助利益相关者确定是否需要对特定年度报告进行进一步调查。实证结果表明，HAN提取的MD＆A部分的文本特征产生了可喜的分类结果，并大大增强了财务比率。该模型提供了以“红色标记”句子表示的可解释的指标，可帮助利益相关者确定是否需要对特定年度报告进行进一步调查。实证结果表明，HAN提取的MD＆A部分的文本特征产生了可喜的分类结果，并大大增强了财务比率。该模型提供了以“红色标记”句子表示的可解释的指标，可帮助利益相关者确定是否需要对特定年度报告进行进一步调查。实证结果表明，HAN提取的MD＆A部分的文本特征产生了可喜的分类结果，并大大增强了财务比率。