Accounting fraud is a widespread problem that causes significant damage in the economic market. Detection and investigation of fraudulent firms require a large amount of time, money, and effort for corporate monitors and regulators. In this study, we explore how textual contents from financial reports help in detecting accounting fraud. Pre-trained contextual language learning models, such as BERT, have significantly advanced natural language processing in recent years. We fine-tune the BERT model on Management Discussion and Analysis (MD&A) sections of annual 10-K reports from the Securities and Exchange Commission (SEC) database. Our final model outperforms the textual benchmark model and the quantitative benchmark model from the previous literature by 15% and 12%, respectively. Further, our model identifies five times more fraudulent firm-year observations than the textual benchmark by investigating the same number of firms, and three times more than the quantitative benchmark. Optimizing this investigation process, where more fraudulent observations are detected in the same size of the investigation sample, would be of great economic significance for regulators, investors, financial analysts, and auditors.

中文翻译：

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使用情境语言学习检测会计欺诈


会计欺诈是一个普遍存在的问题，对经济市场造成重大损害。发现和调查欺诈公司需要企业监管者和监管者投入大量的时间、金钱和精力。在本研究中，我们探讨了财务报告中的文本内容如何帮助检测会计欺诈。近年来，预训练的上下文语言学习模型（例如 BERT）显着推进了自然语言处理。我们对来自证券交易委员会 (SEC) 数据库的年度 10-K 报告的管理讨论与分析 (MD&A) 部分的 BERT 模型进行了微调。我们的最终模型分别优于之前文献中的文本基准模型和定量基准模型 15% 和 12%。此外，通过调查相同数量的公司，我们的模型识别出的欺诈性公司年度观察结果是文本基准的五倍，是定量基准的三倍。优化这一调查过程，在相同规模的调查样本中发现更多的欺诈性观察结果，对于监管机构、投资者、金融分析师和审计师来说具有重大的经济意义。