Detecting Fraud in Narrative Annual Reports

Abstract

Annual reports present the activities of a listed company in terms of its operational performance, financial conditions, and social responsibilities. These reports also provide valuable reference for numerous investors, creditors, or other accounting information end-users. However, many annual reports exaggerate enterprise activities to raise investor capital and support from financial institutions, thereby diminishing the usefulness of such reports. Effectively detecting fraud in the annual report of a company is thus of priority concern during an audit. Therefore, this work develops a novel fraud detection method for narrative annual reports to effectively detect fraud in the narrative annual report of a company in order to reduce investment losses and investor- and creditor-related risks, as well as enhance investment decisions. A developmental procedure of fraud detection is designed for narrative annual reports. Fraud detection-related techniques are then developed for narrative annual reports, followed by a demonstration and evaluation of the proposed fraud detection method. Fraud detection-related techniques for narrative annual reports consist mainly of establishing a fraudulent feature term library and clustering fraudulent and non-fraudulent narrative annual reports. Moreover, establishing fraudulent feature term library involves data preprocessing, term-pair combination, and filtering of fraudulent feature terms.