Automated Retrieval and Categorization of SEC Data Using Python

ABSTRACT Timely and accurate detection of fraudulent activities is important for understanding patterns and trends and implementing preventive measures. Accounting and Auditing Enforcement Releases (AAERs) are frequently used as a proxy for fraudulent financial reporting in accounting literature. However, manual processing makes analyzing these documents time-consuming and error-prone. Recognizing the standardized nature of AAERs, this paper presents an innovative automated approach using Python. Our methodology enhances the process of downloading AAER files from the U.S. Securities and Exchange Commission (SEC) website, introduces a structured categorization approach, and facilitates the alignment of the resulting AAER data with information from the EDGAR database. Our findings show improvements in the efficiency and accuracy of data access and categorization. The paper contributes to the field by providing a replicable, scalable approach for AAER retrieval and systematic categorization, enhancing the capacity of researchers to uncover insights into fraudulent activities and inform regulatory practices. Data availability: The datasets generated and analyzed during the study are publicly available.