Corporate Fraud Detection in Rich-yet-Noisy Financial Graph

Corporate fraud detection aims to automatically recognize companies that conduct wrongful activities such as fraudulent financial statements or illegal insider trading. Previous learning-based methods fail to e ffectively integrate rich interactions in the company network. To close this gap, we collect 18-year financial records in China to form three graph datasets with fraud labels. We analyze the characteristics of the financial graphs, highlighting two pronounced issues: (1) information overload: the dominance of (noisy) non-company nodes over company nodes hinders the message-passing process in Graph Convolution Networks (GCN); and (2) hidden fraud: there exists a large percentage of possible undetected violations in the collected data. The hidden fraud problem will introduce noisy labels in the training dataset and compromise fraud detection results. The proposed model adopts a two-stage learning method to enhance robustness against hidden frauds. Introduction Corporate fraud refers to illegal schemes by listed companies in the stock market, aiming at financial gains through di ff erent means such as fraudulent financial statements and illegal insider trading. This kind of fraud bears systematic risks, which can potentially lead to financial crises at the macro level [1]. Unfortunately, the rapid growth of young capital markets has given rise to an increasing number of fraudulent cases in recent years, putting pressure on regulators and auditors. Since the traditional human supervision solution is no longer effi cient, it is desirable to build an autonomous system to assist regulators in this essential task. These machine-learning models are built to classify annual financial statements as fraudulent or not, based on expert-chosen feature sets. Unfortunately, the rich interactions in the company network have not been e ffec-tively integrated for corporate fraud detection. Financial experts, on the other hand, have recognized the influence of "Directors / Supervisors / Executives (DSE)" and "Related Party Transactions (RPT)" on corporate fraud (see Figure 1). DSE refers to the members of the director board of the company. Being the decision-making body in a company, the director board is certainly the agent behind most corporate frauds [6]. Connection via DSE also helps companies lower the coordination cost for illegal activities, thus significantly increasing the likelihood of committing fraud [7]. RPT refers to deals or arrangements between two companies that are joined by a previous business association or share common interests. RPTs, particularly those that go unchecked, carry the risk of financial fraud by various means such as illegal profit transmission [8, 9].