Abstract
Finding misstatement accounts in financial statements, is a key problem of fraud detection. Potential applications include external audit, internal controls, investment decision and securities market regulation. However, most existing intelligent methods just detect financial statements fraud at the company level, while little research can detect financial statements fraud at the account level. For this, to achieve intelligent fraud detection at the accounts level, an ontology-based fraud detection framework was proposed. To be specific, the proposed framework mainly combines the articulation between different accounts and periods, and 30 financial indicators (ratios) as the knowledge basis of ontology. Notably, with OWL (Ontology Web Language), SWRL (Semantic Web Rule Language) and Protege ontology editor, the case study not only completed the fraud detection in a fast and timely manner, but also provided logical explanation and risk warning at the accounts level. This fully shows the great advantages and applicability of the proposed framework in the detection of misstatements accounts. Moreover, the proposed framework is of great significance for timely detection, prevention and response of financial statements fraud. More importantly, the proposed framework opens-up a new direction of using ontology reasoning techniques to find misstatement accounts in financial statements, which provides an interpretable and fine-grained way for fraud detection.
Highlights
On April 2, 2020, Luckin Coffee released a notice admitting that it had made 2.2 billion yuan of false transactions, which has seriously damaged the interests of investors, creditors and other stakeholders, and brought serious harm to social and economic life
Knowledge engineering, especially ontology model, has strong knowledge representation ability and reasoning ability, and can provide adequate explanation, which is very crucial for financial statements fraud detection and risk warning [5]
Intelligent ontology reasoning may be a powerful tool for financial statements fraud detection
Summary
On April 2, 2020, Luckin Coffee released a notice admitting that it had made 2.2 billion yuan of false transactions, which has seriously damaged the interests of investors, creditors and other stakeholders, and brought serious harm to social and economic life. For investors, regulators, creditors, auditors as well as other stakeholders, timely and effective financial fraud risk early warning framework is urgently needed to be put forward. The data-driven methods, such as machine learning, have made great progress in financial statements fraud detection. Knowledge engineering, especially ontology model, has strong knowledge representation ability and reasoning ability, and can provide adequate explanation, which is very crucial for financial statements fraud detection and risk warning [5]. Intelligent ontology reasoning may be a powerful tool for financial statements fraud detection. The existing intelligent detection methods, whether based on machine learning, data mining [7], or ontology model [6], both are company level detection. These methods can only give a conclusion whether the company is fraudulent or not, but cannot focus on specific risk accounts, or financial
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