Finding Misstatement Accounts in Financial Statements through Ontology Reasoning

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 Proté gé 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.

Ensemble learning algorithm - research analysis on the management of financial fraud and violation in listed companies

“…This paper analyzed validated indicators for managing financial fraud violations and preliminarily determined the indicators for financial fraud as solvency, operational capacity, profitability, cash flow, development capacity, and non-financial variables. These five aspects were used to determine whether a listed company had committed financial fraud (Chen et al, 2020). At the same time, in order to avoid missing important indicators, as many indicators of financial fraud violations as possible were collected during the initial selection stage.…”