Abstract

     Financial fraud has extremely damaged the sustainable growth of financial markets as a serious problem worldwide. Nevertheless, it is fairly challenging to identify frauds with highly imbalanced dataset because ratio of non-fraud companies is very high compared to fraudulent ones. Intelligent financial statement fraud detection systems have therefore been developed to support decision-making for the stakeholders. However, most of current approaches only considered the quantitative part of the financial statement ratios while there has been less usage of the textual information for classifying, especially those related comments in Chinese. As such, this paper aims to develop an enhanced system for detecting financial fraud using a state-of-the-art deep learning models based on combination of numerical features that derived from financial statement and textual data in managerial comments of 5130 Chinese listed companies’ annual reports. First, we construct financial index system including both financial and non-financial indices that previous researches usually excluded. Then the textual features in MD&A section of Chinese listed company’s annual reports are extracted using word vector. After that, powerful deep learning models are employed and their performances are compared with numeric data, textual data and combination of them, respectively. The empirical results show great performance improvement of the proposed deep learning methods against traditional machine learning methods, and LSTM, GRU approaches work with testing samples in correct classification rates of 94.98% and 94.62%, indicating that the extracted textual features of MD&A section exhibit promising classification results and substantially reinforce financial fraud detection.

Introduction

     With the boom of the securities market in last decades, more and more companies raise capital and expand the operation scale through listing, especially in fast growing counties like China. Accompanied by financial market development, fraudulent financial reports have cast rapidly, and have caused dramatic losses to shareholders with negative impacts on capital markets [1], [2]. The Enron scandal in the U.S. in 2001 and the global financial crisis spanning 2008–۲۰۰۹ have severely damaged the world economy [3]. In China, the number of criminals involved with fraudulent activities in 2019 is more than 961 with a value of more than $8 billion [4]. Although there are minor variations in its definition, a financial statement fraud is referred as ‘‘deliberate fraud committed by management that injures investors and creditors through misleading financial statements’’ [۲]. Generally speaking, the main reason for fraud is due to the inaccurate reports of CPAs and auditors. In addition, companies with rapid growth may exceed the monitoring process ability to provide appropriate supervision. According to report issued in 2020, only a limited number of fraud cases were identified by internal and external auditors with rates of 14% and 5%, respectively [5]. As a result, effective detecting financial fraud has always been an important but rather challenging task for accounting and auditing professionals given that the economic and social consequences can be massive [5], [6].

Conclusion and Future Research Directions

     While financial fraud has a negative impact on economic and social development, it also causes huge losses to different stakeholders. However, detecting financial statement fraud is fairly challenging using traditional approaches due to companies’ stratagem. Our main purpose of conducting this research is building models with high classification performance and deriving classification framework which can be used to detect the frauds with textual and numeric data in Chinese listed companies’ annual reports. As the most advanced information processing technology, deep learning has made great achievements in many applications. In this way, this paper gives a framework for how this technique can be used in financial statement detection with Chinese companies’ annual reports. Besides numerical data in financial statements, we analyze the ability of textual data attached to annual reports in financial statement fraud prediction and highlight the importance of textual analytics for detecting fraud with financial documents. Also, the results have shown that the deep learning models achieved considerable improvements in AUC compared to the earlier studies on the financial fraud detection. Furthermore, the textual information of the MD&A section of annual reports extracted through deep learning has the ability to improve the accuracy of financial statement fraud model detection, particularly in the highly unbalanced case of fraud detection.