International Journal of Accounting Research

Financial Fraud Risk Based on Audit Data Using Market Analysis

Flavio Bilac^* Department of Economics and Management, Federal University of Maranhao, Maranhao, Brazil
^*Correspondence: Flavio Bilac, Department of Economics and Management, Federal University of Maranhao, Maranhao, Brazil, Email:

Received: 04-Nov-2022, Manuscript No. IJAR-22-19066; Editor assigned: 07-Nov-2022, Pre QC No. IJAR-22-19066 (PQ); Reviewed: 25-Nov-2022, QC No. IJAR-22-19066; Revised: 01-Dec-2022, Manuscript No. IJAR-22-19066 (R); Published: 08-Dec-2022, DOI: 10.35248/2472-114X.22.10.305

Description

The healthy growth of the capital market has been significantly hampered by the regular occurrence of financial fraud committed by listed corporations. The effectiveness of audit opinions or the association between auditor change rates and financial fraud are the typical methods used in existing studies to examine financial fraud, which ignores the relationships between different participants. This study builds an audit information knowledge graph based on the audit linkages between corporations, audit firms, and auditors and presents a knowledge graph reasoning framework based on the Sub Feature Extraction approach to identify potentially fraudulent organisations. When 376 firms in the China Growth Enterprises Market had their audit data examined between 2013 and 2019, it was discovered that by using searched paths to search from the known fraud corporations, potential fraud corporations may be easily recognized. Additionally, they discover two additional audit characteristics of financial fraud organisations that are connected to both aberrant associations of audit firms and abnormal audit opinions issued by auditors. They can aid authorities in more efficiently identifying the businesses that need to be closely watched for signs of financial wrongdoing.

Financial fraud incidents involving publicly traded corporations have been more common in recent years, which has drawn a lot of attention from regulatory bodies throughout the world. The financial reporting fraud hurts investors' interests and ruins the capital market's resource allocation, impeding the market's ability to grow normally. The case of Luckin Coffee is indicative of financial deception. Having acknowledged the existence of financial deception, the company's stock price fell by 80%, severely harming the company's reputation. Financial reporting fraud produces the biggest economic loss of all types of fraud, with a median loss of 954,000 dollars, according to the Association of Certified Fraud Examiners' (ACFE) 2020 Global Study on Occupational Fraud and Abuse. Strengthening financial market oversight and reducing the danger of financial fraud are therefore crucial. The conventional method for detecting financial fraud in publicly traded companies involves a manual audit of the financial statements and an examination of the financial ratios, both of which take time and rely on the expert's subjective assessments. The current body of research has successfully explored these issues and enhanced the effectiveness and precision of fraud detection. For instance, the regression model or machine learning method uses the company's financial metrics. There are studies to examine the fraud risk by examining the effectiveness of audit opinion on fraud identification and features of the auditor change rate of fraud corporations, taking into account that the audit information might represent the audit findings and auditor's attitude.

However, organisations frequently rely on outsiders in order to hide their fraudulent activities, including auditors and audit firms, which causes the fraud corporations to develop strange relationships with other entities. The use of information from several sources is generally underutilized in the current study, which does not account for this kind of link relationship. The explosion of information due to information technology's quick development presents more demands and difficulties for the detection of fraudulent organisations and the mining of fraudulent features. Creates an audit information knowledge graph based on the audit information of 376 businesses in the China Growth Enterprises Market from 2013 to 2019 in the China Stock Market & Accounting Research (CSMAR) database in order to address the aforementioned difficulties. We search the knowledge network for prospective fraud firms using the inductive reasoning theory's typical path mining method. fully utilizes audit information and takes into account the relationships between various entities in identifying fraud corporations, but it also provides the characteristics of fraud corporations and improves the interpretability, reducing the risk of fraud.