Zheng Xinyi, Abdul Hamid Mohamad Ali, Hou Yihua
Putra Business School, University Putra Malaysia, Selangor, Kuala Lumpur, 43400, Malaysia.
University of Malaya, 50603, Kuala Lumpur, Wilayah, Persekutuan Kuala Lumpur, Malaysia.
Heliyon. 2024 Apr 27;10(9):e30048. doi: 10.1016/j.heliyon.2024.e30048. eCollection 2024 May 15.
The identification of accounting fraud is an important measure to safeguard the interests of stakeholders and ensure the long-term development of the company. The current traditional methods for identifying accounting fraud rely on manual review and judgment, lacking objectivity and accuracy. In order to improve the accuracy of accounting fraud identification, improve identification efficiency and objectivity, this article combines smart city information technology to conduct in-depth research on data mining algorithms for accounting fraud identification. This article first provides a brief overview of smart cities and information technology, then introduces the basic theory of accounting fraud identification, and finally implements accounting fraud identification through k-means clustering mining algorithm. The data is divided into k clusters, and abnormal clusters are identified by checking the characteristics and attributes of each cluster. Compared with traditional rule-based and pattern based methods, this approach can more flexibly adapt to different types and forms of fraud, and can discover unknown patterns of fraud. In the experiment, this article used electronic data collection, analysis, and retrieval systems on the websites of the Shanghai Stock Exchange and Shenzhen Stock Exchange to collect 641 annual reports and financial characteristics from 62 listed companies that engaged in financial statement fraud and 84 companies that were not reported to have financial statement fraud from 2012 to 2021 as test samples. The results were tested and analyzed from several aspects, including the number of misjudgments, misjudgment rate, and ROC curve. The final test results show that compared to traditional accounting fraud identification methods, the comprehensive misjudgment rate of data mining algorithms based on smart cities has decreased by 3 %. The conclusion indicates that data mining algorithms used in smart city information technology to identify accounting fraud can help improve the accuracy of accounting fraud, improve audit objectivity and effectiveness.
会计欺诈识别是维护利益相关者权益、保障公司长远发展的一项重要举措。当前传统的会计欺诈识别方法依赖人工审查与判断,缺乏客观性和准确性。为提高会计欺诈识别的准确性、提升识别效率与客观性,本文结合智慧城市信息技术,对会计欺诈识别的数据挖掘算法展开深入研究。本文首先简要概述了智慧城市和信息技术,接着介绍了会计欺诈识别的基本理论,最后通过k均值聚类挖掘算法实现会计欺诈识别。将数据划分为k个聚类,通过检查每个聚类的特征和属性来识别异常聚类。与传统的基于规则和模式的方法相比,该方法能够更灵活地适应不同类型和形式的欺诈行为,且能发现未知的欺诈模式。在实验中,本文利用上海证券交易所和深圳证券交易所网站上的电子数据收集、分析和检索系统,收集了2012年至2021年期间62家存在财务报表欺诈行为的上市公司和84家未被举报存在财务报表欺诈行为的公司的641份年报及财务特征作为测试样本。从误判数量、误判率和ROC曲线等多个方面对结果进行了测试与分析。最终测试结果表明,与传统会计欺诈识别方法相比,基于智慧城市的数据挖掘算法综合误判率降低了3%。结论表明,利用智慧城市信息技术中的数据挖掘算法识别会计欺诈有助于提高会计欺诈识别的准确性,提升审计的客观性和有效性。