Chen Jifan, Talha Muhammad
Research Center for Economy at the Upper Reaches of the Yangtze River, Chongqing Technology and Business University, Chongqing 400067, China.
Department of Computer Science, Superior University Lahore, Pakistan.
Comput Math Methods Med. 2021 Nov 15;2021:2059432. doi: 10.1155/2021/2059432. eCollection 2021.
Traditional audit data analysis algorithms have many shortcomings, such as the lack of means to mine the hidden audit clues behind the data, the difficulty of finding increasingly hidden cheating techniques caused by the electronic and networked environment, and the inability to solve the quality defects of the audited data. Correlation analysis algorithm in data mining technology is an effective means to obtain knowledge from massive data, which can complete, muffle, clean, and reduce defective data and then can analyze massive data and obtain audit trails under the guidance of expert experience or analysts. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounds the research status and significance of audit data analysis and application; elaborates the development background, current status, and future challenges of correlation analysis algorithm; introduces the methods and principles of data model and its conversion and audit model construction; conducts audit data collection and cleaning; implements audit data preprocessing and its algorithm description; performs audit data analysis based on correlation analysis algorithm; analyzes the hidden node activation value and audit rule extraction in correlation analysis algorithm; proposes the application of audit data based on correlation analysis algorithm; discusses the relationship between audit data quality and audit risk; and finally compares different data mining algorithms in audit data analysis. The findings demonstrate that by analyzing association rules, the correlation analysis algorithm can determine the significance of a huge quantity of audit data and characterise the degree to which linked events would occur concurrently or sequentially in a probabilistic manner. The correlation analysis algorithm first inputs the collected audit data through preprocessing module to filter out useless data and then organizes the obtained data into a format that can be recognized by data mining algorithm and executes the correlation analysis algorithm on the sorted data; finally, the obtained hidden data is divided into normal data and suspicious data by comparing it with the pattern in the rule base. The algorithm can conduct in-depth analysis and research on the company's accounting vouchers, account books, and a large number of financial accounting data and other data of various natures in the company's accounting vouchers; reveal its original characteristics and internal connections; and turn it into an audit. People need more direct and useful information. The study results of this paper provide a reference for further researches on audit data analysis and application based on correlation analysis algorithm.
传统的审计数据分析算法存在诸多缺陷,比如缺乏挖掘数据背后隐藏审计线索的手段,难以发现由电子和网络环境导致的日益隐蔽的作弊技巧,以及无法解决被审计数据的质量缺陷。数据挖掘技术中的关联分析算法是从海量数据中获取知识的有效手段,它可以对有缺陷的数据进行补齐、消噪、清理和规约,进而能够对海量数据进行分析,并在专家经验或分析师的指导下获取审计线索。因此,在总结和分析以往研究工作的基础上,本文阐述了审计数据分析与应用的研究现状及意义;详细说明了关联分析算法的发展背景、现状以及未来面临的挑战;介绍了数据模型及其转换和审计模型构建的方法与原理;进行了审计数据的收集与清理;实现了审计数据预处理及其算法描述;基于关联分析算法进行了审计数据分析;分析了关联分析算法中的隐藏节点激活值和审计规则提取;提出了基于关联分析算法的审计数据应用;探讨了审计数据质量与审计风险之间的关系;最后比较了审计数据分析中不同的数据挖掘算法。研究结果表明,通过分析关联规则,关联分析算法能够确定大量审计数据的重要性,并以概率方式表征相关联事件同时或相继发生的程度。关联分析算法首先通过预处理模块输入收集到的审计数据,过滤掉无用数据,然后将获取的数据组织成数据挖掘算法能够识别的格式,并对排序后的数据执行关联分析算法;最后,将获得的隐藏数据与规则库中的模式进行比较,将其分为正常数据和可疑数据。该算法能够对公司会计凭证、账簿以及公司会计凭证中大量具有各种性质的财务会计数据等进行深入分析和研究;揭示其原始特征和内在联系;并将其转化为审计人员更需要的直接有用的信息。本文的研究成果为基于关联分析算法的审计数据分析与应用的进一步研究提供了参考。