PE-EDD:An efficient peer-effect-based financial fraud detection approach in publicly traded China firms

Financial fraud arises from the exaggeration of business interests,and an accurate detection or prediction is a useful tool for both corporate management and capital market systems.A collection of computer technologies has been made on this problem so far,and one of the most important solutions is unsupervised learning algorithms.Among them,most approaches work by analysing the internal relations in financial data and finding a new description of non-fraud firms.However,current studies focus a lot on the geometry attribute of financial data,while overlooking the obvious behaviour patterns and peer effects among firms.This has limited the accuracy of representation and furthermore the detection performance.In this work,a very general class of functions is allowed to represent firms,constraining them by peer effects between firms and presenting an error-distribution-based financial fraud firm detection approach.Experimental results have shown great performance of the proposed approach.

Financial Statements Frauds: Does Their Intensity Have a Connection with Earnings Management?

This study investigates the relationship between earnings management and financial statements frauds.We examine how earnings management practices;done in the two years before the fraud,impact the likelihood of fraud occurrence.Moreover,we introduce a new measure for the fraud intensity.Using a sample of 70 fraud and 70 no-fraud firms,we find that firms committing fraud of higher intensity have managed earnings in the two years before the fraud occurrence.This paper contributes to the literature about fraud antecedents because it is the first study measuring the relationship between earnings management and the intensity of the fraud,and it can be also useful for practitioners,because using the analysis of earnings management practices,analysts can foresee and prevent financial statements frauds.

Financial Statement Fraud: Lessons Learned from Selected U.S. Legal Cases in the past Twenty Years

The researcher analysed selected U.S.court cases of financial statement fraud from the past twenty years and concluded:(1)U.S.laws used to prosecute financial statement fraudsters include the Securities Act,Securities Exchange Act,S.E.C.Rule 10b-5,Investment Advisers Act,Private Securities Litigation Reform Act,and the Sarbanes-Oxley Act;(2)financial statement fraud schemes included use of unconsolidated special-purpose entities to conceal debt,improper revenue recognition in leasing of bandwidth in the telecom industry,improper accounting for goodwill in a merger,failure to write off a large amount of bad receivables,excessive recognition of revenue in bundled leases in the photocopier industry,use of loss contingency reserves to inflate current income,use of fictitious revenue to overstate earnings,and capitalising a project’s operating expenses to hide cost overruns;(3)to be liable for financial statement fraud,a corporate officer must have knowledge;and(4)employees discharged for reporting financial statement fraud may sue under the Sarbanes-Oxley Act.

Effectiveness of the Financial Ratios in the Determination of the Fraudulent Financial Statements＂ Turkey Practice

Large-scale accounting scandals which were reflected to the world public opinion particularly in the 2000s （such as Enron, Lucent, Xerox, and Parmalat Bank for Reconstruction） carried the matter of fraudulent financial reporting which was made to deceive the financial statement users （Fraudulent Financial Report （FFR）） to the forefront in the agenda of the academicians, operators, and regulatory authorities. As in every crime action, the most effective measure to be taken in preventing FFR events is to try to prevent the FFR before arising. In order to achieve this, in the most effective manner, FFR events should be determined in the formation process. In this study, fraudulent financial statements are tried to be determined by using financial ratios. For this, financial statements of 22 companies which transact in the textile industry in Istanbul Stock Exchange （ISE） were examined. Twenty-three financial ratios were selected for the purpose of determining the risk of fraudulence in the financial statements of the selected companies. These ratios increased in value by multiple regression analysis. The findings which were obtained in the study indicated that some financial statements had the risk of fraudulence. It was concluded that the ratios of inventory/current asset, total debt ratio, and equity turnover rate were a good indicator in the determination of fraudulent financial statements.

Study on how to elevate the discriminating ability of the discriminating model of the financial statement fraud

By summarizing the factor of the financial statement fraud in existing research outcome, the paper confirms the discriminating characteristic of the financial statement fraud and sets up a theoretic model to discriminate the financial statement fraud. Using radial basis function neural network, regarding to the swatch that the listed company that is punished by the Securities Regulatory Commission or the Ministry of Finance, and according to the clustering elements, validating across by set one aside, the paper verifies respectively the 22 characteristics and 31 characteristics of discriminating model. According to the clustering elements, validating across by set one aside, the paper verifies respectively the 31 characteristics and 8 characteristics selected by Fisher-ratio of discriminating model. The research outcome indicates the discriminating ability of the model including 8 characteristics is better elevated than the traditional model including 31 characteristics by comparing the disciplinary error and the forecast precision.

Positive Research on the Financial Statement Fraud Factors of Listed Companies in China

This paper studied the ＂pressure＂ and ＂opportunity＂ factors that caused financial statement fraud on the basis of the data of 41 A-share listed companies （1999-2004） in China which had been forfeited by China Securities Regulatory Commission （SRC） because of accounting irregularities. The author found that avoiding ＂ST＂ and ＂PT＂ was the primary pressure, and the opportunities mainly came from the higher top 10 shareholders＇ ownership concentration, the lower proportion of independent directors, the fewer number of directorate meetings and shares owned by the directorate members, board chairman and CEO held by one person and the ineffective supervisor boards. We also found that the companies involved financial statement fraud had the lower first majority shareholder＇s share proportion and they changed CPA firm more frequently.

Luckin Coffee:A Study of the Reasons for Committing Financial Fraud and Solutions for Getting Out of a Financial Crisis

Luckin Coffee grew rapidly in the past few years and it was the fastest Chinese Concept company go public in NASDAQ.However,its stock price plunged in 2020 due to financial fraud.It is crucial for Luckin Coffee to think about how to recover from the financial fraud and become a business representative of the“new retail”under the Internet model again.To recover from the financial crisis caused and gain profit,Luckin could improve its existing business model by improving product development,strengthening capital management in both marketing and expansion.The internal structure can also be adjusted by diversifying equity and replacing the manager team.Taking Luckin Coffee as an example,this paper analyzes the causes of financial fraud,propose solutions to recover from it,and evaluate the effectiveness of solutions in the context of reality.

Toshiba and Luckin Coffee: A Study of the Reasons for Committing Financial Fraud

Financial fraud,which has become a global issue,is a subject of discussion,surpassing time.Financial fraud significantly undermines investors’confidence and affects the health of capital markets.Hence,it is valuable to explore the reasons for committing financial fraud and propose solutions to this issue.This paper focuses on two financial fraud cases in recent years,Toshiba in 2015 and Luckin Coffee in 2020,analyzes and compares the reasons for the financial fraud in terms of pressure and opportunity factors,as well as proposes comprehensive suggestions for dealing with the corporate financial fraud issue.

Principal Model Analysis Based on Bagging PLS and PCA and Its Application in Financial Statement Fraud

Motivated by the Bagging Partial Least Squares(Bagging PLS)and Principal Component Analysis(PCA)algorithms,a novel approach known as Principal Model Analysis(PMA)method is introduced in this paper.In the proposed PMA algorithm,the PCA and the Bagging PLS are combined.In this method,multiple PLS models are trained on sub-training sets,derived from the training set using the random sampling with replacement approach.The regression coefficients of all the sub-PLS models are fused in a joint regression coefficient matrix.The final projection direction is then estimated by performing the PCA on the joint regression coefficient matrix.Subsequently,the proposed PMA method is compared with other traditional dimension reduction methods,such as PLS,Bagging PLS,Linear discriminant analysis(LDA)and PLS-LDA.Experimental results on six public datasets demonstrate that our proposed method consistently outperforms other approaches in terms of classification performance and exhibits greater stability.Additionally,it is employed in the application of financial statement fraud identification.PMA and other five algorithms are utilized to financial statement fraud which concerned by the academic community,and the results indicate that the classification of PMA surpassed that of the other methods.

Review of Financial Pressure

Different from foreign capital markets,china’s domestic capital markets are special,which also determines that the research on financial pressure starts from the reform of state-owned enterprises,and draws lessons from the relevant theories of financial risk and financial fraud,thus gradually forming a more diversified research results.

数字普惠金融对电信网络诈骗影响的实证研究

电信网络诈骗活动日益频繁,严重影响经济和社会稳定,侵害了广大人民群众的利益。将2015年中国家庭金融调查(CHFS)中的诈骗模块数据与中国数字普惠金融指数相结合,考察数字普惠金融对家庭成为诈骗目标的风险、实际受骗风险和受骗损失的影响。研究表明:数字普惠金融增加了家庭成为电信网络诈骗目标的概率,但能帮助家庭在成为诈骗目标时降低受骗风险,减少经济损失;异质性分析方面,数字普惠金融抑制电信网络诈骗的积极效果在农村和东部地区更加显著,当家庭成员属于数字鸿沟中的优势群体时,数字普惠金融会对其产生更好的降低受骗风险并减少损失的效果;作用机制方面,数字普惠金融主要通过增加家庭的金融可得性、缓解融资约束以及提高家庭成员对金融经济信息关注度来降低居民实际受骗风险,减少损失;进一步分析发现,金融监管、家庭成员金融素养提升和社区宣传教育能增强数字普惠金融对电信网络诈骗的抑制效果。

ESG表现会抑制企业财务舞弊吗?——来自A股上市企业的经验证据

在企业日益关注社会责任与可持续发展的大背景下,ESG(环境、社会和公司治理)表现成为评估企业综合价值的重要指标。基于2013—2022年中国A股上市企业数据,考察ESG表现对企业财务舞弊的影响。研究结果表明,ESG表现能够抑制企业财务舞弊行为,包括降低舞弊发生概率和严重程度。机制研究表明,ESG表现通过降低信息不对称程度、缓解融资约束和降低代理成本进而抑制企业财务舞弊。异质性分析表明,ESG表现抑制企业财务舞弊的作用在内部控制质量差、非国有以及法制环境程度高的样本中更加显著。经济后果检验发现,ESG表现通过抑制企业财务舞弊促进财务绩效的提升、降低企业的经营风险。

立德树人视域下电信诈骗防范路径研究

互联网已成为电信诈骗的主要载体,受骗者以20岁以下的青年群体居多,给青年成长、社会安稳造成巨大伤害。该文归纳了近5年来高校电信诈骗的常见类型,从中分析出大学生受骗的四大因素,并基于思想政治教育的德育引导和财经素养教育对防范电信诈骗提出四项对策,通过树立正确的财富观、健康的消费观、牢固的价值观,提升青年学生的危机规避力、风险防范力。

基于深度学习和文本情感的上市公司财务舞弊识别方法

上市公司财务舞弊是指采用不当手段使会计信息失真的恶劣失信行为,对公司经营、经济发展和社会利益产生负面影响。目前,更多研究关注于金融数字数据,对文本信息和深度学习算法的研究较少,因此提出了一种基于深度学习并融合文本情感特征的上市公司财务舞弊识别方法。该方法首先对财务指标进行预处理,并使用BiLSTM提取股评文本的情感特征。然后使用RCC(residual-cross-convolutional)并行网络进行财务舞弊识别,其采取残差网络、交叉网络、卷积网络和长短期记忆网络并行的方式提取财务舞弊特征,并使用批标准化和全连接得到最终的识别结果。实验结果表明,该方法在识别上市公司财务舞弊的任务上较其他模型取得了更好的效果,召回率和AUC分别达到了88.46%和82.06%。

基于非平衡MD&A文本数据的财务欺诈识别

财务欺诈不仅会导致会计信息失真,还会危害经济的健康发展。因此,找到一种高效的智能化欺诈识别方法具有重要的现实意义。本文基于2020—2022年美国上市公司提交到EDGAR数据库的年度报告,聚焦于报告中管理层讨论与分析部分的文本信息(Management Discussion and Analysis,MD&A)并对其进行分析。考虑到现有数据中欺诈和非欺诈样本数据极度不平衡的特点,本文在分层注意力网络的基础上设计了一个更高效的财务欺诈识别模型,最终使得欺诈识别模型的F1分数和F2分数分别提高了4.1%和3.7%,所提出的算法框架能够有效提高非平衡MD&A文本数据集的分类正确率。研究结果为财务欺诈识别系统性能的提高以及其他领域长文本分类任务的预测提供了新的解决思路,并进一步验证了使用MD&A文本数据进行财务欺诈识别的有效性,为使用非平衡数据进行欺诈识别提供了直接的实证支持。

融合多源图特征的Kcore-GCN反欺诈算法研究

金融欺诈行为给社会带来了许多负面影响,针对金融欺诈行为,多种人工智能与金融反欺诈算法被提出并应用于实际反欺诈业务场景,取得了不错的成绩。这些反欺诈算法或从用户个体的角度进行欺诈检测,或从节点与网络的拓扑关系的角度进行欺诈检测,或通过学习节点的图嵌入式表示进行欺诈检测,出发角度较为局限,无法进行完备的欺诈检测分析。针对上述问题,设计了一种基于融合多源图特征的Kcore图卷积神经网络反欺诈算法,该算法的创新性在于能够高效挖掘网络中节点层级的拓扑关系与全局网络层次的拓扑关系来构建宽领域的特征体系,并通过基于Kcore算法的图卷积神经网络完成深层次图结构特征的传播与聚合,最终完成欺诈风险的检测。实验效果表明,该方法相较于相关机器学习算法与图神经网络算法在相关评价指标上均有较大的提升,其中较LightGBM算法有12%的AUC值提升,较GCN算法有6%的AUC值提升。

面向金融风险预测的时序图神经网络综述

金融风险预测在金融市场监管和金融投资中扮演重要角色,近年来已成为人工智能和金融科技领域的热门研究主题.由于金融事件的实体之间存在复杂的投资、供应等关系,现有的金融风险预测研究常利用各种静态和动态的图结构来建模金融实体间的关系,并通过卷积图神经网络等方法将相关的图结构信息嵌入金融实体的特征表示中,使其能够同时表征金融风险相关的语义和结构信息.然而,以前的金融风险预测综述仅关注了基于静态图结构的研究,这些研究忽视了金融事件中实体间关系会随时间动态变化的特性,降低了风险预测结果的准确性.随着时序图神经网络的发展,越来越多的研究开始关注基于动态图结构的金融风险预测,对这些研究进行系统、全面的回顾有助于学习者构建面向金融风险预测研究的完整认知.根据从动态图中提取时序信息的不同途径,首先综述3类不同的时序图神经网络模型.然后,根据不同的图学习任务,分类介绍股价趋势风险预测,贷款违约风险预测,欺诈交易风险预测,以及洗钱和逃税风险预测共4个领域的金融风险预测研究.最后,总结现有时序图神经网络模型在金融风险预测方面遇到的难题和挑战,并展望未来研究的潜在方向.

SL股份财务造假动因及手段分析

在中国资本市场尚未完全成熟的大背景下,上市公司发展过程中财务造假、资本“暴雷”等现象频发不绝。舞动因纷繁复杂,造假手段愈加高明,严重阻碍了中国资本市场的良性发展。本文以SL股份财务造假为案例,利用GONE理论对造假动因进行分析,并对造假手段进行挖掘。研究发现,SL股份财务造假的动因主要是:大股东贪婪因子,股权失衡、内控失效等机会因子,隐瞒退市压力、融资需要以及提升市场竞争力等需要因子,事务所审计失责与市场处罚小等暴露因子综合组成。并通过操纵收入、费用,隐瞒重要披露事项等手段虚增利润。最后从上市公司自身、审计监督以及市场监管等方面提出防范财务造假的建议,以便于更好地维护资本市场的运转。

互联网金融诈骗犯罪协同治理对策研究

“互联网+”时代背景下,以金融交易、金融投资或借贷的名义实施的互联网金融诈骗犯罪频繁发生。针对互联网金融诈骗犯罪案件发案率高、涉及主体多、财产虚拟化、犯罪专业化、涉案部门联动不足及协同程度有限等特点,依据协同治理理论,提出以公安机关为主导,采取银行、金融、证券、电信等主体协同的犯罪防治策略,完善全流程协同防控体系,实现保护人民财产安全和维护国家金融稳定的目标。

基于CRIME理论的康美药业财务造假分析及防范研究

随着经济的快速发展,资本市场中财务造假事件频繁发生,损害了中小股东的利益,给债权人、广大投资者乃至社会带来了巨大的损失。利用传统财务指标对舞弊手段进行识别的作用有限,提出的解决措施治标不治本。因此,需要采用系统的理论对上市公司财务舞弊进行深入分析,才能提出针对性的治理对策。本文运用CRIME舞弊行为理论,从舞弊者、舞弊手段、舞弊动机、监管机制、舞弊后果五个角度,对康美药业舞弊现象进行剖析,针对产生的问题提出了相应的预防措施,以期增强投资者的风险辨识能力。