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.

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.

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.

Fraud Identification of Chinese Listed Companies——an Improvement Based on M-Score

To evaluate the applicability of the M-score model in the Chinese capital market,this research observed 190 financial fraud samples punished by the China Securities Regulatory Commission(CSRC)in the years from 2014 to 2018.The test results indicate that two types of errors are high,which means that the applicability of the M-score is unacceptable.Therefore,in this paper,a 9-index model is constructed by Wald's backward stepwise regression method,and the optimal threshold is set by the Beneish expected cost method(ECM).The accuracy of the modified M-score is significantly improved,especially the Type I error rate of is reduced from 70.37%to 19.75%.The receiver operating characteristic(ROC)curve test also proves the superior identification effect of the modified M-score applied in the Chinese market.Finally,variables such as current ratio,fixed asset index,and equity concentration in the modified model could represent the fraud characteristics of Chinese listed companies.

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.

Financial reporting fraud and CEO pay-performance incentives

Because prior studies find mixed results on the relation between CEOs’pay performance incentives and a firm’s likelihood of financial reporting fraud,we restudy their relationship using innovative research methods.First,we concentrate on incentives from granting options rather than equity-based incentives.Second,we emphasize vested options,disregarding unvested option holdings,and take the logarithm transformation of option incentives.Third,we analyse the impact of option incentives on future financial reporting irregularities.Using this innovative approach as well as a full sample and a matched sample,we find that an increase in executives’option incentives raises the likelihood of financial reporting violations.Moreover,the effect of option incentives on financial reporting fraud is moderated by auditor effort.In addition,we find that another proxy for the measurement of executives’option incentives,namely,the number of vested options by executives,is highly correlated with the CEO’s vested stock option sensitivity.

Visual analytics for event detection: Focusing on fraud

The detection of anomalous events in huge amounts of data is sought in many domains.For instance,in the context of financial data,the detection of suspicious events is a prerequisite to identify and prevent attempts to defraud.Hence,various financial fraud detection approaches have started to exploit Visual Analytics techniques.However,there is no study available giving a systematic outline of the different approaches in this field to understand common strategies but also differences.Thus,we present a survey of existing approaches of visual fraud detection in order to classify different tasks and solutions,to identify and to propose further research opportunities.In this work,fraud detection solutions are explored through five main domains:banks,the stock market,telecommunication companies,insurance companies,and internal frauds.The selected domains explored in this survey were chosen for sharing similar time-oriented and multivariate data characteristics.In this survey,we(1)analyze the current state of the art in this field;(2)define a categorization scheme covering different application domains,visualization methods,interaction techniques,and analytical methods which are used in the context of fraud detection;(3)describe and discuss each approach according to the proposed scheme;and(4)identify challenges and future research topics.