Detection of financial statement fraud and feature selection using data mining techniques

Ravisankar, P.; Ravi, Vadlamani; Rao, Ganesh; Bose, Indranil

doi:10.1016/j.dss.2010.11.006

Cited by 432 publications

(268 citation statements)

References 23 publications

Supporting

Mentioning

220

Contrasting

Unclassified

Order By: Relevance

“…The results show that Bayesian network (BBN) has the highest accuracy. Ravisankar et al (2011) used multi-layer feedback neural networks, support vector machines, probabilistic neural networks and other four methods to build models. The results showed that probabilistic neural network (PNN) performance was outstanding.…”

Section: Models In Random Forestmentioning

confidence: 99%

Financial Fraud Detection Model: Based on Random Forest

Li¹,

Chan²,

Kazmi³

et al. 2015

IJEF

View full text Add to dashboard Cite

Business's accelerated globalization has weakened regulatory capacity of the law and scholars have been paid attention to fraud detection in recent years. In this study, we introduced Random Forest (RF) for financial fraud technique detection and detailed features selection, variables' importance measurement, partial correlation analysis and Multidimensional analysis. The results show that a combination of eight variables has the highest accuracy. The ratio of debt to equity (DEQUTY) is the most important variable in the model. Moreover, we applied four statistic methodologies, including parametric and non-parametric models to construct detection models and concluded that Random Forest has the highest accuracy and the non-parametric models have higher accuracy than non-parametric models. However, Random Forest can improve the detection efficiency significantly and have an important practical implication.

show abstract

Section: Models In Random Forestmentioning

confidence: 99%

Financial Fraud Detection Model: Based on Random Forest

Li¹,

Chan²,

Kazmi³

et al. 2015

IJEF

View full text Add to dashboard Cite

show abstract

“…Financial statements are the basic documents that reflect the financial status of a company [1][2][3][4][5][6]. Financial statements are also the main basis of decision-making for the investing public, creditors, stakeholders, and other users of accounting information.…”

Section: Introductionmentioning

confidence: 99%

An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan

Jan

2018

Sustainability

View full text Add to dashboard Cite

This study aims to establish a rigorous and effective model to detect enterprises' financial statements fraud for the sustainable development of enterprises and financial markets. The research period is 2004-2014 and the sample is companies listed on either the Taiwan Stock Exchange or the Taipei Exchange, with a total of 160 companies (including 40 companies reporting financial statements fraud). This study adopts multiple data mining techniques. In the first stage, an artificial neural network (ANN) and a support vector machine (SVM) are deployed to screen out important variables. In the second stage, four types of decision trees (classification and regression tree (CART), chi-square automatic interaction detector (CHAID), C5.0, and quick unbiased efficient statistical tree (QUEST)) are constructed for classification. Both financial and non-financial variables are selected, in order to build a highly accurate model to detect fraudulent financial reporting. The empirical findings show that the variables screened with ANN and processed by CART (the ANN + CART model) yields the best classification results, with an accuracy of 90.83% in the detection of financial statements fraud.Keywords: financial statements fraud; data mining; artificial neural network (ANN); support vector machine (SVM); decision tree; classification and regression tree (CART); chi-square automatic interaction detector (CHAID); C5.0; quick unbiased efficient statistical tree (QUEST)

show abstract

“…They explore a self -adaptive framework based on a response surface model with domain knowledge to detect financial statement fraud. Ravisankar et al [23] uses data mining techniques such as Multilayer Feed Forward Neural Network (MLFF), Support Vector Machines (SVM), Genetic Programming (GP), Group Method of Data Handling (GMDH), Logistic Regression (LR), and Probabilistic Neural Network (PNN) to identify companies that resort to financial statement fraud. They found that PNN outperformed all the techniques without feature selection, and GP and PNN outperformed others with feature selection and with marginally equal accuracies.…”

Section: Related Workmentioning

confidence: 99%

A Data Mining Framework for Prevention and Detection of Financial Statement Fraud

Gupta¹,

Gill²

2012

IJCA

View full text Add to dashboard Cite

Financial statement fraud has reached the epidemic proportion globally. Recently, financial statement fraud has dominated the corporate news causing debacle at number of companies worldwide. In the wake of failure of many organisations, there is a dire need of prevention and detection of financial statement fraud. Prevention of financial statement fraud is a measure to stop its occurrence initially whereas detection means the identification of such fraud as soon as possible. Fraud detection is required only if prevention has failed. Therefore, a continuous fraud detection mechanism should be in place because management may be unaware about the failure of prevention mechanism. In this paper we propose a data mining framework for prevention and detection of financial statement fraud.

show abstract

Detection of financial statement fraud and feature selection using data mining techniques

Cited by 432 publications

References 23 publications

Financial Fraud Detection Model: Based on Random Forest

Financial Fraud Detection Model: Based on Random Forest

An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan

A Data Mining Framework for Prevention and Detection of Financial Statement Fraud

Contact Info

Product

Resources

About