Detecting Financial Fraud Using Data Mining Techniques: A Decade Review from 2004 to 2015

Albashrawi, Mousa

doi:10.6339/jds.201607_14(3).0010

Cited by 76 publications

(20 citation statements)

References 61 publications

(4 reference statements)

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“…In the initial stages, researchers mainly included Neural Network (NN), Linear regression (LR), Decision Tree (DT), Support Vector machine (SVM), Discriminant Analysis (DA) and Bayesian Belief Network. Supervised learning techniques were selected for analysis more than unsupervised ones, with 65% of the articles from US, China, Taiwan and Spain [10]. A considerable number of studies that analysed the performance of classifiers on FSF detection have shown that SVM [11,12,13,14,15], NN [16,17,18,19], DT [20,21] perform well in FSF detection/prediction.…”

Section: Related Researchmentioning

confidence: 99%

An Ensemble Model for Financial Statement Fraud Detection

Khedr¹,

El‐Bannany²,

Kanakkayil³

2021

Preprint

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Fraudulent financial statements are deliberate furnishing and/or reporting incorrect statistics, and this has become a major economic and social concern as the global market is witnessing an upsurge in financial accounting fraud, costing businesses billions of dollars a year. Identifying companies that manipulate financial statements remains a challenge for auditors, as fraud strategies have become increasingly sophisticated over the years. We evaluate machine learning techniques for financial statement fraud detection, particularly a powerful ensemble technique, the XGBoost algorithm, that help to identify fraud on a set of sample companies drawn from the MENA region. The issue of the class imbalance in the dataset is addressed by applying the SMOTE algorithm. We found that XGBoost algorithm outperformed other algorithms in this study: Logistic Regression (LR), Decision Tree (DT), Vector Machine Support (SVM), Adaboost, and RandomForest. The XGBoost algorithm is then optimised to obtain the optimum performance.

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Section: Related Researchmentioning

confidence: 99%

An Ensemble Model for Financial Statement Fraud Detection

Khedr¹,

El‐Bannany²,

Kanakkayil³

2021

Preprint

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“…During network training, it may appear that the input feature vector is too far away from the neuron, causing the neuron to never win the competition [21][22][23].…”

Section: Lvq Neural Network Algorithm Process Lvq Neuralmentioning

confidence: 99%

“…During network training, it may appear that the input feature vector is too far away from the neuron, causing the neuron to never win the competition [ 21 – 23 ]. The fraud identification process in accounting based on LVQ neural network is shown in Figure 3 .…”

Section: Lvq Neural Networkmentioning

confidence: 99%

[Retracted] Using an Optimized Learning Vector Quantization‐ (LVQ‐) Based Neural Network in Accounting Fraud Recognition

Zheng

2021

Computational Intelligence and Neuroscience

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With the continuous development and wide application of artificial intelligence technology, artificial neural network technology has begun to be used in the field of fraud identification. Among them, learning vector quantization (LVQ) neural network is the most widely used in the field of fraud identification, and the fraud identification rate is relatively high. In this context, this paper explores this neural network technology in depth, uses the same fraud sample to test the fraud recognition rate of these two models, and proposes an optimized LVQ-based combined neural network fraud risk recognition model on this basis. This paper selects 550 listed companies that have committed fraud from 2015 to 2019 as the fraud samples, determines 550 nonfraud matching sample companies in accordance with the Beasley principle one-to-one, and uses this as the research sample. The fraud risk identification indicators with better identification effects combed out according to the literature were used as the initial indicator system. After the collinearity problem was eliminated through the paired sample T test and principal component analysis, the five indicators with the best identification effects were finally selected. Finally, based on the above theoretical analysis and empirical research summarizing the full text, it analyzes the shortcomings of this research and puts forward prospects for the future development of fraud risk identification models.

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“…e study applied a supervised learning strategy that used the classification technique. Supervised learning gives powerful capabilities for using machine language to classify and handle data [39].…”

Section: Design Of Studymentioning

confidence: 99%

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

Mqadi

Naicker

Adeliyi

2021

Mathematical Problems in Engineering

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In ordinary credit card datasets, there are far fewer fraudulent transactions than ordinary transactions. In dealing with the credit card imbalance problem, the ideal solution must have low bias and low variance. The paper aims to provide an in-depth experimental investigation of the effect of using a hybrid data-point approach to resolve the class misclassification problem in imbalanced credit card datasets. The goal of the research was to use a novel technique to manage unbalanced datasets to improve the effectiveness of machine learning algorithms in detecting fraud or anomalous patterns in huge volumes of financial transaction records where the class distribution was imbalanced. The paper proposed using random forest and a hybrid data-point approach combining feature selection with Near Miss-based undersampling technique. We assessed the proposed method on two imbalanced credit card datasets, namely, the European Credit Card dataset and the UCI Credit Card dataset. The experimental results were reported using performance matrices. We compared the classification results of logistic regression, support vector machine, decision tree, and random forest before and after using our approach. The findings showed that the proposed approach improved the predictive accuracy of the logistic regression, support vector machine, decision tree, and random forest algorithms in credit card datasets. Furthermore, we found that, out of the four algorithms, the random forest produced the best results.

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Detecting Financial Fraud Using Data Mining Techniques: A Decade Review from 2004 to 2015

Cited by 76 publications

References 61 publications

An Ensemble Model for Financial Statement Fraud Detection

An Ensemble Model for Financial Statement Fraud Detection

[Retracted] Using an Optimized Learning Vector Quantization‐ (LVQ‐) Based Neural Network in Accounting Fraud Recognition

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

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