Applying Cost-Sensitive Classification for Financial Fraud Detection under High Class-Imbalance

Moepya, Stephen O.; Akhoury, Sharat Saurabh; Nelwamondo, Fulufhelo V.

doi:10.1109/icdmw.2014.141

Cited by 32 publications

(23 citation statements)

References 17 publications

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“…The general approaches to dealing with the data imbalance problem include minority class over-sampling or under-sampling the majority class (Chawla et al, 2002). Another approach to deal with the data imbalance problem is to use cost sensitive learning, where different weights are placed on false negatives (classifying a firm as non-fraudulent when it fraudulent) compared false positives (classifying a company as fraudulent when it is not fraudulent) when training the FSF models (Moepya, Akhoury, & Nelwamondo, 2014).…”

Section: Data Issuesmentioning

confidence: 99%

“…In addition, since the sample is balanced, one can achieve an accuracy of 50% by making random predictions (Alden et al, 2012). A more direct approach to deal with the data imbalance problem using cost sensitive learning is explored in Moepya, Akhoury, and Nelwamondo (2014). In this paper the authors use different weights for false positive and false negatives, and show that this cost sensitive approach results in an increase in the detection of the minority class, albeit at a cost of lowering the overall classification accuracy.…”

Section: Data Issuesmentioning

confidence: 99%

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A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context

Mongwe

Malan

2020

SACJ

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Financial statement fraud has been on the increase in the past two decades and includes prominent scandals such as Enron, WorldCom and more recently in South Africa, Steinhoff. These scandals have led to billions of dollars being lost in the form of market capitalisation from different stock exchanges across the world. During this time, there has been an increase in the literature on applying automated methods to detecting financial statement fraud using publicly available data. This paper provides a survey of the literature on automated financial statement fraud detection and identifies current gaps in the literature. The paper highlights a number of important considerations in the implementation of financial statement fraud detection decision support systems, including 1) the definition of fraud, 2) features used for detecting fraud, 3) region of the case study, dataset size and imbalance, 4) algorithms used for detection, 5) approach to feature selection / feature engineering, 6) treatment of missing data, and 7) performance measure used. The current study discusses how these and other implementation factors could be approached within the South African context.

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Section: Data Issuesmentioning

confidence: 99%

Section: Data Issuesmentioning

confidence: 99%

A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context

Mongwe

Malan

2020

SACJ

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“…[38] established a Bayesian network model to classify bad debts in hospitals. [39] compared the performance of Weighted Support Vector Machines (SVM), Naive Bayes (NB) and K-Nearest Neighbors classifiers for financial statement fraud, the weighted SVM method is more effective.…”

Section: Literature Review In the Field Of Financial Managementmentioning

confidence: 99%

Application of Data Mining in the Field of Human Resource Management: A Review

Ma¹,

Zhang²,

Song³

et al. 2019

Proceedings of the 1st International Symposium on Economic Development and Management Innovation (EDMI 2019)

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Data mining methods play an important role in customer management, manufacturing management, and financial management. This paper focuses on the latest research progress from 2012 to the present. The analysis of the progress of existing research will help researchers to better engage in research work in the future. The literature review is classified according to the research field and the data mining methods involved. In each of the research areas, we conduct research classification based on the data mining tools used. Through literature analysis, it can be seen that the degree of attention of neural networks has increased continuously in recent years.

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“…The quality of solution heavily depends on underlying distribution of data points in problem space [1]. However, many real-world problems including oil spilling [2], network intrusion detection [3], weld flaw [4], financial fraud detection [5], churn prediction [6]and bankruptcy prediction [7] Are suffered from skewed distribution of class instances. Such problems are attributed to class imbalance problems where most of the example belongs to one class, and few of them belong to another class [8].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Data Re-Sampling on Learning Performance of Class Imbalanced Bankruptcy Prediction Models

Sisodia¹,

Verma²

2018

IJEEI

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The aim of this paper is to evaluate the effect of data sampling techniques on the performance of learners using real highly imbalanced Spanish bankruptcy dataset. The class imbalance problem refers to the highly uneven distribution of class instances where one class is having most of the instances than others. In the presence of highly skewed data distribution, the performance of classical learners is heavily biased in recognizing the majority class and consequently leads to the performance degradation of quantitative classifier or predictors models. In this paper, six sampling methods such as synthetic minority oversampling technique (SMOTE), Borderline-SMOTE, Safe-level-SMOTE, Random under sampling, random oversampling and condensed nearest neighbor are used with a different individual(SVM, C4.5, and Logistic regression) and ensemble learners(AdaBoostM1, DTBagging, and Random Forests). The different quantitative prediction models are designed by combination data sampling techniques and classical learners. The performance of quantitative prediction models are evaluated using G-Mean and area under the curve (AUC) measures on the real highly imbalanced data set. The result suggest that the performance of oversampling (with LR and DTBagging) and undersampling (with C4.5 and RF) methods are superior as compare to others on this data set.

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Applying Cost-Sensitive Classification for Financial Fraud Detection under High Class-Imbalance

Cited by 32 publications

References 17 publications

A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context

A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context

Application of Data Mining in the Field of Human Resource Management: A Review

The Impact of Data Re-Sampling on Learning Performance of Class Imbalanced Bankruptcy Prediction Models

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