FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining

Seeja, K. R.; Zareapoor, Masoumeh

doi:10.1155/2014/252797

Cited by 118 publications

(42 citation statements)

References 27 publications

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“…AIS are a recent branch of artificial intelligence based on the biological metaphor of the human immune system. Seeja and Zareapoor () proposed credit card fraud detection model from imbalanced transaction datasets, which are based on frequent items. Zareapoor and Shamsolmoali () used an ensemble classifier which handles both classification and regression methods.…”

Section: Data Mining Approaches To Financial Fraud Detectionmentioning

confidence: 99%

How to detect illegal corporate insider trading? A data mining approach for detecting suspicious insider transactions

Esen

Bilgiç

Basdas³

2019

Intell Sys Acc Fin Mgmt

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Summary Only in the U.S. Stock Exchanges, the daily average trading volume is about 7 billion shares. This vast amount of trading shows the necessity of understanding the hidden insights in the data sets. In this study, a data mining technique, clustering based outlier analysis is applied to detect suspicious insider transactions. 1,244,815 transactions of 61,780 insiders are analysed, which are acquired from Thomson Financial, covering a period of January 2010–April 2017. In order to detect outliers, similar transactions are grouped into the same clusters by using a two‐step clustering based outlier detection technique, which is an integration of k‐means and hierarchical clustering. Then, it is shown that outlying transactions earn higher abnormal returns than non‐outlying transactions by using event study methodology.

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Section: Data Mining Approaches To Financial Fraud Detectionmentioning

confidence: 99%

How to detect illegal corporate insider trading? A data mining approach for detecting suspicious insider transactions

Esen

Bilgiç

Basdas³

2019

Intell Sys Acc Fin Mgmt

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“…Adams and Moore [20] analyzed the risky behavior of college students, for they are of more convenience for researchers to acquire card usage sample data on; while some of the related research has focused on the usage trend of specific behavior, such as for the misuse of a credit card, by simply investigating statistics data. Manning [21] and Ladka [22] pointed out that the popularization of credit cards had increased the trend of personal bankruptcy, and by a statistical analysis, the American Bankruptcy Institute [23] showed the result that about 1 personal bankruptcy happens in every 175 adults, and by frequent itemset mining, Seeja and Zareapoor [24] developed a credit card fraud detection model to identify misbehaving tendencies. As for the number of cards owned and the total amount of items to be repaid, Wang and Xiao [25] showed the result of a mean of 2 cards with a range of 1 to 18 cards owned by each holder, and that the total amount of items to be repaid has risen from around $250 to almost $1500 in the past 30 years.…”

Section: On Trend Studies Of Credit Card Usagementioning

confidence: 99%

Modeling the Trend of Credit Card Usage Behavior for Different Age Groups Based on Singular Spectrum Analysis

Nai¹,

Liu

Wang³

et al. 2018

Algorithms

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Credit card holders from different age groups have different usage behaviors, so deeply investigating the credit card usage condition and properly modeling the usage trend of all customers in different age groups from time series data is meaningful for financial institutions as well as banks. Until now, related research in trend analysis of credit card usage has mostly been focused on specific group of people, such as the behavioral tendencies of the elderly or college students, or certain behaviors, such as the increasing number of cards owned and the rise in personal card debt or bankruptcy, in which the only analysis methods employed are simply enumerating or classifying raw data; thus, there is a lack of support in specific mathematical models based on usage behavioral time series data. Considering that few systematic modeling methods have been introduced, in this paper, a novel usage trend analysis method for credit card holders in different age groups based on singular spectrum analysis (SSA) has been proposed, using the time series data from the Survey of Consumer Payment Choice (SCPC). The decomposition and reconstruction process in the method is proposed. The results show that the credit card usage frequency falls down from the age of 26 to the lowest point at around the age of 58 and then begins to increase again. At last, future work is discussed.

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“…This data set was used before for detecting odd e-commerce transactions as presented in [19], [20]. It has 94682 examples of real ecommerce transactions, where 92,588 of them are legitimate transactions and the remaining 2094 transactions are fraudulent with a fraud ratio of 2.2%.…”

Section: B Ucsd Transactional Data Setmentioning

confidence: 99%

The Impact of False Negative Cost on the Performance of Cost Sensitive Learning Based on Bayes Minimum Risk: A Case Study in Detecting Fraudulent Transactions

Hassan¹

2017

IJISA

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Abstract-In this paper, we present a new investigation to the literature, where we study the impact of false negative (FN) cost on the performance of cost sensitive learning. The proposed investigation approach has been performed on cost sensitive classifiers developed using Bayes minimum risk as an example of an applied mechanism for making a classifier cost sensitive. We consider a case study in credit card fraud detection, where FN refers to the number of fraudulent transactions that are miss-detected and approved as legitimate ones, assuming the classifier predicts the fraudulent transaction. Our investigation approach relies on testing the performance of various complex cost sensitive classifiers from different categories developed using Bayes minimum risk at different costs of FN. Our results show that those classifiers behave differently at different costs of FN including the real and average amount of transaction, and a range of random constant costs that are greater or less than the average amount. However, in general the results show that the lower the costs of FN are, the better the classifier performances are. This leads to different conclusions from the one drawn in [1], which states that choosing the cost of FN to be equal to the amount of transaction leads to better performance of cost sensitive learning using Bayes minimum risk. The results of this paper are based on the real life anonymous and imbalanced UCSD transactional data set.

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FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining

Cited by 118 publications

References 27 publications

How to detect illegal corporate insider trading? A data mining approach for detecting suspicious insider transactions

How to detect illegal corporate insider trading? A data mining approach for detecting suspicious insider transactions

Modeling the Trend of Credit Card Usage Behavior for Different Age Groups Based on Singular Spectrum Analysis

The Impact of False Negative Cost on the Performance of Cost Sensitive Learning Based on Bayes Minimum Risk: A Case Study in Detecting Fraudulent Transactions

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