Detecting credit card fraud by Modified Fisher Discriminant Analysis

Mahmoudi, Nader; Duman, Ekrem

doi:10.1016/j.eswa.2014.10.037

Cited by 143 publications

(79 citation statements)

References 19 publications

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“…(Pozzolo et al, 2014) tested an architecture to take into account temporal concept drift in the credit card transaction data stream with Random Forest, Support Vector Machine and Neural Network. (Mahmoudi and Duman, 2015) applied a modified Fisher discriminant func-tion to take into account the higher false negative cost in credit card fraud detection. More recently, (Jurgovsky et al, 2018) used LSTM for sequence classification on the same real world dataset that we use in this article.…”

Section: Credit Card Fraud Detectionmentioning

confidence: 99%

Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

Lucas

Portier

Laporte

et al. 2020

Future Generation Computer Systems

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Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions.In this framework, we model a sequence of credit card transactions from three different perspectives, namely (i) The sequence contains or doesn't contain a fraud (ii) The sequence is obtained by fixing the cardholder or the payment terminal (iii) It is a sequence of spent amount or of elapsed time between the current and previous transactions. Combinations of the three binary perspectives give eight sets of sequences from the (training) set of transactions. Each one of these sequences is modelled with a Hidden Markov Model (HMM). Each HMM associates a likelihood to a transaction given its sequence of previous transactions. These likelihoods are used as additional features in a Random Forest classifier for fraud detection.Our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to improve the effectiveness of the classification task and allows for an increase in the detection of fraudulent transactions when combined with the state of the art expert based feature engineering strategy for credit card fraud detection.In extension to previous works, we show that this approach goes beyond ecommerce transactions and provides a robust feature engineering over different datasets, hyperparameters and classifiers. Moreover, we compare strategies to deal with structural missing values.

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Section: Credit Card Fraud Detectionmentioning

confidence: 99%

Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

Lucas

Portier

Laporte

et al. 2020

Future Generation Computer Systems

View full text Add to dashboard Cite

show abstract

“…Topological pattern in [38] discovered the 'topological patterns' of 'fraudulent financial reporting' FFR via dual 'GHSOM' ('Growing Hierarchical Self-Organizing Map') approach, as well as presenting an expert competitive feature extraction mechanism, which has been accurate in detecting the fraudulent and genuine by using the topological patterns for FFR and feature extraction. On the other hand, the authors in [39] proposed a linear discriminate as the fisher discriminant function to detect credit card fraud for the first time, their experiment which has been produced from the fisher discriminant function was more efficient for the fraudulent / genuine detection classifier. The study in [40] proposed a combination of the derived intrinsic features and network-based features for cardholders' behavior merchants, their results for the combination of the two types which are strongly tangled, and leads to the best performance models where the 'AUC' reaches higher than 0,98.…”

Section: Credit Card Fraud Detectionmentioning

confidence: 99%

Comparative Analysis of Different Distributions Dataset by Using Data Mining Techniques on Credit Card Fraud Detection

2020

Teh. vjesn

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Banks suffer multimillion-dollars losses each year for several reasons, the most important of which is due to credit card fraud. The issue is how to cope with the challenges we face with this kind of fraud. Skewed "class imbalance" is a very important challenge that faces this kind of fraud. Therefore, in this study, we explore four data mining techniques, namely naïve Bayesian (NB),Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF), on actual credit card transactions from European cardholders. This paper offers four major contributions. First, we used under-sampling to balance the dataset because of the high imbalance class, implying skewed distribution. Second, we applied NB, SVM, KNN, and RF to under-sampled class to classify the transactions into fraudulent and genuine followed by testing the performance measures using a confusion matrix and comparing them. Third, we adopted cross-validation (CV) with 10 folds to test the accuracy of the four models with a standard deviation followed by comparing the results for all our models. Next, we examined these models against the entire dataset (skewed) using the confusion matrix and AUC (Area Under the ROC Curve) ranking measure to conclude the final results to determine which would be the best model for us to use with a particular type of fraud. The results showing the best accuracy for the NB, SVM, KNN and RF classifiers are 97,80%; 97,46%; 98,16% and 98,23%, respectively. The comparative results have been done by using four-division datasets (75:25), (90:10), (66:34) and (80:20) displayed that the RF performs better than NB, SVM, and KNN, and the results when utilizing our proposed models on the entire dataset (skewed), achieved preferable outcomes to the under-sampled dataset.

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“…They applied bagging classifier based on decision tree and indicated that the model detects the fraudulent transactions with a high detection rate. Mahmoudi and Duman () used a linear discriminant, Fisher Discriminant Function, to detect credit card frauds. Lahmiri () used several DM techniques for financial risk prediction.…”

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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Detecting credit card fraud by Modified Fisher Discriminant Analysis

Cited by 143 publications

References 19 publications

Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

Comparative Analysis of Different Distributions Dataset by Using Data Mining Techniques on Credit Card Fraud Detection

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

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