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Data mining for credit card fraud: A comparative study
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“This methodology was first proposed by Whitrow et al. (2009) and has been used by a number of studies (Bhattacharyya et al., 2011; Jha et al., 2012; Dal Pozzolo et al., 2014; Bahnsen et al., 2016).”
From Data engineering for fraud detection (2021) · cites this paper as Bhattacharyya et al., 2011
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“Both supervised [8], [12], [15] and unsupervised [11], [14], [62] methods have been proposed for credit card frauddetection purposes.”
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Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017) · International Conference on Computing Networking and Informatics (ICCNI) · doi:10.1109/iccni.2017.8123782 · Cited by 644
“This paper [13] evaluates two advanced data mining approaches, support vector machines and random forests, together with logistic regression, as part of an attempt to better detect credit card fraud while neural network and logistic regression is applied on credit card fraud detection problem [14].”
MethodFinancial Fraud: A Review of Anomaly Detection Techniques and Recent Advances
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“Fraudsters are continually refining their methods, and as such there is a requirement for detection methods to be able to evolve accordingly [7].”
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“At the same time, financial data sets have been used extensively for credit scoring (26), fraud detection (27), and understanding the predictability of shopping patterns (28).”
BackgroundSequence classification for credit-card fraud detection
Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.-É., He-Guelton, L., & Caelen, O. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.01.037 · Cited by 628
“[Bhattacharyya et al., 2011] compared the predictive accuracy of Logistic Regression, Random Forests and Support Vector Machines on a real-world credit-card fraud data set under varying proportions of fraud in the training set, finding that Random Forests demonstrated an overall higher precision at low recall levels.”
A Systematic Review on Imbalanced Data Challenges in Machine Learning
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“Credit card fraud mining has been studied in Bhattacharyya et al. (2011) using SVM, RF, and logistic regression.”
MethodCredit Card Fraud Detection Using AdaBoost and Majority Voting
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“Logistic Regression (LOR) can handle data with both nominal and numerical features.”
InfluentialCombining unsupervised and supervised learning in credit card fraud detection
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A Semi-Supervised Graph Attentive Network for Financial Fraud Detection
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“Commonly, these methods first extract statistical features from different aspects like user’s profiles and historical behaviours and then use some classical classifiers like SVM [9], treebased methods [10] or neural networks [7], [8] to decide fraud or not.”
BackgroundMethodA Semi-supervised Graph Attentive Network for Financial Fraud Detection
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“Commonly, these methods first extract statistical features from different aspects like user’s profiles and historical behaviours and then use some classical classifiers like SVM [9], treebased methods [10] or neural networks [7], [8] to decide fraud or not.”
BackgroundMethodFeature engineering strategies for credit card fraud detection
Correa Bahnsen, A., Aouada, D., Stojanovic, A., & Ottersten, B. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.12.030 · Cited by 451
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“Both supervised learning [8], and unsupervised learning [12] algorithms have been utilized for credit card fraud detection.”
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“In order to cover the relevant costs of theft or accidental damages to a car, an agreement between the insurance provider and the insured person or organization is typically involved in automobile insurance claims [42,44].”
BackgroundRandom forest for credit card fraud detection
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“However, single-tree model is possibly sensitive to specific training data and easy to overfit [11].”
BackgroundFinancial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019
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“While e-commerce is still gaining popularity, it is also the playground of fraudsters who try to misuse the transparency of online purchases and the transfer of credit card records.”
BackgroundAn Experimental Study With Imbalanced Classification Approaches for Credit Card Fraud Detection
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“Bhattacharyya et al. [2] conducted a comparative study between logistic regression, support vector machine and random forest, they showed that random forest outperformed the two other methods.”
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Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud
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Credit Card Fraud Detection Using Convolutional Neural Networks
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Deep learning detecting fraud in credit card transactions
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“In particular, the classification relies on Random Forests [42, 43], which have been shown to be particularly effective in fraud detection problems [44, 45, 46].”
MethodData Mining for Detecting Bitcoin Ponzi Schemes
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“Specially, as dealing with rich features problem like AML, the simplicity of classifier can well generalise [50].”
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Phishing Detection: Analysis of Visual Similarity Based Approaches
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Big Data and Service Operations
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Ensemble of deep sequential models for credit card fraud detection
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Example-dependent cost-sensitive decision trees
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“, 2002), hybrid models (Krivko, 2010), support vector machines (Bhattacharyya et al., 2011) and random forest (Correa Bahnsen et al.”
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“This information comes from tons of sources like social media followers, likes and comments, user’s purchase behaviours[1],[2].”
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Intelligent financial fraud detection practices in post-pandemic era
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“Banks usually predict fraud with the help of transaction information, such as transactional history and payment observation.(42,43) More comprehensively, regulators collect incidents in the entire industry and issue relevant reports.”
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A deep learning approach for credit scoring using credit default swaps
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“Bhattacharyya et al. (2011) evaluate the performance of two advanced data mining techniques, random forests, support vector machines and logistic regression, for credit card fraud detection.”
MethodZero-day malware detection based on supervised learning algorithms of API call signatures
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Enhanced credit card fraud detection based on attention mechanism and LSTM deep model
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“Some of these algorithms are support vector machines [10, 11], hidden Markov models [11, 12], logistic regression algorithms [10, 13], decision trees [14, 15], random forests [10, 16–19], and k-nearest neighbors [20, 21].”
MethodThe analytics paradigm in business research
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Cost Sensitive Credit Card Fraud Detection Using Bayes Minimum Risk
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“The motivation behind this approach is the need to train the system in the same way it will be implemented, in which past months are used to predict the current month [6], [13].”
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Performance of machine learning techniques in the detection of financial frauds
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Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison
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“These cards may be used in both online as well as offline modes to make payments [7].”
BackgroundData engineering for fraud detection
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“(2009) and has been used by a number of studies (Bhattacharyya et al., 2011; Jha et al., 2012; Dal Pozzolo et al., 2014; Bahnsen et al., 2016).”
MethodUnsupervised learning for robust Bitcoin fraud detection
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Comprehensive review of text-mining applications in finance
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“Bhattacharyya et al. (2011) explored the use of text mining in credit card fraud detection by evaluating two predictive models: one based on SVM, and the other based on a combination of random forest with logistic regression.”
MethodSeoul's Wi-Fi hotspots: Wi-Fi access points as an indicator of urban vitality
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An intelligent payment card fraud detection system
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Ensemble classification based on supervised clustering for credit scoring
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“AI techniques include k-nearest neighbors (KNN) [7], mathematical programming [8-9], artificial neural network (ANN) [10], artificial immune systems (AIS) [11-12], genetic algorithm (GA) [13], support vector machines (SVMs) [14-17], and their variations [18].”
BackgroundFraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining
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“There are many review papers describing the different types of frauds and different fraud detection techniques [6– 11].”
BackgroundInsurance fraud detection: Evidence from artificial intelligence and machine learning
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A Comparative Performance Assessment of Ensemble Learning for Credit Scoring
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“. may be time-consuming. potential work.”
Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning
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On the suitability of resampling techniques for the class imbalance problem in credit scoring
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“…domain, research has mainly focused on analysing the behaviour of prediction models, showing that the performance on the minority class drops down significantly as the imbalance ratio increases (Hand and Vinciotti, 2003; Kennedy et al , 2010; Bhattacharyya et al , 2011; Brown and Mues, 2012).”
BackgroundCoDetect: Financial Fraud Detection With Anomaly Feature Detection
Huang, D., Mu, D., Yang, L., & Cai, X. (2018) · IEEE Access · doi:10.1109/access.2018.2816564 · Cited by 149
“The detection models are built on primary features and derived features from transaction [30].”
MethodTowards automated feature engineering for credit card fraud detection using multi-perspective HMMs
Lucas, Y., Portier, P.-É., Laporte, L., He-Guelton, L., Caelen, O., Granitzer, M., & Calabretto, S. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.08.029 · Cited by 144
“(Bhattacharyya et al., 2011) compared Support Vector Machine, Random Forest and logistic regression on a real world dataset using a wide variety of metrics.”
MethodA Model Based on Convolutional Neural Network for Online Transaction Fraud Detection
Zhang, Z., Zhou, X., Zhang, X., Wang, L., & Wang, P. (2018) · Security and Communication Networks · doi:10.1155/2018/5680264 · Cited by 143
“In addition to neural network algorithms, logistic regression, support vector machines, random forest algorithms [3, 4], hidden Markov models [17, 18], and adversarial learning methods [19] are also widely applied in constructing credit fraud detection model.”
MethodCost-sensitive payment card fraud detection based on dynamic random forest and k -nearest neighbors
Nami, S., & Shajari, M. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.06.011 · Cited by 140
Modelling and interpreting pre-evacuation decision-making using machine learning
Zhao, X., Lovreglio, R., & Nilsson, D. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103140 · Cited by 138
“To name a few instances, the RF has been successfully applied to predict construction injury [35], model travel mode choice [13, 26], forecast Alzheimer's disease [36], detect credit card fraud [37], and classify earthquake building damage [38].”
MethodGray, G. L., & Debreceny, R. S. (2014) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2014.05.006 · Cited by 137
Credit card fraud detection and concept-drift adaptation with delayed supervised information
Pozzolo, A. D., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280527 · Cited by 135
“This distinction is crucial for the design of an accurate FDS, though most FDSs in the literature [25], [36], [16], [4] assume an immediate and accurate labeling after the processing of each transaction.”
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“[4] analyzed various aspects of user attributes elaborately such as categorical attributes, transac-”
BackgroundMethodDigitalisation and Big Data Mining in Banking
Hassani, H., Huang, X., & Silva, E. S. (2018) · Big Data and Cognitive Computing · doi:10.3390/bdcc2030018 · Cited by 132
“This is due to the emerging need for combating cyber crime and developing more advanced technologies [102] (A review paper specifically targeting DM applications in fraud detection can be found in [103–105]).”
BackgroundSpatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection
Cheng, D., Xiang, S., Shang, C., Zhang, Y., Yang, F., & Zhang, L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5371 · Cited by 132
“Credit card fraud is a general term for the unauthorized use of funds in a transaction typically through a credit or a debit card (Bhattacharyya et al. 2011).”
BackgroundFederated learning model for credit card fraud detection with data balancing techniques
Salam, M. A., Fouad, K. M., Bably, D. L. E., & Elsayed, S. M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-023-09410-2 · Cited by 166
Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation
Xiang, S., Zhu, M., Cheng, D., Li, E.-X., Zhao, R., Ouyang, Y., Chen, L., & Zheng, Y. (2023) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26702 · Cited by 161
Fraud detection using self-organizing map visualizing the user profiles
Olszewski, D. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.07.008 · Cited by 128
“The rest of this paper is organized as follows: in Section 2, the related work is discussed as the background for the present paper’s proposal; in Section 3, the SOM algorithm is described; in Section 4, the representation of the user data is described and explained; in Section 5, the main proposal…”
MethodUsing machine learning for direct demand modeling of ridesourcing services in Chicago
Yan, X. “., Liu, X., & Zhao, X. (2020) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2020.102661 · Cited by 128
Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture
Malik, E. F., Khaw, K. W., Belaton, B., Wong, W. P., & Chew, X. (2022) · Mathematics · doi:10.3390/math10091480 · Cited by 155
“At each phase of validation a subset of 25 percent was set aside as the validation dataset to assess the output”
MethodAuto loan fraud detection using dominance-based rough set approach versus machine learning methods
Błaszczyński, J., Filho, A. T. D. A., Matuszyk, A., Szeląg, M., & Słowiński, R. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113740 · Cited by 126
Detecting Financial Fraud Using Data Mining Techniques: A Decade Review from 2004 to 2015
Mousa, A. (2021) · Journal of Data Science · doi:10.6339/jds.201607_14(3).0010 · Cited by 123
“4 Support vector machine 12 SVM is a statistical method that used for linear classification [4].”
BackgroundMethodInfluentialPredicting overall survivability in comorbidity of cancers: A data mining approach
Zolbanin, H. M., Delen, D., & Zadeh, A. H. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.04.003 · Cited by 121
Carcillo, F., Borgne, Y. L., Caelen, O., & Bontempi, G. (2018) · International Journal of Data Science and Analytics · doi:10.1007/s41060-018-0116-z · Cited by 128
“The high cost of human labor, for assessing the transaction labels, leads to the labeling bottleneck [2].”
BackgroundA Survey on Credit Card Fraud Detection Using Machine Learning
Popat, R. R., & Chaudhary, J. (2018) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei.2018.8553963 · Cited by 120
A review of data mining applications in crime
Hassani, H., Huang, X., Silva, E. S., & Ghodsi, M. (2016) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11312 · Cited by 118
“Moreover, an SVM model is used alongside logistic regression and RF for detecting credit card fraud in [72], where the performances are evaluated on real world transaction data from international financial institutions.”
MethodGenerative Adversarial Networks and Markov Random Fields for oversampling very small training sets
Salazar, A., Vergara, L., & Safont, G. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113819 · Cited by 114
A framework for understanding artificial intelligence research: insights from practice
Bawack, R. E., Wamba, S. F., & Carillo, K. (2021) · Journal of Enterprise Information Management · doi:10.1108/jeim-07-2020-0284 · Cited by 134
Xie, Y., Liu, G., Yan, C., Jiang, C., & Zhou, M. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3158318 · Cited by 120
Intention-aware Heterogeneous Graph Attention Networks for Fraud Transactions Detection
Liu, C., Sun, L., Ao, X., Feng, J., He, Q., & Yang, H. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467142 · Cited by 109
“Traditional fraud transactions detection methods usually treat each transaction as an independent entity, and train supervised models by feeding statistical features based on transaction-related data [4, 30].”
BackgroundData Mining-Based Ethereum Fraud Detection
Jung, E., Tilly, M. L., Gehani, A., & Ge, Y. (2019) · IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain.2019.00042 · Cited by 106
“It has proven to be an excellent classifier for other fraud detection [19]; • Stochastic Gradient Descent (SGD): iterative method for optimizing a differentiable objective function.”
MethodLearning Transactional Behavioral Representations for Credit Card Fraud Detection
Xie, Y., Liu, G., Yan, C., Jiang, C., Zhou, M., & Li, M. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3208967 · Cited by 116
“Several studies [11], [39], [40], [41], [42] followed the method in [3].”
BackgroundMethodUsing Variational Auto Encoding in Credit Card Fraud Detection
Huang, T., Guangquan, C., & Kuihua, H. (2020) · IEEE Access · doi:10.1109/access.2020.3015600 · Cited by 117
“In academia, machine learning has become the main method to study credit card fraud, because it can benefit from these massive datasets of complete transaction information [23]–[25].”
MethodLarge Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble
Wang, H., Xu, Q., & Zhou, L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0117844 · Cited by 103
Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications
Stojanović, B., Božić, J., Hofer-Schmitz, K., Nahrgang, K., Weber, A., Badii, A., Sundaram, M., Jordan, E., & Runevic, J. (2021) · Sensors · doi:10.3390/s21051594 · Cited by 130
“[44] proposed a comparison of results of applying SVM, random forest, and logistic regressions to a credit card fraud detection.”
Result
Metadata
- Authors
- Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C.
- Affiliations
- University of Illinois Chicago (US)
- University of New Hampshire (US)
- Millikin University (US)
- Corresponding author
- Bhattacharyya, Siddhartha
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2011-02
- Volume
- 50
- Issue
- 3
- Pages
- 602-613
- ISSN
- 0167-9236 (ISSN-L)
- OpenAlex
- W2045049630
- Cited by
- 1,108
- References
- 52
- Citations by year
- 2026: 153 · 2025: 142 · 2024: 103 · 2023: 86 · 2022: 98 · 2021: 89 · 2020: 95 · 2019: 83 · 2018: 77 · 2017: 42 · 2016: 34 · 2015: 40 · 2014: 29 · 2013: 17 · 2012: 13
- FWCI
- 11.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Decision Support Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Keywords
- credit card fraud detection, support vector machine, random forest, logistic regression, transaction data
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