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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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Kumar, S., Ahmed, R., Bharany, S., Shuaib, M., Ahmad, T., Eldin, S. M., Rehman, A. U., & Shafiq, M. (2022) · Sustainability · doi:10.3390/su142113875 · Cited by 40
“[59], and its usefulness was demonstrated.”
A distributed deep neural network model for credit card fraud detection
Lei, Y.-T., Ma, C., Ren, Y., Chen, X.-Q., Narayan, S., & Huynh, A. N. Q. (2023) · Finance research letters · doi:10.1016/j.frl.2023.104547 · Cited by 40
Fraud detection within bankcard enrollment on mobile device based payment using machine learning
Zhou, H., Chai, H., & Qiu, M.-L. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1800580 · Cited by 40
A combined entropy-based approach for a proactive credit scoring
Carta, S., Ferreira, A., Recupero, D. R., Saia, M., & Saia, R. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.103292 · Cited by 40
“We report comparisons between our approach and Random Forests, which are considered state-of-the-art approaches for credit scoring tasks [10, 11, 12].”
MethodCost-sensitive learning for imbalanced data streams
Loezer, L., Enembreck, F., Barddal, J. P., & Britto, A. D. S. (2020) · ACM Symposium on Applied Computing · doi:10.1145/3341105.3373949 · Cited by 40
“This phenomenon can be categorized as intrinsic if the class imbalance is natural such as in the following applications: fraud detection [3], oil spill detection [38], abnormal activity classification [22] and breast cancer malignancy classification [28].”
BackgroundCredit Card Fraud Detection using Machine Learning: A Systematic Literature Review
Paruchuri, H. (2017) · ABC Journal of Advanced Research · doi:10.18034/abcjar.v6i2.547 · Cited by 40
Padhi, B. K., Chakravarty, S., Naik, B., Pattanayak, R. M., & Das, H. (2022) · Sensors · doi:10.3390/s22239321 · Cited by 39
Zeng, Q., Li, L., Jiang, R., Huang, W., & Lin, D. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103916 · Cited by 50
Analysis of Credit Card Fraud Detection Using Fusion Classifiers
Kumari, P., & Mishra, S. P. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-8055-5_11 · Cited by 40
A Comparison of Prediction Methods for Credit Default on Peer to Peer Lending using Machine Learning
Setiawan, N., Suharjito, S., & Diana, D. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.08.139 · Cited by 39
Credit Card Fraud Detection using Machine Learning: A StudyPreprint
Tiwari, P., Mehta, S., Sakhuja, N., Kumar, J., & Singh, A. K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.10005 · Cited by 109
“…with it obtaining variance amongst its trees and hence are easy to use because of use of only two randomness sources or parameters that is building trees using trained data separate bootstrapped along samples with considering only a random data attribute subset to build each tree as specified [5].”
BackgroundMethodInfluentialSurrogate techniques for testing fraud detection algorithms in credit card operations
Salazar, A., Safont, G., & Vergara, L. (2014) · IEEE International Carnahan Conference on Security Technology (ICCST) · doi:10.1109/ccst.2014.6986987 · Cited by 38
“, the records include confidential customer data and banks are afraid of public quantitative evidence of existing fraud operations [1].”
BackgroundSarno, R., & Sinaga, F. (2015) · International Conference on Computer, Control, Informatics and its Applications · doi:10.1109/ic3ina.2015.7377738 · Cited by 38
“Data mining techniques such as Decision Tree, Neural and Bayesian Network and Support Vector Machine have been used in previous studies to analyze data contain within the process [7], [8], and [9].”
MethodElucidation of big data analytics in banking: a four-stage Delphi study
Delgosha, M. S., Hajiheydari, N., & Fahimi, S. M. (2020) · Journal of Enterprise Information Management · doi:10.1108/jeim-03-2019-0097 · Cited by 42
Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud Detection
Wu, T.-Y., & Wang, Y.-T. (2021) · Conference on Technologies and Applications of Artificial Intelligence (TAAI) · doi:10.1109/taai54685.2021.00014 · Cited by 43
Improving Credit Card Fraud Detection using a Meta-Classification Strategy
Pun, J., & Lawryshyn, Y. (2012) · International Journal of Computer Applications · doi:10.5120/8930-3007 · Cited by 37
“Other methods that have been used in the literature to detect credit card fraud include: Bayesian Belief Networks [5], rule-based systems [6], decision trees[7], support vector machines [8], logistic regression[3], random forests[3], Hidden Markov Models[9], and other single algorithm data mining methods.”
MethodDetecting Credit Card Fraud Using Periodic Features
Bahnsen, A. C., Aouada, D., Stojanović, A., & Ottersten, B. (2015) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2015.28 · Cited by 37
“Different detection systems that are based on machine learning techniques have been successfully used for this problem, in particular: neural networks [3], Bayesian learning [4], artificial immune systems [5], hybrid models [6], support vector machines [7], peer group analysis [8], online learning [9] and social netwo…”
MethodAnomaly Detection using Unsupervised Methods: Credit Card Fraud Case Study
Rezapour, M. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0101101 · Cited by 56
“Different machine learning techniques such as SVM, random forest, and the logistic regression model were compared for detection of credit card fraud [11].”
MethodCredit Card Fraud Detection Using Weighted Support Vector Machine
Zhang, D., Bhandari, B., & Black, D. (2020) · Applied Mathematics · doi:10.4236/am.2020.1112087 · Cited by 38
“…using a weighting scheme to account for imbalance and cost, as well as adjusting the evaluation metrics, is not well re-searched, although [7] created a metric, wtdAcc, and examines the following weighting schema: ( ) . the authors take, w = 0.7 to indicate higher weights for accuracy on…”
BackgroundMethodInfluentialA Deep Learning Method of Credit Card Fraud Detection Based on Continuous-Coupled Neural Networks
Wu, Y., Wang, L., Li, H., & Liu, J. (2025) · Mathematics · doi:10.3390/math13050819 · Cited by 36
A comparative study of the leading machine learning techniques and two new optimization algorithms
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“…comparison of different ensemble algorithms, including bagging and AdaBoost; Perlich et al. (2003) compare decision trees and logistic regression; Bhattacharyya et al. (2011) compare support vector machines, random forests and logistic regression for detecting credit card fraud; and Caigny et…”
InfluentialVerma, P., & Tyagi, P. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-8892-8_36 · Cited by 35
A probabilistic approach driven credit card anomaly detection with CBLOF and isolation forest models
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Automatic credit card fraud detection based on non-linear signal processing
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“There is extensive literature that reviews and provides taxonomies and comparisons about the large number of methods that have been developed during the last two decades (see for instance [1][4][5][6][7] and the references within).”
BackgroundMemory-Augmented Generative Adversarial Networks for Anomaly Detection
Yang, Z., Zhang, T., Soltani, I., & Darve, E. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3132928 · Cited by 40
Graph Neural Network for Credit Card Fraud Detection
Liu, G., Tang, J. P., Tian, Y. X., & Wang, J. (2021) · International Conference on Cyber-Physical Social Intelligence (ICCSI) · doi:10.1109/iccsi53130.2021.9736204 · Cited by 34
COMBATING BANKING FRAUD WITH IT: INTEGRATING MACHINE LEARNING AND DATA ANALYTICS
Mohammad, N., Imran, M. A. U., Prabha, M., Sharmin, S., & Khatoon, R. (2024) · The American Journal of Management and Economics Innovations · doi:10.37547/tajmei/volume06issue07-04 · Cited by 34
Bayesian multinomial latent variable modeling for fraud and abuse detection in health insurance
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FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data
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“Traditional supervised learning methods such as Support Vector Machines (SVMs) and random forests have long been used to classify malware [43], [64], [5], [22], [50], detect network intrusions [48], [39], [18], [63], and identify fraudulent accounts [7], [54], [72].”
MethodYousefi, N., Alaghband, M., & Garibay, I. I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.02629 · Cited by 34
“credit card operation, the study in [10] shows the performance advantages of RF in capturing more fraud cases, with fewer false positives.”
BackgroundMethodThe role of artificial intelligence in preventing corporate crime
Segura, L. A. G. (2024) · Journal of Economic Criminology · doi:10.1016/j.jeconc.2024.100091 · Cited by 33
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Comparative study on credit card fraud detection based on different support vector machines
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“In practice, credit card fraud identification model is actively used [22].”
Credit Card Fraud Detection Using Autoencoder Neural NetworkPreprint
Zou, J., Zhang, J., & Jiang, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.11553 · Cited by 33
“In a seminar work, [6] proposes 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…”
Manogna, R. L., & Anand, A. (2023) · Kybernetes · doi:10.1108/k-04-2023-0637 · Cited by 36
No Smurfs: Revealing Fraud Chains in Mobile Money Transfers
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“[13] made a comparative study of SVM, random forest and logistic regression on a database of real-life credit card transactions.”
BackgroundMethodFraud detection in credit card transaction using machine learning techniques
Sadgali, I., Sael, N., & Benabbou, F. (2019) · International Conference on Smart Systems and Data Science (ICSSD) · doi:10.1109/icssd47982.2019.9002674 · Cited by 32
Adaptive Model for Credit Card Fraud Detection
Sadgali, I., Sael, N., & Benabbou, F. (2020) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v14i03.11763 · Cited by 32
“evaluated SVM and Random Forests approaches [5], with the Logistic Regression.”
MethodA Deep-forest based approach for detecting fraudulent online transaction
Wang, L., Zhang, Z., Zhang, X., Zhou, X., Wang, P., & Zheng, Y. (2020) · Advances in computers · doi:10.1016/bs.adcom.2020.10.001 · Cited by 32
Multilayer perceptron artificial neural networks-based model for credit card fraud detection
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Credit Card Fraud Detection with Automated Machine Learning Systems
Plakandaras, V., Gogas, P., Papadimitriou, T., & Tsamardinos, I. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2086354 · Cited by 32
“The authors train Logit Boost, Random Forests and Support Vector Machines (SVM) classifiers in forecasting credit card fraud based on an under-sampling scheme.”
MethodInfluentialRefined Weighted Random Forest and Its Application to Credit Card Fraud Detection
Xuan, S., Liu, G., & Li, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04648-4_29 · Cited by 31
Can Alert Models for Fraud Protect the Elderly Clients of a Financial Institution?
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Fraud detection via behavioral sequence embedding
Liu, G., Guo, J., Zuo, Y., Wu, J., & Guo, R.-Y. (2020) · Knowledge and Information Systems · doi:10.1007/s10115-019-01433-3 · Cited by 32
Adversarial attacks on machine learning systems for high-frequency trading
Goldblum, M., Schwarzschild, A., Patel, A., & Goldstein, T. (2021) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3490354.3494367 · Cited by 40
“Recent trends have seen finance professionals rely on automated machine learning systems for algorithmic trading [2], as robo-advisers that allocate investments and rebalance portfolios [27, 29], in fraud detection systems that identify illicit transactions [1, 8, 33], for risk models that approve/deny loans [9], and…”
BackgroundSigTran: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks
Poursafaei, F., Reihaneh Rabbany, K., & Žilić, Ž. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-75762-5_3 · Cited by 31
Credit card fraud detection using a hierarchical behavior-knowledge space model
Nandi, A. K., Randhawa, K. K., Chua, H. S., Seera, M., & Lim, C. P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0260579 · Cited by 37
“Capturing irregular transaction patterns is a vital step in fraud detection [12], and efficient and effective classification methods are required for accurate detection of credit card frauds.”
BackgroundBenedek, B., Ciumaş, C., & Nagy, B. Z. (2022) · Journal of Financial Regulation and Compliance · doi:10.1108/jfrc-11-2021-0102 · Cited by 31
Implications of imbalanced datasets for empirical ROC-AUC estimation in binary classification tasks
Liu, Y., Li, Y., & Xie, D. (2023) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2023.2238235 · Cited by 30
“Imbalanced datasets are common in many areas of applied sciences, such as medical image diagnostics [1,2], financial fraud detection [3], or cybersecurity [4].”
BackgroundEvaluation of performance enhancement in Ethereum fraud detection using oversampling techniques
Ravindranath, V., Nallakaruppan, M. K., Shri, M. L., Balusamy, B., & Bhattacharyya, S. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111698 · Cited by 35
Self-citationPredicting financial distress in TSX-listed firms using machine learning algorithms
Lokanan, M. E., & Ramzan, S. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1466321 · Cited by 47
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Crime Data Mining, Threat Analysis and Prediction
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A New Surrogating Algorithm by the Complex Graph Fourier Transform (CGFT)
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“Another totally different example is credit card fraud detection [46], where the number of legitimate operations is an order of magnitudes larger than the fraudulent operations.”
BackgroundObserving and predicting knowledge worker stress, focus and awakeness in the wild
Soto, M., Satterfield, C., Fritz, T., Murphy, G. C., Shepherd, D. C., & Kraft, N. A. (2020) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2020.102560 · Cited by 33
NAG: neural feature aggregation framework for credit card fraud detection
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The role of diversity and ensemble learning in credit card fraud detection
Paldino, G. M., Lebichot, B., Borgne, Y. L., Siblini, W., Oblé, F., Boracchi, G., & Bontempi, G. (2022) · Advances in Data Analysis and Classification · doi:10.1007/s11634-022-00515-5 · Cited by 30
“Random Forest achieved the best performance in different cases (Bhattacharyya et al. 2011; Dal Pozzolo et al. 2015a; Dal Pozzolo et al. 2014a).”
BackgroundQayoom, A., Khuhro, M. A., Kumar, K., Waqas, M., Saeed, U., Wu, Y., Wang, S., & Ur-Rehman, S. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1998 · Cited by 29
“For feature generation, some methods have been described in Bhattacharyya et al. (2011) , Khandani, Kim & Lo (2010) , Ravisankar et al. (2011) and Van Vlasselaer et al. (2015) but they could not detect complex patterns, Fu et al. (2016) introduced a novel strategy for mining complicated latent…”
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Improving Credit Card Fraud Detection by Profiling and Clustering Accounts
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“Credit card fraud transactions often highlight behavioral patterns of the cardholder [6].”
BackgroundA decision tree classifier for credit assessment problems in big data environments
Chern, C.-C., Lei, W.-U., Huang, K., & Chen, S.-Y. (2021) · Information Systems and e-Business Management · doi:10.1007/s10257-021-00511-w · Cited by 29
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Some Experimental Issues in Financial Fraud Mining
West, J., & Bhattacharya, M. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.05.515 · Cited by 29
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Enhancing Attribute-Driven Fraud Detection With Risk-Aware Graph Representation
Xiang, S., Zhang, G., Cheng, D., & Zhang, Y. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3543887 · Cited by 29
“Research in the area of financial fraud detection often focuses on credit card fraud, which involves unauthorized transactions typically made through credit or debit cards [1].”
Credit-card Fraud Detection System using Neural Networks
Balawi, S. A., & Aljohani, N. (2023) · The International Arab Journal of Information Technology · doi:10.34028/iajit/20/2/10 · Cited by 28
An empirical study on predicting cloud incidents
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Computational Techniques for Real-Time Credit Card Fraud Detection
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Novel Machine Learning Approach for Analyzing Anonymous Credit Card Fraud Patterns
Manlangit, S. (2019) · International Journal of Electronic Commerce Studies · doi:10.7903/ijecs.1732 · Cited by 27
“6 There are a number of critical factors in training the algorithm in fraud detection.”
BackgroundArtificial intelligence, ethics, and intergenerational responsibility
Klockmann, V., von Schenk, A., & Villeval, M. C. (2022) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2022.09.010 · Cited by 36
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“Today, hacker attacks, identity theft, credit card fraud, fishing for sensitive information and non-delivered merchandise are important issues of concern, growing in number as well as in geographical reach [8,14,20,21,83].”
A loan application fraud detection method based on knowledge graph and neural network
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“Its models are usually built using linear methods, such as logistic regression [4,5].”
MethodA Double Pruning Scheme for Boosting Ensembles
Soto, V., García-Moratilla, S., Martínez-Muñoz, G., Hernández-Lobato, D., & Suárez, A. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2313638 · Cited by 26
“tion prediction, reverse engineering biological networks or credit card fraud detection [11]–[17].”
BackgroundEnsemble Classification and Extended Feature Selection for Credit Card Fraud Detection
Noghani, F. F., & Moattar, M. H. (2017) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.22044/jadm.2016.788 · Cited by 26
“Statistical methods of fraud detection are divided into two broad categories, supervised and unsupervised [7].”
MethodSignal processing on graphs for improving automatic credit card fraud detection
Vergara, L., Salazar, A., Belda, J., Safont, G., Moral, S., & Iglesias, S. (2017) · IEEE International Carnahan Conference on Security Technology (ICCST) · doi:10.1109/ccst.2017.8167820 · Cited by 26
“An example is credit card fraud detection [15], where the number of legitimate operations is order of magnitudes larger than the fraudulent operations.”
BackgroundA computational study on outliers in world music
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The relative importance of ESG pillars: A two‐step machine learning and analytical framework
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“Support vector machines are linear classifiers which operate in a high dimensional property space which is a nonlinear mapping of the input space of the present problem [6], SVM is used for solving the non-linear classification problems.”
BackgroundMethodInfluentialANOMALY DETECTION IN CREDIT CARD TRANSACTIONS USING MACHINE LEARNING
Meenu, S. G., Patel, S. P., Kumar, S., & Chauhan, G. (2020) · International Journal of Innovative Research in Computer Science & Technology · doi:10.21276/ijircst.2020.8.3.5 · Cited by 25
Application of Credit Card Fraud Detection Based on CS-SVM
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A Machine Learning Framework towards Bank Telemarketing Prediction
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A Performance Analysis of Machine Learning Techniques for Credit Card Fraud Detection
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Fraud Analysis and Prevention in e-Commerce Transactions
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“Due to the importance of the fraud detection problem, we may distinguish several works that discuss this subject[3], [5], [6], [7].”
BackgroundDetecting malicious behaviour using supervised learning algorithms of the function calls
Alazab, M., & Venkatraman, S. (2013) · International Journal of Electronic Security and Digital Forensics · doi:10.1504/ijesdf.2013.055047 · Cited by 25
“The downside to neural networks’ distribution free generalisation is that they are prone to local minima and over-fitting (Bhattacharyya et al., 2011).”
BackgroundPredicting Product Returns in E-Commerce: The Contribution of Mahalanobis Feature Extraction
Urbanke, P., Kranz, J. J., & Kolbe, L. M. (2015) · International Conference on Information Systems · Cited by 25
Solving the False Positives Problem in Fraud Prediction Using Automated Feature Engineering
Wedge, R., Kanter, J. M., Veeramachaneni, K. K., Rubio, S. M., & Pérez, S. I. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10997-4_23 · Cited by 25
Leveraging feature selection to detect potential tax fraudsters
Matos, T., de Macêdo, J. A. F., Lettich, F., Monteiro, J. M., Renso, C., Perego, R., & Nardini, F. M. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.113128 · Cited by 25
Approaches to Fraud Detection on Credit Card Transactions using Artificial Intelligence Methods
Yazici, Y. (2020) · doi:10.5121/csit.2020.101018 · Cited by 25
“Behavioral fraud is, on the other hand, could be mail theft, stolen cards, counterfeit card or ‘card holder not present fraud’ [4].”
P2P Lending Fraud Detection: A Big Data Approach
Xu, J., Lü, Y., & Chau, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18455-5_5 · Cited by 25
“combined support vector machines and logistic regression models for fraud detection [5].”
Synthetic logs generator for fraud detection in mobile transfer services
Gaber, C., Hemery, B., Achemlal, M., Pasquet, M., & Urien, P. (2013) · International Conference on Collaboration Technologies and Systems (CTS) · doi:10.1109/cts.2013.6567225 · Cited by 24
“This type of frauds is realistic for mobile payment as fraud cases since they have been observed in the field of credit card payment [14] and telecommunications [15].”
BackgroundAn Empirical Method for Discovering Tax Fraudsters
Matos, T., de Macêdo, J. A. F., & Monteiro, J. M. (2014) · International Database Engineering and Applications Symposium (IDEAS) · doi:10.1145/2790755.2790759 · Cited by 24
“[9] evaluated two advanced data mining approaches, support vector machines and random forests, together with the well-known logistic regression, as part of an attempt to better detect (and thus control and prosecute) credit card fraud.”
MethodSecure UPI: Machine Learning-Driven Fraud Detection System for UPI Transactions
Rani, R., Alam, A., & Javed, A. (2024) · International Conference on Disruptive Technologies (ICDT) · doi:10.1109/icdt61202.2024.10489682 · Cited by 24
“An extremely precise and flexible classification procedure is guaranteed by the ensemble of decision trees, which have been trained successively to fix the mistakes of the earlier models [14] [15].”
MethodConfidence in predictions from random tree ensembles
Bhattacharyya, S. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-012-0600-z · Cited by 24
“Two recent studies have reported strong performance of random forests for fraud identification [23,2].”
BackgroundSelf-citationA survey - data mining frameworks in credit card processing
Wongchinsri, P., & Kuratach, W. (2016) · International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · doi:10.1109/ecticon.2016.7561287 · Cited by 24
An Assessment of Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection
Carcillo, F., Borgne, Y. L., Caelen, O., & Bontempi, G. (2017) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2017.10 · Cited by 24
“The use of machine learning for credit card fraud detection requires to address three major problems: the class imbalance of the training set (many more genuine transactions than fraudulent ones), the nonstationarity of the phenomenon (due to changes in the behavior of customers as well as in fraudsters) and the label…”
BackgroundHybrid Machine Learning System for Solving Fraud Detection Tasks
Vynokurova, O. А., Peleshko, D., Bondarenko, O., Ilyasov, V., Serzhantov, V., & Peleshko, M. (2020) · IEEE International Conference on Data Stream Mining & Processing (DSMP) · doi:10.1109/dsmp47368.2020.9204244 · Cited by 27
“As the analysis of scientific results shows that among the most popular methods for Fraud Detection are Logistic regression, Random Forests and Support Vector Machines [7, 8].”
Method"You Know What to Do": Proactive Detection of YouTube Videos Targeted by Coordinated Hate Attacks
Mariconti, E., Suárez‐Tangil, G., Blackburn, J., Cristofaro, E. D., Kourtellis, N., Ilias Leontiadis, A., Serrano, J. L., & Stringhini, G. (2018) · OpenBU (Boston University) · doi:10.48550/arxiv.1805.08168 · Cited by 27
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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