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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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Data-driven financial fraud detection using hybrid artificial and quantum intelligence
Mia, M. S., Roy, S., Ihsan, M. A., Hossain, S., & Ahamed, M. K. U. (2025) · BenchCouncil Transactions on Benchmarks Standards and Evaluations · doi:10.1016/j.tbench.2025.100252 · Cited by 11
Financial Fraud and Anomaly Detection Techniques: A Literature Review
Tao, Z., & Chao, J. (2023) · International Conference on Computer Science and Management Technology (ICCSMT) · doi:10.1145/3644523.3644639 · Cited by 6
“However, along with this growth, the payment industry has also witnessed a significant increase in fraud risks and the misuse of credit or debit cards [19-20].”
BackgroundFraud Detection using Quantum Machine Learning(QML)
Kamble, S. S., Pawar, S. S., Veer, T. B., & Padulkar, D. M. (2024) · Global Conference on Communications and Information Technologies (GCCIT) · doi:10.1109/gccit63234.2024.10862508 · Cited by 6
Statistical and Spending Behavior based Fraud Detection of Card-based Payment System
Teh, B., Islam, M. B., Appadurai, N. K., Islam, M. K., & Eaganathan, U. (2018) · doi:10.1109/iceltics.2018.8548878 · Cited by 6
“…techniques are either supervised or unsupervised; supervised techniques involve training a system beforehand with a dataset for which the outcomes are already known, whereas unsupervised techniques involve the comparison of data in dataset and the detection of statistical outliers [4], [5], [10].”
MethodDrilling Into Dashboards: Responding to Computer Recommendation in Fraud Analysis
Morar, N., Baber, C., McCabe, F., Starke, S., Skarbovsky, I., Artikis, A., & Correai, I. (2019) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2019.2925619 · Cited by 3
“THE analysis of credit card fraud has become highly automated in recent years [5], [8], [9], [13], [30].”
BackgroundMethodNOVEL LEARNING STRATEGY BASED ON GENETIC PROGRAMMING FOR CREDIT CARD FRAUD DETECTION IN BIG DATA
Benchaji, I., Douzi, S., & Ouahidi, B. E. (2019) · doi:10.33965/bigdaci2019_201907l001 · Cited by 6
“…machine learning problem and different techniques have been proposed to deal with it (Aleskerov, E. et al, 1997; Whitrow, C. et al, 2009; Sánchez, D. et al, 2009; Bhattacharyya, S. et al, 2011; Jha, S. et al, 2012; Sahin, Y. et al, 2013; Dal Pozzolo, A. et al, 2014; Bahnsen, A. C. et al, 2015).”
Learning Transaction Cohesiveness for Online Payment Fraud Detection
Cui, J., Yan, C., & Wang, C. (2021) · The 2nd International Conference on Computing and Data Science · doi:10.1145/3448734.3450489 · Cited by 6
“To tackle this problem, existing studies mainly adopt feature engineering approaches [8] or transaction aggregation [9] [10].”
MethodPredicting hotel reviews from sentiment: a multinomial classification framework
YÜCEL, A., Çağlar, M., Dolatsara, H. A., George, B., & Dağ, A. (2021) · Journal of Modelling in Management · doi:10.1108/jm2-09-2020-0255 · Cited by 6
Fraudulent credit card transaction detection using soft computing techniques
Priyadarshini, A., Mishra, S., Mishra, D. P., Salkuti, S. R., & Mohanty, R. (2021) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v23.i3.pp1634-1642 · Cited by 6
“The rules are based on patterns found in the data using various intelligent machine learning algorithms, such as logistic regression (LR) and support vector machines (SVM) [18], [19].”
MethodPerformance Comparison of Anomaly Detection Algorithms
Kumar, M. M., & Ramya, G. R. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-5529-6_58 · Cited by 6
Imbalance example-dependent cost classification: A Bayesian based method
Mediavilla-Relaño, J., Lázaro, M., & Figueiras-Vidal, A. R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118909 · Cited by 6
Convex and nonconvex nonparametric frontier-based classification methods for anomaly detection
Jin, Q., Kerstens, K., & de Woestyne, I. V. (2024) · OR Spectrum · doi:10.1007/s00291-024-00751-5 · Cited by 6
“…as in medical diagnosis (Park et al. 2010; da Silva et al. 2021), faults and failure detection in complex systems (Clifton et al. 2014; Jiang et al. 2020), and credit card or telecom fraud detection (Bhattacharyya et al. 2011; Ahmed et al. 2016; Al-Hashedi and Magalingam 2021) to name but a few.”
Atakpa, M. I., & Abetoh, N. F. (2023) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2342441 · Cited by 37
Employing Self-Organizing Map for Fraud Detection
Olszewski, D., Kacprzyk, J., & Zadrożny, S. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38658-9_14 · Cited by 6
Fraud detection in web transactions
Lima, R. A. F., & Pereira, A. C. M. (2012) · Brazilian Symposium on Multimedia and the Web · doi:10.1145/2382636.2382695 · Cited by 6
Mining shopping behavior in the Taiwan luxury products market
Wen, C.-H., Liao, S.-H., Chang, W., & Hsu, P. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.03.072 · Cited by 6
Application of SIRUS in Credit Card Fraud Detection
Zhang, Y., Liu, G., Luan, W., Yan, C., & Jiang, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04648-4_6 · Cited by 6
Network-Based Feature Extraction Method for Fraud Detection via Label Propagation
Zhao, P., Fu, X., Wu, W., Li, D., & Li, J. K. (2019) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp.2019.8679414 · Cited by 6
“The fraud detection models used in the field of online lending are mainly divided into three categories: logical regression [6] [7], neural network [8] [9] [10], and integrated model based on tree algorithm [11] [12] [13].”
MethodSome Experimental Issues in Financial Fraud Detection: An Investigation
West, J., & Bhattacharya, M. (2015) · IEEE International Conference on Smart City/SocialCom/SustainCom · doi:10.1109/smartcity.2015.227 · Cited by 6
“performed several credit card fraud experiments, comparing two common classification solutions against the well-known logistic regression and observing results across various common metrics [1].”
BackgroundMethodInfluentialCredit card fraud detection by dynamic incremental semi-supervised fuzzy clustering
Casalino, G., Castellano, G., & Mencar, C. (2019) · European Society for Fuzzy Logic and Technology Conference · doi:10.2991/eusflat-19.2019.30 · Cited by 6
Outlier Detection with Data Mining Techniques and Statistical Methods
Orellana, M., & Cedillo, P. (2019) · International Conference on Information Systems and Computer Science (INCISCOS) · doi:10.1109/inciscos49368.2019.00017 · Cited by 6
“generate noise [21].”
Taxonomy of Fraud Detection Metrics for Business Processes
Omair, B., & Alturki, A. (2020) · IEEE Access · doi:10.1109/access.2020.2987337 · Cited by 6
“Consequently, fraud is a severe problem with far-reaching consequences [7], [8].”
BackgroundHybrid bio-inspired model for fraud detection with correlation based feature selection
Singh, A., & Jain, A. (2021) · Journal of Discrete Mathematical Sciences and Cryptography · doi:10.1080/09720529.2021.1932929 · Cited by 6
“Singh and Ujjwal have proposed anomaly detection approach based on Gini Impurity for attack [12].”
MethodSilva, J. C. S., Macêdo, D., Zanchettin, C., Oliveira, A. L. I., & Filho, A. T. D. A. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533313 · Cited by 6
“Another relevant issue is the high amount of losses registered in a figure of billions of dollars [2]–[6].”
BackgroundA Comparison of Data Balancing Techniques for Credit Card Fraud Detection using Neural Network
Uttam, A. K., & Sharma, G. (2021) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac52330.2021.9640911 · Cited by 6
Subaşı, A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821379-7.00005-9 · Cited by 6
Improving the Data Quality for Credit Card Fraud Detection
Jing, R., Tian, H., Li, Y., Zhang, X., Zheng, X., Zhang, Z., & Zeng, D. D. (2020) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi49825.2020.9280510 · Cited by 6
“However, the rise of such cashless electronic financial transactions has also led to the emergence of new financial fraud behaviors [25].”
BackgroundNAG: Neural Feature Aggregation Framework for Credit Card Fraud Detection
Dastidar, K. G., Jurgovsky, J., Siblini, W., He-Guelton, L., & Granitzer, M. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00018 · Cited by 6
“This includes different discriminant approaches such as using random forests and logistic regression [5] or approaches that tackle specific peculiarities of the fraud detection domain such as concept drift [6], and class imabalance [7].”
MethodYapay Zeka Destekli ÇOKS Yöntemi ile Kredi Kartı Sahtekarlığının Tespiti
Keskenler, M. F., Dal, D., & Aydın, T. (2021) · El-Cezeri Fen ve Mühendislik Dergisi · doi:10.31202/ecjse.908260 · Cited by 6
Financial statement fraud detection using supervised learning methods
Gepp, A. (2015) · Bond University Research Portal (Bond University) · Cited by 6
A Primer on Artificial Intelligence and Machine Learning for the Financial Services IndustryPreprint
Osterrieder, J. R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4349078 · Cited by 6
Chowdhury, S., Ahsan, M., & Barraclough, P. (2025) · Applied Sciences · doi:10.3390/app15105241 · Cited by 5
Graph Learning-Empowered Financial Fraud Detection: Progress and Future Directions
Li, E., Chen, M., Xiang, S., & Chen, L. (2025) · Intelligent Computing · doi:10.34133/icomputing.0146 · Cited by 5
Explainable artificial intelligence models for detecting suspicious bank transactions
Narasapuram, N. K., Shah, S. A. A., Shiratuddin, M. F., & Sohel, F. A. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-02994-w · Cited by 5
“● RF is used as one of the most used algorithms in fraud detection systems due to its accuracy and robustness [112 , 113].”
False Positives in Credit Card Fraud Detection: Measurement and Mitigation
Wallny, F. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.195 · Cited by 5
“Most publications report only standard classification metrics such as accuracy, precision and recall [6,7,8].”
BackgroundChen, L., Jia, N., Jiao, Z., Zhao, H., Cui, R., & Wang, H. (2024) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2024.07.011 · Cited by 5
Harris, O., & Andrews, M. (2024) · World Journal of Advanced Engineering Technology and Sciences · doi:10.30574/wjaets.2024.13.2.0584 · Cited by 5
Li, J., Li, Y., Han, H., & Lu, X. B. (2021) · International Conference on Computing and Big Data (ICCBD) · doi:10.1145/3507524.3507537 · Cited by 5
“The class imbalance problem is expected in the diagnosis of rare diseases [11], detection of credit card fraud [12], prediction of loan risk [13], and so on.”
BackgroundA New Improved Method for Online Credit Anti-Fraud
Kang, H., & Zhang, H. (2022) · Automatic Control and Computer Sciences · doi:10.3103/s0146411622040046 · Cited by 5
“Classification algorithms like logistic regression, supporting vector machines, and random forests are the most common [3] ones.”
BackgroundChergui, H., Lylia Abrouk, A., Cullot, N., & Nicolas, C. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140298 · Cited by 5
“There are few works based on SWIFT transactions [20] or international transactions fields (countries or currencies) [21] to extract features.”
BackgroundAutomatic Card Fraud Detection Based on Decision Tree Algorithm
Flondor, E., Donath, L. E., & Neamţu, M. (2024) · Applied Artificial Intelligence · doi:10.1080/08839514.2024.2385249 · Cited by 5
“Bhattacharyya et al. (2011) investigate the effectiveness of random forests and support vector machines, as data mining techniques, both being able to thoroughly detect card fraud.”
Gupta, V., Mishra, N., Dash, Y., Kumar, U., & Abraham, A. (2025) · International Conference on Communication, Security and Artificial Intelligence (ICCSAI) · doi:10.1109/iccsai64074.2025.11064532 · Cited by 6
“Early works such as [4] compared various classification algorithms, including logistic regression, neural networks, and decision trees.”
Introducing a Vector Space Model to Perform a Proactive Credit Scoring
Saia, R., & Carta, S. (2018) · Communications in computer and information science · doi:10.1007/978-3-319-99701-8_6 · Cited by 5
ATM Fraud Detection using Behavior Model
Laimek, R., & Kaothanthong, N. (2018) · Asian Conference on Defence Technology · doi:10.1109/acdt.2018.8593092 · Cited by 5
A Legendre Neural Network for Credit Card Fraud Detection
Dash, R., Dash, R., & Dash, R. (2020) · Smart innovation, systems and technologies · doi:10.1007/978-981-15-6202-0_42 · Cited by 5
Role of Machine Learning in Credit Card Fraud Detection
Ghai, V., & Kang, S. S. (2021) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n53548.2021.9725540 · Cited by 5
“Electronic fraud is characterized by the use of the internet as any sort of fraud [24].”
BackgroundFlores, L., Hernandez, R., Tolentino, L. C., Mendez, C., & Fernando, M. G. Z. (2022) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon55314.2022.9908827 · Cited by 5
An Antifraud System for Tourism SMEs in the Context of Electronic Operations with Credit Cards
Rebón, F., Castander, I., Argandoña, J., Gerrikagoitia, J. K., & Alzua-Sorzabal, A. (2015) · American Journal of Intelligent Systems · Cited by 5
Is the cardholder an efficient alarm system to detect credit card incidents?
Pavía, J. M., & Veres‐Ferrer, E. J. (2015) · International Journal of Consumer Studies · doi:10.1111/ijcs.12246 · Cited by 5
Tang, D., Zhang, M., Xu, J., Zhang, X., Yang, F. O., Li, H., Feng, L., Wang, K., & Zheng, Y. (2018) · Complexity · doi:10.1155/2018/9193248 · Cited by 5
Application of Computational Intelligence to Identify Credit Card Fraud
Nur-E-Arefin, M., & Islam, M. S. (2018) · doi:10.1109/ciet.2018.8660840 · Cited by 5
“There have been many studies describing the different types of frauds and different fraud detection techniques [4–8].”
Background(2019) · i-manager’s Journal on Pattern Recognition · doi:10.26634/jpr.5.3.15676 · Cited by 5
Wang, Y., & Ni, X. S. (2019) · International Journal of Managing Information Technology · doi:10.5121/ijmit.2019.11101 · Cited by 5
Classification of switching intentions toward internet telephony services: a quantitative analysis
Ha, S. H., & Yang, J. (2012) · Information Technology and Management · doi:10.1007/s10799-012-0151-8 · Cited by 5
“Logistic regression is a special form of regression analysis and is used when a dependent variable is binary [8].”
MethodArtificial Intelligence, Ethics, and Intergenerational ResponsibilityPreprint
Series, G. W. P., Klockmann, V., von Schenk, A., & Villeval, M. C. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3853827 · Cited by 5
Exploring Resampling Techniques in Credit Card Default PredictionPreprint
Lokanan, M. E. (2024) · Research Square · doi:10.21203/rs.3.rs-4087259/v1 · Cited by 5
A new fusion neural network model and credit card fraud identification
Jiang, S., Liao, X., Feng, Y., Gao, Z., & Onasanya, B. O. (2024) · PLoS ONE · doi:10.1371/journal.pone.0311987 · Cited by 4
“In the realm of deep learning, scholars have used supervised learning algorithms such as artificial neural networks (ANN), support vector machines (SVM), and deep neural networks (DNN) to predict fraud risks, achieving high recognition accuracy [21,22].”
BackgroundIdentifying Illicit Activities in Blockchain Transaction Graph Networks
Adam, T., & Babič, F. (2025) · Electronics · doi:10.3390/electronics14234599 · Cited by 7
Identification of Fraudulent Transactions for Enhanced Credit Card Fraud Detection
Gupta, M., Gayathri, V., Narula, R., Kumar, V., Kaur, S. P., & Singh, N. (2024) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs62700.2024.10840673 · Cited by 4
“The paper [17] focuses on various machine learning algorithms to predict credit card fraud, particularly on supervised classification methods: Random Forest, Logistic Regression, Naive Bayes, and AdaBoost.”
Karnavou, E., Cascavilla, G., Marcelino, G., & Geradts, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128148 · Cited by 4
Credit Card Fraud Detection via Machine Learning
Alsufyani, K. H., AlMuallim, A. F., Alshahrani, M., Alsufyani, A., Alhanaya, O., & Zerguine, A. (2022) · International Multi-Conference on Systems, Signals & Devices (SSD) · doi:10.1109/ssd54932.2022.9955815 · Cited by 4
Auwul, M. R., Hakim, M. A., Dhonno, F. T., Shilpa, N. A., Sohag, A., & Abedin, M. Z. (2023) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-031-18552-6_5 · Cited by 4
Credit Card Default Prediction based on Machine Learning Techniques
Zhang, Z. (2023) · BCP Business & Management · doi:10.54691/bcpbm.v44i.4954 · Cited by 4
Zhang, W., Zhang, S., Chen, J., & Huang, H. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191810 · Cited by 4
“Credit card fraud refers to the unauthorized access of a credit card and using it to make transactions [3].”
BackgroundAdvancing Fraud Detection Systems Through Online Learning
Paladini, T., Bernasconi, M., Carminati, M., Polino, M., Trovò, F., & Zanero, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43427-3_17 · Cited by 4
Why do Chinese multinationals in South Africa get benefits from digital business strategy?
Rong, K., & Liu, R. (2024) · South African Journal of Economic and Management Sciences · doi:10.4102/sajems.v27i1.5473 · Cited by 4
“Employing data mining techniques (Bhattacharyya et al. 2011), MNEs can distil the copious amounts of data produced in the digital realm.”
MethodSaini, D. K. J. B., Shelke, N. M., Prajwalasimha, S. N., Pimpalkar, A. P., Kumar, G. H., & Monish, L. (2025) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet64471.2025.11139964 · Cited by 4
“More sophisticated tools, i.e., SVM and Hidden Markov Models (HMMs), have also been employed in detecting anomalies in financial transactions but face challenges under real-time processing limits and scalability issues [10-12].”
Suspicious Behavior Detection in Debit Card Transactions using Data Mining
Saghehei, E., & Memariani, A. (2015) · Information Resources Management Journal · doi:10.4018/irmj.2015070101 · Cited by 4
“Bhattacharyya et al. (2011) applied support vector machines and random forests techniques as a comparative study.”
MethodEnhanced Anomaly Detection in Imbalanced Credit Card Transactions using Hybrid PSO
Sivakumar, N., & Balasubramanian, R. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016908520 · Cited by 4
A Signature Based Method for Fraud Detection on E-Commerce Scenarios
Belo, O., Mota, G., & Fernandes, J. (2016) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-319-25226-1_45 · Cited by 4
Chattopadhyay, M., & Mitra, S. K. (2016) · Computational and Mathematical Organization Theory · doi:10.1007/s10588-016-9237-x · Cited by 4
“In the last few decades, numerous classification algorithms have been used in diverse research areas (Wong and Selvi 1998; Cao and Parry 2009; Bhattacharyya et al. 2011; Austin et al. 2013; Zhu et al. 2015).”
BackgroundAuthor Collaboration in Ten Years of IPS²: A Bibliometric Analysis
Knop, S., Merchel, R., & Pöppelbuß, J. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.03.092 · Cited by 4
Unsupervised Machine Learning for Card Payment Fraud Detection
Parreno-Centeno, M., Ali, M. A., Guan, Y., & van Moorsel, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-41568-6_16 · Cited by 4
Shareholder Wealth Effects of Audit Data Analytics Announcements
Kim, J. W., Lim, J., & Yoon, K. (2020) · Accounting Horizons · doi:10.2308/horizons-19-140 · Cited by 4
Credit Card Fraud Prevention Planning using Fuzzy Cognitive Maps and Simulation
Dar, H. S., Abbasi, A., & Naveed, A. (2020) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito48877.2020.9198002 · Cited by 4
Real Time Data Science Decision Tree Approach to Approve Bank Loan from Lawyer’s Perspective
Pasupuleti, R., Alam, M., Tahernezhadi, M., Vikram, C., & Phaneendra, P. N. (2020) · International Conference on Machine Learning and Applications · doi:10.1109/icmla51294.2020.00150 · Cited by 4
“Loan Prediction System allows switch to specific application so that it can be check on priority basis [13,14].”
BackgroundEffects of Resampling Techniques on Imbalanced Data Classification: A New Under-resampling Method
Nguyen, S. T., Schumacher, P. A., Olinsky, A. D., & Quinn, J. (2021) · Emerald Group Publishing Limited eBooks · doi:10.1108/s1477-407020210000014005 · Cited by 4
Consumer Fraud Detection via P-feature Conversion
Lai, S., Wu, J., Ma, Z., Ye, C., & Zhou, H. (2021) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac51774.2021.00052 · Cited by 4
“Bhattacharyya et al. [17] tested random forest (RF), support vector machine (SVM) and logistic regression (LOR) using one-year transaction data from an international financial institution.”
MethodNaïve Bayes Based Classifier for Credit Card Fraud Discovery
Ogundokun, R. O., Misra, S., Fatigun, O. J., & Adeniyi, J. K. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-030-95947-0_37 · Cited by 4
Credit Card Fraud Detection Using Deep Learning Approach (LSTM) Under IoT Environment
Tayeb, B., Amine, A., Hamou, R. M., & Kumar, A. V. S. (2022) · International Journal of Organizational and Collective Intelligence · doi:10.4018/ijoci.305207 · Cited by 5
Korkoman, M. J., & Abdullah, M. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-222344 · Cited by 5
Fraud Detection of Credit Card using Data Mining Techniques
Nishi, N. J., Sunny, F. A., & Bakchy, S. C. (2022) · International Conference on Sustainable Technologies for Industry 5.0 (STI) · doi:10.1109/sti56238.2022.10103292 · Cited by 4
“There is a comparative study among the SVM, logistic regression and ANN, they proved that SVM classification is giving better accuracy than two other algorithms [24].”
DEFD: Adapted Decision Tree Ensemble for Financial Fraud Detection
Chergui, H., Lylia Abrouk, A., Cullot, N., & Nicolas, C. (2023) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-28332-1_29 · Cited by 4
Developing an Intelligent Credit Card Fraud Detection System with Machine Learning
Dabade, O., Admane, A., Shitole, D., & Kamble, V. (2022) · Journal of Artificial Intelligence Machine Learning and Neural Network · doi:10.55529/jaimlnn.21.45.53 · Cited by 4
Innovative approaches in forensic accounting: The role of data analytics
Kapo, A., Turulja, L., & Vidačak, Z. (2024) · Journal of Forensic Accounting Profession · doi:10.2478/jfap-2024-0001 · Cited by 4
Decision Tree Approach to Discovering Fraud in Leasing Agreements
Horvat, I., Bach, M. P., & Skok, M. M. (2014) · Business Systems Research Journal · doi:10.2478/bsrj-2014-0010 · Cited by 4
Analysis of Resampling Techniques on Predictive Performance of Credit Card Classification
Anis, M., Ali, M., Mirza, S. A., & Munir, M. M. (2020) · Modern Applied Science · doi:10.5539/mas.v14n7p92 · Cited by 4
A Study of Data Requirements for Data Mining Applications in Banking
Ranjbarfard, M., & Ahmadi, S. (2020) · Journal of Digital Information Management · doi:10.6025/jdim/2020/18/3/109-117 · Cited by 4
“Data mining methods of business intelligence have been applied to improve banking operations such as fraud detection [7], [8], [9], credit assessment [10], [11], [12], customer churn prediction, etc.”
MethodElectronic Credit Card Fraud Detection System by Collaboration of Machine Learning Models
(2019) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.l1028.10812s19 · Cited by 4
Address Fraud: Monkey Typed Address Classification for e-Commerce Applications.
Babu, T. R., & Kakkar, V. (2017) · Cited by 4
A New Method for Fraud Detection in Credit Cards Based on Transaction Dynamics in Subspaces
Salazar, A., Safont, G., & Vergara, L. (2019) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci49370.2019.00137 · Cited by 4
“There are several methods that have been proposed to solve this problem, see for instance [1-13] and the references within.”
MethodNew Perspectives of Pattern Recognition for Automatic Credit Card Fraud Detection
Salazar, A., Safont, G., Rodríguez, A., & Vergara, L. (2017) · IGI Global eBooks · doi:10.4018/978-1-5225-2255-3.ch428 · Cited by 4
Breadth-first, Depth-next Training of Random ForestsPreprint
Anghel, A., Ioannou, N., Parnell, T. P., Papandreou, N., Mendler-Dünner, C., & Pozidis, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.06853 · Cited by 4
“Random forest (RF) models [4] are a powerful tool in machine learning (ML) that are being used in many applications such as bioinformatics [3], climate change modeling [16] and credit card fraud detection [2].”
Leveraging Machine Learning Algorithms for Real-Time Fraud Detection in Digital Payment SystemsPreprint
Paripati, L. K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5052498 · Cited by 4
Financial Fraud Detection Leveraging on Deep Learning
Rathod, V. K., Gupta, M., Rao, N., & Kumar, R. (2025) · International Conference on Circuit, Power and Computing Technologies · doi:10.1109/iccpct65132.2025.11176772 · Cited by 3
“Bhattacharyya et al. [7] demonstrated the effectiveness of Random Forests in reducing false positives, while Carcillo et al. [8] proposed ensemble approaches to knob the class imbalance problem commonly found in scam detection datasets.”
Sztuczna inteligencja w systemach informatycznych zarządzania w środowisku procesowym
Oleś-Filiks, M. (2025) · Wydawnictwa Uniwersytetu Warszawskiego eBooks · doi:10.7172/978-83-235-7092-9.swwz.33 · Cited by 5
Jump-Attentive Graph Learning for Camouflaged Financial Fraud in Transaction Networks
Muppuri, P. (2026) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi68868.2026.11465630 · Cited by 3
“These models improved accuracy but were hampered by class imbalance and limited capacity to model temporal or relational dependencies [12].”
A review of deep learning-based Unsupervised Anomaly Detection in brain MRI
Behrendt, F., Bhattacharya, D., Maack, L., Krüger, J., Opfer, R., & Schlaefer, A. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104076 · Cited by 3
Development of Deep Learning based Intelligent Approach for Credit Card Fraud Detection
Krishnan, V. G., Saradhi, M. V. V., Prakash, T. A. M., Kannan, K. G., & Julaiha, A. N. (2022) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v10i12.5894 · Cited by 3
Nama, F. A., Obaid, A. J., & Alrammahi, A. A. H. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-6553-3_31 · Cited by 3
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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