Cited by 1,108Imbalanced Data Classification TechniquesPrivacy-Preserving Technologies in DataFinancial Distress and Bankruptcy Prediction

The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index.

Data mining for credit card fraud: A comparative study

Bhattacharyya, Siddhartha, Jha, Sanjeev, Tharakunnel, Kurian, Westland, J. Christopher·2011·Decision Support Systems

DOI 10.1016/j.dss.2010.08.008

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.dss.2010.08.008

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Applied logistic regression
(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305
Statistical Learning Theory
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
Applied Logistic Regression
Hosmer, D. W., & Lemeshow, S. (2000) · doi:10.1002/0471722146 · Cited by 20,302
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
Advances in kernel methods: support vector learning
Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809
Fast Training of Support Vector Machines Using Sequential Minimal Optimization
Platt, J. C. (1998) · The MIT Press eBooks · doi:10.7551/mitpress/1130.003.0016 · Cited by 5,501
The Handbook of brain theory and neural networks
(1996) · Choice Reviews Online · doi:10.5860/choice.33-4484 · Cited by 3,913
Random forest classifier for remote sensing classification
Pal, M. (2005) · International Journal of Remote Sensing · doi:10.1080/01431160412331269698 · Cited by 3,402
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
(2001) · Kybernetes · doi:10.1108/k.2001.30.1.103.6 · Cited by 3,359

Flagged first, then by citations

Show all 52 references

1 of the 52 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

Provost, 2002, Comment on Bolton and Hand, Statistical Science, 17, 249

Sorted by citations

Items 101–200 of 1,001

Analysis on Credit Card Fraud Detection Techniques: Based on Certain Design Criteria
Zareapoor, M., Seeja.K.R, S. K. R., & Alam, M. A. (2012) · International Journal of Computer Applications · doi:10.5120/8184-1538 · Cited by 106
An Efficient Real Time Model For Credit Card Fraud Detection Based On Deep Learning
Abakarim, Y., Lahby, M., & Attioui, A. (2018) · International Conference on Intelligent Systems: Theories and Applications · doi:10.1145/3289402.3289530 · Cited by 116
“Based on the transactions history, machine learning showed promising new methods to analyze the behavior of users and detect if there is a fraud or not [24] [33] [5].”
Method
Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques
Mehbodniya, A., Alam, I., Pande, S. D., Neware, R., Rane, K. P., Shabaz, M., & Madhavan, M. V. (2021) · Security and Communication Networks · doi:10.1155/2021/9293877 · Cited by 96
“In paper [23], there is a comparison between the different classification models such as Logistic Regression and different artificial neural networks are developed to train and test on a dataset of fraud detection with highly skewed data [22].”
Method
Retracted
The Use of Predictive Analytics in Finance
Broby, D. (2022) · The Journal of Finance and Data Science · doi:10.1016/j.jfds.2022.05.003 · Cited by 130
Improving crime count forecasts using Twitter and taxi data
Vomfell, L., Härdle, W. K., & Lessmann, S. (2018) · Decision Support Systems · doi:10.1016/j.dss.2018.07.003 · Cited by 93
“2014). We concentrate on random forest (RF), gradient boosting machines (GBMs), and feed-forward articial neural networks (ANNs), all of which have shown promising results in previous studies (e.g. Bhattacharyya et al., 2011; Delen, 2010). RF develops an ensemble of size kthrough drawing kbootstrap samples from the tr…”
Result
A robust hybrid machine learning model for Bengali cyber bullying detection in social media
Akhter, A., Acharjee, U. K., Talukder, M. A., Islam, M. M., & Uddin, M. A. (2023) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100027 · Cited by 92
“By analyzing the correlation from a provided group of classed data, it helps classify data into distinct classes Bhattacharyya et al. (2011).”
Background
Learning temporal representation of transaction amount for fraudulent transaction recognition using CNN, Stacked LSTM, and CNN-LSTM
Heryadi, Y., & Warnars, H. L. H. S. (2017) · IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) · doi:10.1109/cyberneticscom.2017.8311689 · Cited by 93
“Many methods have been proposed such as: Random forest [4] [5], Decision Tree [6], K-Nearest Neighbors [7], SVM [8] [9], HMM [10], neural networks [11], Bayesian learning [11], artificial immune systems [12], association rules [13], hybrid models [14], discriminant analysis [15].”
Method
"You Know What to Do"
Mariconti, E., Suárez‐Tangil, G., Blackburn, J., Cristofaro, E. D., Kourtellis, N., Ilias Leontiadis, A., Serrano, J. L., & Stringhini, G. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359309 · Cited by 90
Anomaly detection in business processes using process mining and fuzzy association rule learning
Sarno, R., Sinaga, F., & Sungkono, K. R. (2020) · Journal Of Big Data · doi:10.1186/s40537-019-0277-1 · Cited by 90
Fraud detection for E-commerce transactions by employing a prudential Multiple Consensus model
Carta, S., Fenu, G., Recupero, D. R., & Saia, R. (2019) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.02.007 · Cited by 89
Credit rating with a monotonicity-constrained support vector machine model
Chen, C.-C., & Li, S.-T. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.05.035 · Cited by 89
Interleaved Sequence RNNs for Fraud Detection
Branco, B., Abreu, P., Gomes, A. S., Almeida, M. S. C., Ascensão, J. T., & Bizarro, P. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403361 · Cited by 105
“Frequently used models are tree-based learners, such as Random Forests [4], XGBoost [6], or LightGBM [16].”
Method
Assessing effects of economic factors on construction cost estimation using deep neural networks
Wang, R., Asghari, V., Cheung, C. M., Hsu, S., & Lee, C.-J. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.104080 · Cited by 102
Digital payment fraud detection methods in digital ages and Industry 4.0
Chang, V. I. C., Doan, L. M. T., Stefano, A. D., Sun, Z., & Fortino, G. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107734 · Cited by 86
Evaluation and aggregation of pay-as-you-drive insurance rate factors: A classification analysis approach
Paefgen, J., Staake, T., & Thiesse, F. (2013) · Decision Support Systems · doi:10.1016/j.dss.2013.06.001 · Cited by 89
“identical subsets of predictor variables across models [6].”
Background
Nearest-neighbor-based approach to time-series classification
Lee, Y., Wei, C., Cheng, T.-H., & Yang, C.-T. (2012) · Decision Support Systems · doi:10.1016/j.dss.2011.12.014 · Cited by 85
TRANSFORMING FINTECH FRAUD DETECTION WITH ADVANCED ARTIFICIAL INTELLIGENCE ALGORITHMS
Shoetan, P. O., & Familoni, B. T. (2024) · Finance & Accounting Research Journal · doi:10.51594/farj.v6i4.1036 · Cited by 84
Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest
Lin, T.-H., & Jiang, J. (2021) · Mathematics · doi:10.3390/math9212683 · Cited by 82
Detecting Credit Card Fraud Using Selected Machine Learning Algorithms
Puh, M., & Brkić, L. (2019) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro.2019.8757212 · Cited by 79
Machine Learning for Credit Card Fraud Detection System
Rahangdale, S., Gedam, S., Pusam, D., & Harne, P. (2022) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-5365 · Cited by 75
A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance
Rajora, S., Li, D.-L., Jha, C., Bharill, N., Patel, O. P., Joshi, S., Puthal, D., & Prasad, M. (2018) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2018.8628930 · Cited by 80
Improving Credit Card Fraud Detection with Calibrated Probabilities
Bahnsen, A. C., Stojanović, A., Aouada, D., & Ottersten, B. (2014) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611973440.78 · Cited by 73
Credit risk assessment for unbalanced datasets based on data mining, artificial neural network and support vector machines
Khemakhem, S., Said, F. B., & Boujelbène, Y. (2018) · Journal of Modelling in Management · doi:10.1108/jm2-01-2017-0002 · Cited by 72
Real Time Data-Driven Approaches for Credit Card Fraud Detection
Tran, P. H., Tran, K. P., Hương, T. T., Heuchenne, C., HienTran, P., & Le, T. M. H. (2018) · International Conference on E-Business and Applications (ICEBA) · doi:10.1145/3194188.3194196 · Cited by 72
“For further details see, for instance, [18]–[20], [21] and [22].”
Background
Credit Card Fraud Detection through Parenclitic Network Analysis
Zanin, M., Romance, M., Moral, S., & Criado, R. (2018) · Complexity · doi:10.1155/2018/5764370 · Cited by 78
Comparative Evaluation of Credit Card Fraud Detection Using Machine Learning Techniques
Adepoju, O., Wosowei, J. B., Lawte, S., & Jaiman, H. (2019) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat47503.2019.8978372 · Cited by 71
“Design are projected based on features of deceptive and legit operations in supervised fraud detection methods [20] to classify new transactions as fraudulent or legitimate while outliers’ transactions are detected as potential instances of”
Background
Selection Features and Support Vector Machine for Credit Card Risk Identification
Rtayli, N., & Enneya, N. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.05.012 · Cited by 71
Combining oversampling and undersampling techniques for imbalanced classification: A comparative study using credit card fraudulent transaction dataset
Shamsudin, H., Yusof, U. K., Jayalakshmi, A., & Khalid, M. N. A. (2020) · International Conference on Control and Automation · doi:10.1109/icca51439.2020.9264517 · Cited by 71
“Undersampling requires shorter training time, but it is risky in which it will ignore potentially useful data [9], [14], [12].”
BackgroundMethod
Machine learning applied to tourism: A systematic review
Núñez, J. C. S., Gomez-Pulido, J. A., & Ramírez, R. R. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1549 · Cited by 93
“…et al., 2019), such as predicting demand (Silva et al., 2019), classifying opinions in social networks (Vairetti et al., 2020), detecting fraud (Bhattacharyya et al., 2011) or estimating tourist spending (Madzík et al., 2023) and optimizing processes such as hotel cancellations or estimating…”
Online pruning of base classifiers for Dynamic Ensemble Selection
Oliveira, D. V. R., Cavalcanti, G. D. C., & Sabourin, R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.06.030 · Cited by 71
Predicting congenital heart defects: A comparison of three data mining methods
Luo, Y., Li, Z., Guo, H., Cao, H., Song, C., Guo, X., & Zhang, Y. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177811 · Cited by 70
Cost-sensitive Risk Induced Bayesian Inference Bagging (RIBIB) for credit card fraud detection
Akila, S., & Reddy, U. S. (2018) · Journal of Computational Science · doi:10.1016/j.jocs.2018.06.009 · Cited by 70
Fraud Transactions Detection via Behavior Tree with Local Intention Calibration
Liu, C., Zhong, Q., Ao, X., Sun, L., Lin, W., Feng, J., He, Q., & Tang, J. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403354 · Cited by 73
“Traditional fraud transactions detection approaches generally calculate statistical features based on user profiles and historical trans-actions, and detect fraud transactions by supervised learning meth-ods [4, 17, 23].”
Background
Imbalanced credit card fraud detection data: A solution based on hybrid neural network and clustering-based undersampling technique
Huang, H., Лю, Б., Xue, X., Cao, J., & Chen, X. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111368 · Cited by 68
FraudGNN-RL: A Graph Neural Network With Reinforcement Learning for Adaptive Financial Fraud Detection
Cui, Y., Han, X., Chen, J., Zhang, X., Yang, J., & Zhang, X. (2025) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2025.3543450 · Cited by 83
“Traditional fraud detection started with rule-based and statistical methods, where Bhattacharyya et al. [2] compared various data mining approaches and highlighted class imbalance challenges, while Bolton and Hand [18] introduced peer group analysis for identifying abnormal spending patterns.”
Credit Card Fraud Detection using autoencoder based clustering
Zamini, M., & Montazer, G. (2018) · International Symposium on Telecommunications · doi:10.1109/istel.2018.8661129 · Cited by 67
“In the same year [7] measured linear regression, random forest and support vector machines.”
Background
Fraud detection in big data using supervised and semi-supervised learning techniques
Melo-Acosta, G. E., Duitama, F., & Arias-Londoño, J. D. (2017) · IEEE Colombian Conference on Communications and Computing (COLCOM) · doi:10.1109/colcomcon.2017.8088206 · Cited by 66
“In [20] there was performed a comparison among SVM, RL and RF.”
Method
A financial statement fraud detection model based on hybrid data mining methods
Yao, J., Zhang, J., & Wang, L. (2018) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd.2018.8396167 · Cited by 65
“Meanwhile, other fraud problems like credit card fraud[8, 9], internal transaction fraud[10] and insurance fraud[11] have also been investigated.”
Background
Modeling and analysis of identity threat behaviors through text mining of identity theft stories
Zaeem, R. N., Manoharan, M., Yang, Y., & Barber, K. S. (2016) · Computers & Security · doi:10.1016/j.cose.2016.11.002 · Cited by 64
A Novel Approach for Credit Card Fraud Detection using Decision Tree and Random Forest Algorithms
Dileep, M. R., Navaneeth, A. V., & Abhishek, M. (2021) · International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) · doi:10.1109/icicv50876.2021.9388431 · Cited by 64
“S. Jha et al [9], implemented a system which supports in the detection of the scams or frauds in the field of the business by recording the transactions and there by building a model using data mining models.”
Method
An efficient fraud detection framework with credit card imbalanced data in financial services
El-Naby, A. A., Hemdan, E. E., & El‐Sayed, A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13434-6 · Cited by 61
“Credit card is considered to be the most common type of fraud since it takes much less time for an attacker to steal information, and most of the time, fraud is discovered a few days after it has occurred [9].”
Background
Facilitating User Authorization from Imbalanced Data Logs of Credit Cards Using Artificial Intelligence
Arora, V., Leekha, R. S., Lee, K., & Kataria, A. (2020) · Mobile Information Systems · doi:10.1155/2020/8885269 · Cited by 60
Integrating Machine Learning Algorithms With Quantum Annealing Solvers for Online Fraud Detection
Wang, H., Wang, W., Liu, Y., & Alidaee, B. (2022) · IEEE Access · doi:10.1109/access.2022.3190897 · Cited by 75
Enhancing Fraud Detection in Banking With Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud Prevention
Alarfaj, F. K., & Shahzadi, S. (2024) · IEEE Access · doi:10.1109/access.2024.3466288 · Cited by 60
Feature construction for fraudulent credit card cash-out detection
Wu, Y., Xu, Y. C., & Li, J. (2019) · Decision Support Systems · doi:10.1016/j.dss.2019.113155 · Cited by 61
Performance Analysis of Machine Learning Algorithms in Credit Cards Fraud Detection
Jain, V., Agrawal, M., & Kumar, A. (2020) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito48877.2020.9197762 · Cited by 59
Sequential credit card fraud detection: A joint deep neural network and probabilistic graphical model approach
Forough, J., & Momtazi, S. (2021) · Expert Systems · doi:10.1111/exsy.12795 · Cited by 63
“Support vector machines (SVM) (Bhattacharyya et al., 2011; R. C. Chen et al., 2004; Y. J. Chen et al., 2019), artificial neural networks (Aleskerov et al., 1997; Behera & Panigrahi, 2015; Patidar & Sharma, 2011; Razooqi et al., 2016; Zakaryazad & Duman, 2016), convolutional neural networks (Fu et…”
BackgroundMethodInfluential
Supply chain fraud prediction with machine learning and artificial intelligence
Lokanan, M. E., & Maddhesia, V. (2024) · International Journal of Production Research · doi:10.1080/00207543.2024.2361434 · Cited by 75
Detecting corporate misconduct through random forest in China’s construction industry
Wang, R., Asghari, V., Hsu, S., Lee, C.-J., & Chen, J. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.122266 · Cited by 57
Fraud prediction using machine learning: The case of investment advisors in Canada
Lokanan, M. E., & Sharma, K. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100269 · Cited by 65
Intelligent Financial Fraud Detection Practices: An Investigation
West, J., Bhattacharya, M., & Islam, M. R. (2014) · Security and Privacy in Communication Networks · doi:10.1007/978-3-319-23802-9_16 · Cited by 48
“For example, Bhattacharyya et al. made use of transaction amount, categorical values such as account number, transaction date, and currency, and aggregated properties like total transaction amount per day, and average amount spent at a single merchant [3].”
BackgroundMethodInfluential
Adaptive Credit Card Fraud Detection Techniques Based on Feature Selection Method
Singh, A., & Jain, A. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-6861-5_15 · Cited by 55
Managing a pool of rules for credit card fraud detection by a Game Theory based approach
Gianini, G., Fossi, L. G., Mio, C., Caelen, O., Brunie, L., & Damiani, E. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.08.028 · Cited by 55
Credit card fraud detection strategies with consumer incentives
Wang, D., Chen, B., & Chen, J. (2018) · Omega · doi:10.1016/j.omega.2018.07.001 · Cited by 54
Towards Explainable Machine Learning for Bank Churn Prediction Using Data Balancing and Ensemble-Based Methods
Tekouabou, S. C. K., Gherghina, Ş. C., Toulni, H., Mata, P. N., & Martins, J. M. (2022) · Mathematics · doi:10.3390/math10142379 · Cited by 60
“, and also data collected from external sources (such as social networks) to enrich the banking information management system [6,19].”
BackgroundMethod
Detection of Credit Card Fraud Transactions Using Machine Learning Algorithms and Neural Networks: A Comparative Study
Dighe, D., Patil, S., & Kokate, S. (2018) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea.2018.8697799 · Cited by 53
Evaluating the benefits of using proactive transformed-domain-based techniques in fraud detection tasks
Saia, R., & Carta, S. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.10.016 · Cited by 54
“The Random Forests approach represents one of the most common and powerful stateof-the-art techniques for data analysis, because in most of the cases it outperforms the other ones [51, 35, 52].”
Background
SCSGuard: Deep Scam Detection for Ethereum Smart Contracts
Hu, H., Bai, Q., & Xu, Y. (2022) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps54753.2022.9798296 · Cited by 53
Review the role of artificial intelligence in detecting and preventing financial fraud using natural language processing
Sood, P., Sharma, C. B., Nijjer, S., & Sakhuja, S. (2023) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-023-02043-7 · Cited by 51
“Earlier works have likewise emphasized the usefulness of algorithmic and computer-based methods for spotting fraud (Bhattacharyya et al. 2011; Ngai et al. 2011; Zareapoor et al. 2012).”
Background
FrauDetector
Tseng, V. S. M., Ying, J. J.-C., Huang, C.-W., Kao, Y., & Chen, K.-T. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2788623 · Cited by 50
A Bayesian approach for incorporating expert opinions into decision support systems: A case study of online consumer-satisfaction detection
Coussement, K., Benoit, D. F., & Antioco, M. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.07.006 · Cited by 49
“The literature on decision support systems highlights several application domains that have been significantly affected by this trend, including credit risk [2, 3], bankruptcy prediction [4], customer relationship management [5, 6], and fraud detection [7].”
Background
A graph-based, semi-supervised, credit card fraud detection system
Lebichot, B., Braun, F., Caelen, O., & Saerens, M. (2016) · Studies in computational intelligence · doi:10.1007/978-3-319-50901-3_57 · Cited by 51
“Finally, those 12 new features (plus transaction-related features, see Table 1) are fed to a random forest classification model, as this model proved to perform well for the problem at hand, predicting fraudulent transaction [3, 12].”
Method
A Comprehensive Review of AI’s Current Impact and Future Prospects in Cybersecurity
Siam, A. A., Alazab, M., Awajan, A. W., & Faruqui, N. (2025) · IEEE Access · doi:10.1109/access.2025.3528114 · Cited by 50
“This article discussed how data mining algorithms can be used to spot credit card fraud, the earliest employment of AI for cyber security, and further explains what AI in Cyber Defense is all about, as well as its capabilities and perils [25].”
A REVIEW ON DATA MINING IN BANKING SECTOR
Jayasree (2013) · American Journal of Applied Sciences · doi:10.3844/ajassp.2013.1160.1165 · Cited by 50
Detecting Malicious Ethereum Entities via Application of Machine Learning Classification
Poursafaei, F., Hamad, G. B., & Žilić, Ž. (2020) · Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS) · doi:10.1109/brains49436.2020.9223304 · Cited by 52
“Among the methods employed in anti-money laundering and fraud detection applications, RF and LR are two of the most widely used ones where the former is known for its high accuracy and the latter for its explainability [2], [11], [27], [28].”
BackgroundMethod
Semi-Supervised Learning For Imbalanced Classification Of Credit Card Transaction
Salazar, A., Safont, G., & Vergara, L. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489755 · Cited by 49
Web Application Attacks Detection Using Machine Learning Techniques
Betarte, G., Pardo, Á., & Martinez, R. (2018) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2018.00174 · Cited by 49
“Finally, we used Random Forest as it could be seen as a rule set generator (comparable to the OWASP CRS ) and it has been reported to produce good results in problems related to fraud detection [24].”
Method
Fraudulent Transaction Detection in Credit Card by Applying Ensemble Machine Learning techniques
Prusti, D., & Rath, S. K. (2019) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt45670.2019.8944867 · Cited by 49
Using machine learning methods to predict hepatic encephalopathy in cirrhotic patients with unbalanced data
Yang, H., Li, X., Cao, H., Cui, Y., Luo, Y., Liu, J., & Zhang, Y. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106420 · Cited by 48
A Dual Approach for Credit Card Fraud Detection using Neural Network and Data Mining Techniques
Sahu, A., Harshvardhan, G. M., & Gourisaria, M. K. (2020) · IEEE India Conference · doi:10.1109/indicon49873.2020.9342462 · Cited by 48
“They compared the results yielded by the different classifiers and found Random Forest to be the best performing model [7].”
BackgroundResult
Amaretto: An Active Learning Framework for Money Laundering Detection
Labanca, D., Primerano, L., Markland-Montgomery, M., Polino, M., Carminati, M., & Zanero, S. (2022) · IEEE Access · doi:10.1109/access.2022.3167699 · Cited by 57
Predicting credit risk on the basis of financial and non-financial variables and data mining
Khemakhem, S., & Boujelbène, Y. (2018) · Review of Accounting and Finance · doi:10.1108/raf-07-2017-0143 · Cited by 47
Fraud detection for job placement using hierarchical clusters-based deep neural networks
Kim, J., Kim, H., & Kim, H. (2019) · Applied Intelligence · doi:10.1007/s10489-019-01419-2 · Cited by 49
OMLML: a helpful opinion mining method based on lexicon and machine learning in social networks
Keyvanpour, M. R., Zandian, Z. K., & Heidarypanah, M. (2020) · Social Network Analysis and Mining · doi:10.1007/s13278-019-0622-6 · Cited by 47
“7 (Bhattacharyya et al. 2011) where TN is the number of the opinions with negative polarity which are labeled negative polarity correctly.”
BackgroundInfluential
How to identify early defaults in online lending: A cost-sensitive multi-layer learning framework
Li, Z., Zhang, J., Yao, X., & Kou, G. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106963 · Cited by 48
A Spatial–Temporal Gated Network for Credit Card Fraud Detection by Learning Transactional Representations
Xie, Y., Liu, G., Zhou, M., Wei, L., Zhu, H., Zhou, R., & Cao, L. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3335145 · Cited by 48
“This method was employed by several works to generate new features for model training [33], [34].”
Method
Learning Sequential Behavior Representations for Fraud Detection
Guo, J., Liu, G., Zuo, Y., & Wu, J. (2018) · Proceedings · doi:10.1109/icdm.2018.00028 · Cited by 49
“[19] compared the performance of support vector machines, random forests, and logistic regression for detecting fraud on a real-world dataset of international credit card transactions.”
Background
Incremental learning strategies for credit cards fraud detection
Lebichot, B., Paldino, G. M., Siblini, W., He-Guelton, L., Oblé, F., & Bontempi, G. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00258-0 · Cited by 50
An ensemble learning approach for anomaly detection in credit card data with imbalanced and overlapped classes
Islam, A., Uddin, M. A., Aryal, S., & Stea, G. (2023) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103618 · Cited by 45
A rule-based machine learning model for financial fraud detection
Islam, S., Haque, M. M., & Karim, A. N. M. R. (2023) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v14i1.pp759-771 · Cited by 45
“Financial fraud has far-reaching consequences for the industry, including supplying funds for illegal operations such as drug trafficking and organized crime [11].”
Background
AI and Cyber-Security: Enhancing threat detection and response with machine learning.
Katiyar, N., Tripathi, M. S., Kumar, M. P., Verma, M. S., Sahu, A. K., & Saxena, S. (2024) · doi:10.53555/kuey.v30i4.2377 · Cited by 60
Corporate residence fraud detection
de Fortuny, E. J., Stankova, M., Moeyersoms, J., Minnaert, B., Provost, F. J., & Martens, D. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623333 · Cited by 45
“Abuse of the tax system is a very costly fraud type [25], with estimates of losses going into the billions of euros (dollars, pounds) for the EU, US and UK governments.”
Background
Hybrid approaches for detecting credit card fraud
Kültür, Y., & Çağlayan, M. U. (2016) · Expert Systems · doi:10.1111/exsy.12191 · Cited by 99
Imbalanced classification in sparse and large behaviour datasets
Vanhoeyveld, J., & Martens, D. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-017-0517-y · Cited by 47
Application of GA Feature Selection on Naive Bayes, Random Forest and SVM for Credit Card Fraud Detection
Saheed, Y. K., Hambali, M. A., Arowolo, M. O., & Olasupo, Y. A. (2020) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa51403.2020.9317228 · Cited by 45
“[22] appraised random forests and SVM combined with logistic regression, with the effort of attaining a better detection of CCF.”
Method
Machine Learning Methods for Credit Card Fraud Detection: A Survey
Dastidar, K. G., Caelen, O., & Granitzer, M. (2024) · IEEE Access · doi:10.1109/access.2024.3487298 · Cited by 56
“Bhattacharya et al. [34] use a set of 16 derived attributes (as aggregates), primarily computing the average of amounts spent and the number of transactions in certain time periods, with the country and merchant features used as constraints.”
Background
A Comparative Study of Chebyshev Functional Link Artificial Neural Network, Multi-layer Perceptron and Decision Tree for Credit Card Fraud Detection
Mishra, M. K., & Dash, R. (2014) · doi:10.1109/icit.2014.25 · Cited by 44
“In [6] performance of support vector machine and random forest together has been compared with logistic regression with respect to different performance metrics like sensitivity, specificity, precision.”
Method
Fraud Detection in Mobile Payments Utilizing Process Behavior Analysis
Rieke, R., Zhdanova, M., Repp, J., Giot, R., & Gaber, C. (2013) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1109/ares.2013.87 · Cited by 44
“[22] and Delamaire et.al.”
A state of the art survey of data mining-based fraud detection and credit scoring
Zhou, X. S., Cheng, S., Zhu, M. S., Guo, C., Zhou, S., Xu, P., Xue, Z., & Zhang, W. (2018) · MATEC Web of Conferences · doi:10.1051/matecconf/201818903002 · Cited by 44
“Using real-life dataset of transactions from an international credit card operation, Bhattacharyya et al. [27] evaluates the performance of support vector machines and random forests, together with the well-known logistic regression models for credit fraud detection.”
Method
ReMEMBeR: Ranking Metric Embedding-Based Multicontextual Behavior Profiling for Online Banking Fraud Detection
Cui, J., Yan, C., & Wang, C. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3052950 · Cited by 44
“For supervised learning based fraud detection, methods such as artiﬁcial neural networks by Daliri [23], RFs by Pozzolo et al. [24], support vector machines (SVMs) by Bhattacharyya et al. [25], and logistic regression (LR) [26] are most widely used.”
Method
Inferring multi-stage risk for online consumer credit services: An integrated scheme using data augmentation and model enhancement
Zhou, J., Wang, C., Ren, F. Y., & Chen, G. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113611 · Cited by 44
Credit Card Fraud Detection in Card-Not-Present Transactions: Where to Invest?
Mekterović, I., Karan, M., Pintar, D., & Brkić, L. (2021) · Applied Sciences · doi:10.3390/app11156766 · Cited by 43
A Review of Machine Learning Applications for Credit Card Fraud Detection with A Case study
Faraji, Z. (2022) · SEISENSE Journal of Management · doi:10.33215/sjom.v5i1.770 · Cited by 43
“Still, SVM considered more fraudulent instances in the training data (Bhattacharyya, 2011).”
Fraud Detection by Monitoring Customer Behavior and Activities
Singh, P., & Singh, M. (2015) · International Journal of Computer Applications · doi:10.5120/19584-1340 · Cited by 42
A Heterogeneous Ensemble Learning Model Based on Data Distribution for Credit Card Fraud Detection
Xie, Y., Li, A., Gao, L., & Liu, Z. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/2531210 · Cited by 42
Peer-to-Peer Loan Fraud Detection: Constructing Features from Transaction Data
Xu, J., Chen, D., Chau, M., Li, L., & Zheng, H. (2022) · MIS Quarterly · doi:10.25300/misq/2022/16103 · Cited by 45
Artificial intelligence and machine learning in combating illegal financial operations: Bibliometric analysis
Lyeonov, S., Drašković, V., Kubaščíková, Z., & Fenyves, V. (2024) · Human Technology · doi:10.14254/1795-6889.2024.20-2.5 · Cited by 41
Foundations and Innovations in Data Fusion and Ensemble Learning for Effective Consensus
Du, K.-L., Zhang, R., Jiang, B., Zeng, J., & Lu, J. (2025) · Mathematics · doi:10.3390/math13040587 · Cited by 55
A Feature Extraction Method for Credit Card Fraud Detection
Xie, Y., Liu, G., Cao, R., Li, Z., Yan, C., & Jiang, C. (2019) · Intelligent Autonomous Systems · doi:10.1109/icoias.2019.00019 · Cited by 41
“by card number, then by transaction type, merchant group or other, followed by calculating the number of transactions or the total amount spent on those transactions [5], [6].”
Background
Credit Card Fraud Detection Technique by Applying Graph Database Model
Prusti, D., Das, D., & Rath, S. K. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-05682-9 · Cited by 45
“Bhattacharyya et al. have provided a list of data mining algorithms used till date for this purpose [19].”
Method

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
ORCID: Bhattacharyya, Siddhartha
Westland, J. Christopher
Venue: Decision Support Systems
Publisher: Elsevier BV
Published: 2011-02
Volume: 50
Issue: 3
Pages: 602-613
ISSN: 0167-9236 (ISSN-L)
DOI: 10.1016/j.dss.2010.08.008
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data mining for credit card fraud: A comparative study

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks