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

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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

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Manavadaria, M. S., Srinivas, T. A. S., Ahamed, S. K., Amshavalli, M. M., Nadaf, A. B., & Bhoopathy, V. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-7540-2.ch013 · Cited by 3
Enhancing Banking Fraud Detection: Role of Machine Learning and Deep Learning Methods
Manzoor, M. F., & Aslam, M. F. (2025) · Premier Journal of Artificial Intelligence · doi:10.70389/pjai.100014 · Cited by 3
CAD: Computer-Aided Detection of Pneumonia Using Convolutional Neural Networks (CNN)
Jain, M., Shah, A., Sharma, P. R., & Campisi, M. (2025) · International Journal of Computer Science and Mobile Computing · doi:10.47760/ijcsmc.2025.v14i05.005 · Cited by 4
GPT-LGBM: a ChatGPT-based integrated framework for credit scoring with textual and structured data
Yu, L., Bai, X., & Chen, Z. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02602-3 · Cited by 3
“Bhattacharyya et al. evaluated support vector machines (SVM), random forests (RF) and LR for credit card fraud prediction [26].”
Replay Attack: A Prevalent Pattern of Fraudulent Online Transactions
Jing, C., Wang, C., & Yan, C. (2018) · IEEE International Conference on Cyber Security and Cloud Computing / IEEE International Conference on Edge Computing and Scalable Cloud (CSCloud/EdgeCom) · doi:10.1109/cscloud/edgecom.2018.00022 · Cited by 3
“[15] evaluated two approaches, Support Vector Machines and Random Forests, on a real data set, and reached to the conclusion that Random Forests demonstrated overall better performance across performance measures.”
Method
An Approach to Automatically Extract Predictive Properties from Nominal Attributes in Relational Databases
Kassarnig, V., & Wotawa, F. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622359 · Cited by 3
“Its applications range from forecasting stock prices [1], [2], [3] to predicting customer behavior [4], [5]; from filtering email spam [6], [7], [8] to detecting credit card fraud [9], [10], [11].”
Background
Machine Learning Techniques for Credit Card Fraud Detection
Elhamid, H. E. M. A., Khalif, W., Roushdy, M. I., & Salem, A.-B. M. (2020) · Future Computing and Informatics Journal · doi:10.54623/fue.fcij.4.2.5 · Cited by 3
The Analysis of Big Financial Data Through Artificial Intelligence Methods
Özhan, E., & Uzun, E. (2021) · Accounting, finance, sustainability, governance & fraud · doi:10.1007/978-981-33-6811-8_4 · Cited by 3
A Novel GBT-Based Approach for Cross-Channel Fraud Detection on Real-World Banking Transactions
Dolu, U., & Sefer, E. (2022) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-08333-4_6 · Cited by 3
TeGraF
Reddy, S., Poduval, P., Chauhan, A. V. S., Singh, M., Verma, S., Singh, K., & Bhowmik, T. (2021) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3490354.3494383 · Cited by 3
“Broadly, these methods are applications of Logistic Regression [3], Support Vector Machines [3, 19], Bayesian Networks [20, 23], Hidden Markov Model [4], Gradient Boosted Trees [29, 30], and genetic algorithms [27] for detecting fraud transactions.”
Method
Purchase Pattern Based Anti-Fraud Framework in Online E-Commerce Platform Using Graph Neural Network
Wang, S., Liu, Y., Chu, Z., & Lin, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20500-2_9 · Cited by 3
Comparison of machine learning algorithms for credit card fraud transaction prediction
Wu, J. (2023) · Applied and Computational Engineering · doi:10.54254/2755-2721/6/20230934 · Cited by 3
Avaliação de Técnicas de Balanceamento de Dados na Detecção de Fraude em Transações Online de Cartão de Crédito
Cavalcanti, A., Brandão, D. N., Bezerra, E. A. D., & Coutinho, R. D. C. (2024) · Simpósio Brasileiro de Bancos de Dados (SBBD) · doi:10.5753/sbbd.2024.243462 · Cited by 3
Enhancing Financial Risk Management with Federated AI
Dhanawat, V., Shinde, V., Karande, V., & Singhal, K. (2024) · SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) · doi:10.1109/slaai-icai63667.2024.10844982 · Cited by 5
“Machine learning models, trained on vast but sensitive and imbalanced financial data, are central to this approach [7].”
A novel intelligent credit scoring method using MOPSO
Guo, Y. F., & Dong, C. J. (2017) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2017.7978359 · Cited by 3
Predicting Credit Card Delinquency: An Application of the Decision Tree Technique
Sun, T., & Vasarhelyi, M. A. (2019) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-78743-085-320191006 · Cited by 3
Detección de valores atípicos con técnicas de minería de datos y métodos estadísticos
Orellana, M., & Cedillo, P. (2020) · Enfoque UTE · doi:10.29019/enfoque.v11n1.584 · Cited by 3
Online Credit Card Fraud Analytics Using Machine Learning Techniques
Kumar, A., Anand, K., Jha, S., & Gupta, J. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5616-6_8 · Cited by 3
Self-citation
Credit Card Risk Detection based on Feature-Filter and Fraud Identification
Rtayli, N., & Enneya, N. (2019) · International Conference on Intelligent Computing in Data Sciences (ICDS) · doi:10.1109/icds47004.2019.8942373 · Cited by 3
“S could be more than $400 billion [4], while another study shows that United Kingdom insurers are out 1.”
Background
Micro-Level Incident Analysis using Spatial Association Rule Mining
Yoo, J. S., Park, S. J., & Raman, A. (2019) · IEEE International Conference on Big Knowledge (ICBK) · doi:10.1109/icbk.2019.00049 · Cited by 3
“[9] reviewed data mining techniques for credit card fraud.”
Background
Multi-Dimensional Fraud Detection Metrics in Business Processes and their Application
Omair, B., & Alturki, A. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110968 · Cited by 3
“Furthermore, the increase in fraud in recent years reflects the persistent nature of the issue [12].”
Background
Identification of Fraudulent Online Transactions and Protection: State-of-art Techniques
Gaurav, A., & Gupta, B. B. (2022) · ESIC Digital Economy and Innovation Journal · doi:10.55234/edeij-1-3-07 · Cited by 3
“These machine learning techniques use supervised (Bhattacharyya et al., 2011; Dhankhad et al., 2018), and unsupervised learning (Carminati et al., 2015) methods to detect fraudulent transactions (Bolton & Hand, 2002; Kou et al., 2004).”
Method
Detection of Web API Content Scraping : An Empirical Study of Machine Learning Algorithms
Jawad, D. (2017) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 3
Automating Software Development using Artificial IntelligencePreprint
Nagulapati, V., Rapelli, S. R., & Fiaidhi, J. (2020) · doi:10.36227/techrxiv.12089139.v1 · Cited by 3
“Bhattacharyya, Jha, Tharakunnel and Westland provide a detailed description of Random Forest (RF) and Support Vector Machine (SVM) [6] below: • Support Vector Machines – Based on a collection of features given, an SVM is used to predict what form of change will occur.”
BackgroundInfluential
Improving Crime Count Forecasts Using Twitter and Taxi DataPreprint
Vomfell, L., HHrdle, W. K., & Lessmann, S. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3131517 · Cited by 3
Imbalance Example-Dependent Cost Classification: A Bayesian Based MethodPreprint
Mediavilla, J., Lázaro, M., & Figueiras-Vidal, A. R. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4156578 · Cited by 3
Fraud Detection In Financial Transactions Through Data Science For Real-Time Monitoring And PreventionPreprint
Waliullah, M., & Alam, M. A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5051030 · Cited by 3
Credit card fraud detection using machine learning with integration of contextual knowledge
Lucas, Y. (2019) · theses.fr (ABES) · doi:10.70675/44c26b72z831fz4a87z8e51z91b3d0434fa3 · Cited by 3
Class Balancing for Fraud Detection in Point Of Sale Systems
Hines, C., & Youssef, A. (2019) · 2019 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata47090.2019.9006040 · Cited by 3
An analytical assessment of credit card fraud detection techniques: Supervised, Unsupervised, and Reinforcement Learning
Djama, A. D. (2025) · Herald of Dagestan State Technical University Technical Sciences · doi:10.21822/2073-6185-2024-51-4-23-32 · Cited by 2
A Machine Learning-Based Approach to Improve Fraud Detection using ISSO and PKRR
Panda, G., Ojha, R. K., & Dhal, S. K. (2024) · International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT) · doi:10.1109/ic-sit63503.2024.10862834 · Cited by 2
“Bhattacharyya et al. demonstrated the effectiveness of various supervised learning algorithms, including logistic regression and support vector machines (SVM), in detecting credit card fraud [1].”
Research on Loan Default Risk Prediction Methods Based on Graph Neural Networks
Chen, H., Li, S., Huidang, Z., Zhang, H., & Liang, Y. (2024) · International Symposium on Computer Science and Intelligent Control (ISCSIC) · doi:10.1109/iscsic64297.2024.00026 · Cited by 2
“With the evolution of machine learning technologies, an increasing number of practitioners are turning to machine learning methodologies, such as logistic regression, decision trees, and XGBoost [2-3], to address this challenge, yielding notable successes.”
Fraud Detection – A Hybrid Machine Learning Approach
Jena, S., Rajput, D., & Pathak, T. B. (2025) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit25112825 · Cited by 2
Interpretable Machine Learning-Based Fraud Detection Model and Knowledge Discovery in Financial Transactions
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Application of Data Analytics in Banking
Kumar, J., Ahuja, A., Arora, N., Saini, K., & Sindhwani, N. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-92916-8_22 · Cited by 2
Detecting credit card fraud from transaction data streams with incremental time-aware liquid recurrent network
Cao, R., Mao, M., Wang, X., & Fang, X. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114729 · Cited by 4
Hybrid Architecture Using Deep Auto-Encoder and Clustering Methods to Identify Diverse Transaction Anomalies
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“Unlike general financial anomalies, credit card fraud detection involves identifying subtle behavioral deviations in individual transactions, such as irregularities in transaction amount, frequency, location, and usage patterns [1].”
A Hybrid Approach Using Maximum Entropy and Bayesian Learning for Detecting Delinquency in Financial Industry
Kumar, D., & Arora, S. (2016) · International Journal of Knowledge-Based Organizations · doi:10.4018/ijkbo.2016010105 · Cited by 2
Retracted: Credit Card Fraud Detection Using Local Outlier Factor & Isolation Forest Algorithms: A Complete Analysis
Ghevariya, R., Desai, R., Bohara, M. H., & Garg, D. (2021) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca52323.2021.9675971 · Cited by 2
Retracted
Credit card fraud detection based on self-paced ensemble neural network
Zhou, W. H., Xue, X., & Xu, Y. (2022) · International Conference on Information Technology and Computer Communications (ITCC) · doi:10.1145/3548636.3548650 · Cited by 2
“Bhattacharyya et al. (2011) have based on real data from an international credit card business, and combined with logistic regression methods, two support vector machine and random forest advanced data mining methods were evaluated.”
Method
Financial Markets Fraud Detection and Prevention using Kernel Adatron Algorithm with Machine Learning
Yadav, O. P., Sahu, M. R. K., & Soni, M. A. (2022) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-7581 · Cited by 2
Performance Analysis of Algorithms for Credit Card Fraud Detection
Rawat, A., Aswal, S. S., Gupta, S., Singh, A. P., Singh, S. P., & Purohit, K. C. (2024) · International Conference on Disruptive Technologies (ICDT) · doi:10.1109/icdt61202.2024.10489771 · Cited by 2
“A hybrid model [5] combining support vector machines (SVM) and KNN for credit card fraud detection and achieved an accuracy of 95%.”
Method
Role of Artificial Intelligence and Machine Learning Algorithms in Detecting Financial Frauds
Dar, B. I., & Jaiswal, S. (2024) · Advances in finance, accounting, and economics book series · doi:10.4018/979-8-3693-3633-5.ch011 · Cited by 4
Optimizing Machine Learning Data Pre-Processing for Financial Fraud Detection
Bower, M., Godasu, R., Nyakundi, N. B. N., & Reynolds, S. (2024) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit60633.2024.10609910 · Cited by 2
Fraud Detection Based on Credit Review Texts with Dual Channel Memory Networks
Wang, Y., Lian, D., & Chen, E. (2024) · Applied Artificial Intelligence · doi:10.1080/08839514.2024.2385854 · Cited by 2
“Therefore, machine learning methods have been widely used in financial fraud detection (Carneiro, FigueiraI, and Costa 2017), with typical examples including support vector machines, random forests, XGboost and other voting methods(Hattacharyya et al. 2011; Kumar et al. 2019).”
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Gao, M., Chen, S., Gao, Y., Zhang, Z., Chen, Y., Li, Y., Ye, Q., Wang, X., & Chen, Y. (2024) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2300291 · Cited by 2
“…(e.g., account takeover (Tao et al., 2018; Doerﬂer et al., 2019; Kawase et al., 2019; Mirian et al., 2019; Thomas et al., 2021), credit card fraud (Bhattacharyya et al., 2011; Branco et al., 2020; Cheng et al., 2020), and identity theft (Bilge et al., 2009; Tao et al., 2018; Zou et al., 2020;…”
Generative AI in Fintech: Advancing Risk Assessment and Fraud Detection in Digital Payment Technologies
Mohammad, R. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.64110 · Cited by 2
Fraud Detection Based on Graph Neural Network
Luo, Y., & Wang, G. (2024) · International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC) · doi:10.1109/raiic61787.2024.10671201 · Cited by 2
A Comprehensive Study: Evaluating Machine Learning Algorithms with Credit Card Transaction Data
Basak, S., & Shanto, S. Q. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3937-0_49 · Cited by 2
An Application of Logistic Regression in Bank Lending Prediction: A Machine Learning Perspective
Zhang, Z. (2024) · Highlights in Business Economics and Management · doi:10.54097/20qcm204 · Cited by 2
“These models analyze patterns in transaction data to flag suspicious activities for further investigation [12].”
Neural Networks for Financial Fraud Detection: Enhancing Security with Scalable Machine Learning and Deep learning Solutions
Nuthan, M. C., Raviprakash, B., Rama Moorthy, H., Praveen, N., & S, K. R. N. (2025) · International Conference on Artificial Intelligence and Data Engineering (AIDE) · doi:10.1109/aide64228.2025.10987357 · Cited by 2
“Traditional ML models, which achieve 75% accuracy, rely on predefined rules and patterns from historical data to detect fraud [13].”
A multi-modal CNN framework for integrating medical imaging for COVID-19 Diagnosis
Jain, M., & Shah, A. (2020) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2020.8.3.0418 · Cited by 2
ATAD-Net: An Adaptive Deep Learning Framework for Real-Time Financial Fraud Detection
Abd-Elmegid, L. A., Abrar, M. Y. B. T., & Ismaeel, A. A. K. (2025) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2025.52225 · Cited by 2
AI-Powered Detection of Fraudulent Job Listings using DistilBERT and XGBoost
Shetty, K., Shetty, S. K., Sanjana, B., Murthy, A. K., Harshitha, G. M., & Prathwini, P. (2025) · International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) · doi:10.1109/icicv64824.2025.11085449 · Cited by 2
“Machine learning methods have also been studied extensively in other fraud offenses, i.e., credit card fraud, to which data-driven solutions are a solution [9].”
A Machine Learning Approach for Credit Card Fraud Detection using Feature Engineering and Ensemble Models
Gupta, I., Kumar, R. R., Muduli, D., Mishra, S., & Parija, S. (2025) · International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3) · doi:10.1109/isac364032.2025.11156887 · Cited by 2
Credit Card Fraud Detection Techniques Under IoT Environment: A Survey
Kanchana, M., Naresh, R., Deepa, N., Pandiaraja, P., & Stephan, T. (2022) · Springer eBooks · doi:10.1007/978-3-030-86749-2_8 · Cited by 2
Credit Card Fraud Detection Using Machine Learning Model
Swarnalatha, K. S., Shah, K. K., Kumar, K., Patel, K. K., & Sah, A. R. (2022) · IEEE Mysore Sub Section International Conference (MysuruCon) · doi:10.1109/mysurucon55714.2022.9972647 · Cited by 2
A Unified Statistical Framework for Evaluating Predictive Methods
Urbanke, P., Kranz, J. J., & Kolbe, L. M. (2014) · Journal of the Association for Information Systems · Cited by 2
Change-Point Detection in a Sequence of Bags-of-Data
Koshijima, K., Hino, H., & Murata, N. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2426693 · Cited by 2
“This technique, which is often referred to as change-point detection, can be directly applied to various situations such as intrusion detection in computer networks [3], fault detection in machines [4], and fraud detection in credit card use [5].”
Method
The surveying of the effect of the incentive pays to the degree of the attraction of resources in bank branches through the data mining technique
Massrur, R., Nejad, A. F., & Sami, A. (2014) · Iranian Conference on Intelligent Systems (ICIS) · doi:10.1109/iraniancis.2014.6802590 · Cited by 2
A method of improving credit evaluation with support vector machines
Chen, J., & Xu, L. (2015) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2015.7378060 · Cited by 2
“Furthermore, some combination of features may have more important influence to the effect of credit scoring than a single feature [13, 14].”
Background
Industry Paper: The Uncertain Case of Credit Card Fraud Detection
Correia, I., Fournier, F., & Skarbovsky, I. (2015) · Cited by 2
Survey on Credit Card Fraud Detection using Recurrent Attributes
Jain, N., & Khan, V. (2017) · IJARCCE · doi:10.17148/ijarcce.2017.6175 · Cited by 2
“Based on machine learning techniques, different detection systems have been successfully used for this problem, in particular: neural networks [3], Bayesian learning [4], artificial immune systems, hybrid models, support vector machines, peer group analysis, online learning and social network…”
Method
Predicting Online Returns
Smriti, K. (2017) · Advances in logistics, operations, and management science book series · doi:10.4018/978-1-5225-3056-5.ch010 · Cited by 2
Secure Framework for E-Commerce Applications in Cloud Environment
Shrivastava, S., & Pateriya, R. K. (2018) · Advances in e-business research series · doi:10.4018/978-1-5225-3646-8.ch004 · Cited by 2
Evolutionary propositionalization of multi-relational data
Kassarnig, V., & Wotawa, F. (2018) · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · doi:10.18293/seke2018-136 · Cited by 2
Latent Variable Models for Integrated Analysis of Credit and Point Usage History Data on Rewards Credit Card System
Shimizu, R., Yamashita, H., Ueda, M., Tanaka, R., Tachibana, T., & Goto, M. (2020) · International Business Research · doi:10.5539/ibr.v13n3p106 · Cited by 2
Quantifying Family Business Owners' Attitudes towards Succession: Delving Deeper by Random Forests
Kilic, A., & Filizöz, B. (2020) · Central European Business Review · doi:10.18267/j.cebr.235 · Cited by 2
“RF was shown to be robust to the complex and dynamic data structures (Bhattacharyya et al., 2011), and thus, how important to maximize the benefit from its use in a better understanding of causal relationships.”
Background
Machine Learning en la detección de fraudes de comercio electrónico aplicado a los servicios bancarios
Alvarez, F. (2020) · Ciencia y Tecnología · doi:10.18682/cyt.vi0.4310 · Cited by 2
An Evolutionary Framework for Real-Time Fraudulent Credit Detection
Mohebali, B., Karbaschi, G., Tahmassebi, A., Meyer‐Baese, A., & Gandomi, A. H. (2021) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec45853.2021.9504729 · Cited by 2
Hybrid Optimization and Deep Learning for Detecting Fraud Transactions in the Bank
Kolli, C. S., & Tatavarthi, U. D. (2022) · International Journal of Information Security and Privacy · doi:10.4018/ijisp.300323 · Cited by 2
Credit Card Fraud Intelligent Detection Based on Machine Learning
Mu, D. (2022) · 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) · doi:10.1109/eebda53927.2022.9744875 · Cited by 2
“However, accuracy is not properly performed in unbalanced datasets, so other evaluation metrics are needed[9].”
Background
A Proactive Time-frame Convolution Vector (TFCV) Technique to Detect Frauds Attempts in e-Commerce Transactions
Saia, R., Boratto, L., & Carta, S. (2015) · International Journal of e-Education e-Business e-Management and e-Learning · doi:10.17706/ijeeee.2015.5.4.229-236 · Cited by 2
Unsupervised Fraud Transaction Detection on Dynamic Attributed Networks
Hou, Y., Wang, D., Hu, B., Zhuang, R., Zhang, Z., Zhou, J., Zhao, F., Kang, Y., & Qiao, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30678-5_41 · Cited by 2
Enhancing Credit Card Fraud Detection through LSTM-Based Sequential Analysis with Early Stopping
Ida, S. J., Balasubadra, K., Skandarsini, R. R., & Lakshmi Narayanaa, T. (2024) · International Conference on Networking and Communications (ICNWC) · doi:10.1109/icnwc60771.2024.10537550 · Cited by 2
“Bhattacharyya et al.'s study of random forests, Support vector machines and logistic regression are the predictive power of integrated algorithms for credit card fraud detection is demonstrated in [5].”
Background
Towards a model of factors affecting resistance to using multi-method authentication systems in higher-education environments
Marnell, J. W., & Levy, Y. M. (2014) · NSUWorks (Nova Southeastern University) · Cited by 2
Adaptive subspace sampling for class imbalance processing
Liu, Y., Pal, N. R., Wu, S.-L., Hsieh, T.-Y., & Lin, C. (2016) · International conference on Fuzzy Theory and Its Applications · doi:10.1109/ifuzzy.2016.8004947 · Cited by 2
Datamining for Fraud Detecting, State of the Art
Bouazza, I., Ameur, E. B., & Ameur, F. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-11928-7_17 · Cited by 2
Credit card cash‐out as informal financing: Evidence from China
Ma, Y., Xue, Y., & Yang, J. (2019) · Australian Economic Papers · doi:10.1111/1467-8454.12153 · Cited by 2
Predictive Analytics for Fraud Detection
(2015) · doi:10.1002/9781119146841.ch4 · Cited by 2
The Terminator of Social Welfare? The Economic Consequences of Algorithmic DiscriminationPreprint
Bauer, K., Pfeuffer, N., Abdel-Karim, B. M., Hinz, O. F., & Kosfeld, M. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3675313 · Cited by 3
Fraud Detection by Using Deep Learning in Mining the Information Technology for Artificial and Business IntelligencePreprint
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DETECTION OF CREDIT CARD FRAUDS WITH MACHINE LEARNING SOLUTIONS: AN EXPERIMENTAL APPROACH
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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

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