Data mining for credit card fraud: a comparative study

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works

Applied logistic regression

(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305 · see citing works

Statistical Learning Theory

Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236 · see citing works

Applied Logistic Regression

Hosmer, D. W., & Lemeshow, S. (2000) · doi:10.1002/0471722146 · Cited by 20,302 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

The Handbook of brain theory and neural networks

(1996) · Choice Reviews Online · doi:10.5860/choice.33-4484 · Cited by 3,913 · see citing works

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 3,358 · see citing works

Random forest classifier for remote sensing classification

Pal, M. (2005) · International Journal of Remote Sensing · doi:10.1080/01431160412331269698 · Cited by 3,293 · see citing works

Combining Pattern Classifiers

Kuncheva, L. I. (2004) · Wiley eBooks · doi:10.1002/0471660264 · Cited by 3,007 · see citing works

Ensemble based systems in decision making

Robi Polikar, A. (2006) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2006.1688199 · Cited by 2,960 · see citing works

An empirical comparison of supervised learning algorithms

Caruana, R., & Niculescu-Mizil, A. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143865 · Cited by 2,780 · see citing works

Editorial

Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007733 · Cited by 2,074 · see citing works

The Handbook of brain theory and neural networks

(2003) · Choice Reviews Online · doi:10.5860/choice.41-0300 · Cited by 1,540 · see citing works

Statistical Fraud Detection: A Review

Bolton, R. J., & Hand, D. J. (2002) · Statistical Science · doi:10.1214/ss/1042727940 · Cited by 1,531 · see citing works

Experimental perspectives on learning from imbalanced data

Hulse, J. V., Khoshgoftaar, T. M., & Napolitano, A. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273614 · Cited by 785 · see citing works

The support vector machine under test

MEYER, D. A., Leisch, F., & Hornik, K. (2003) · Neurocomputing · doi:10.1016/s0925-2312(03)00431-4 · Cited by 773 · see citing works

Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions

Provost, F. J., & Fawcett, T. E. (1997) · Cited by 723 · see citing works

A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

Statnikov, A., Wang, L., & Aliferis, C. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-319 · Cited by 684 · see citing works

Random-Forests-Based Network Intrusion Detection Systems

Zhang, J., Zulkernine, M., & Haque, A. (2008) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2008.923876 · Cited by 587 · see citing works

Misclassification of the dependent variable in a discrete-response setting

Hausman, J. A., Abrevaya, J., & Scott-Morton, F. M. (1998) · Journal of Econometrics · doi:10.1016/s0304-4076(98)00015-3 · Cited by 563 · see citing works

Distributed data mining in credit card fraud detection

Chan, P. K., Fan, W., Prodromidis, A. L., & Stolfo, S. J. (1999) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.809570 · Cited by 540 · see citing works

Credit Card Fraud Detection Using Hidden Markov Model

Srivastava, A., Kundu, A., Sural, S., & Majumdar, A. K. (2008) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2007.70228 · Cited by 535 · see citing works

An empirical evaluation of supervised learning in high dimensions

Caruana, R., Karampatziakis, N., & Yessenalina, A. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390169 · Cited by 531 · see citing works

AUC: a statistically consistent and more discriminating measure than accuracy

Ling, C. X., Huang, J., & Zhang, H. (2003) · Cited by 501 · see citing works

Survey of fraud detection techniques

Kou, Y., Lu, C., Sirwongwattana, S., & Huang, Y.-P. (2004) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc.2004.1297040 · Cited by 444 · see citing works

Credit card fraud detection with a neural-network

Ghosh, & Reilly (1994) · doi:10.1109/hicss.1994.323314 · Cited by 436 · see citing works

Customer churn prediction using improved balanced random forests

Xie, Y., Li, X., Ngai, E. W. T., & Ying, W. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.06.121 · Cited by 412 · see citing works

CARDWATCH: a neural network based database mining system for credit card fraud detection

Aleskerov, E., Freisleben, B., & Rao, B. D. (2002) · doi:10.1109/cifer.1997.618940 · Cited by 401 · see citing works

An extensive comparison of recent classification tools applied to microarray data

Lee, J. W., Lee, J. B., Park, M., & Song, S. H. (2004) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.03.017 · Cited by 394 · see citing works

An Empirical Study of Learning from Imbalanced Data Using Random Forest

Khoshgoftaar, T. M., Golawala, M., & Hulse, J. V. (2007) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2007.46 · Cited by 390 · see citing works

Neural data mining for credit card fraud detection

Brause, R. W., Langsdorf, T., & Hepp, M. (2003) · doi:10.1109/tai.1999.809773 · Cited by 364 · see citing works

Unsupervised Profiling Methods for Fraud Detection

Bolton, R. J., & Hand, D. J. (2002) · Cited by 352 · see citing works

Transaction aggregation as a strategy for credit card fraud detection

Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-008-0116-z · Cited by 337 · see citing works

Predicting customer retention and profitability by using random forests and regression forests techniques

Larivière, B., & den Poel, D. V. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.04.043 · Cited by 313 · see citing works

Logistic Regression When the Outcome Is Measured with Uncertainty

Magder, L. S., & Hughes, J. P. (1997) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a009251 · Cited by 271 · see citing works

Neural fraud detection in credit card operations

Dorronsoro, J. R., Ginel, F., Sgnchez, C., & Cruz, C. S. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.595879 · Cited by 263 · see citing works

A New Feature Transformation Method Based on Rotation for Speaker Identification

Kim, M., & Yu, H.-J. (2007) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2007.49 · Cited by 231 · see citing works

Detection of Automobile Insurance Fraud With Discrete Choice Models and Misclassified Claims

Artı́s, M., Ayuso, M., & Guillén, M. (2002) · Journal of Risk & Insurance · doi:10.1111/1539-6975.00022 · Cited by 194 · see citing works

Proceedings of the Third International Conference on Knowledge Discovery and Data Mining

Heckerman, D. E., Mannila, H., Pregibon, D., & Uthurusamy, R. (1997) · Medical Entomology and Zoology · Cited by 178 · see citing works

Modeling Discrete Choice with Response Error: Food Stamp Participation

Bollinger, C. R., & David, M. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10474038 · Cited by 176 · see citing works

Multiple algorithms for fraud detection

Wheeler, R. M., & Aitken, S. (2000) · Knowledge-Based Systems · doi:10.1016/s0950-7051(00)00050-2 · Cited by 125 · see citing works

Unemployment Benefits and Labor Market Transitions: A Multinomial Logit Model with Errors in Classification

Poterba, J. M., & Summers, L. H. (1995) · The Review of Economics and Statistics · doi:10.2307/2109860 · Cited by 124 · see citing works

A NEW BINARY SUPPORT VECTOR SYSTEM FOR INCREASING DETECTION RATE OF CREDIT CARD FRAUD

CHEN, R.-C., Chen, T.-S., & Lin, C.-C. (2006) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001406004624 · Cited by 97 · see citing works

Fraud Detection Using a Multinomial Logit Model With Missing Information

Caudill, S. B., Ayuso, M., & Guillén, M. (2005) · Journal of Risk & Insurance · doi:10.1111/j.1539-6975.2005.00137.x · Cited by 87 · see citing works

Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence

(2003) · doi:10.1109/tai.2003 · Cited by 78 · see citing works

Binary choice models for rare events data: a crop insurance fraud application

Jin, Y., Rejesus, R. M., & Little, B. B. (2005) · Applied Economics · doi:10.1080/0003684042000337433 · Cited by 44 · see citing works

Modeling Discrete Choice With Response Error: Food Stamp Participation

Bollinger, C. R., & David, M. (1997) · Journal of the American Statistical Association · doi:10.2307/2965547 · Cited by 30 · see citing works

Credit card fraud detection using artificial neural networks tuned by genetic algorithms

Paasch, C. A. W. (2008) · doi:10.14711/thesis-b1023238 · Cited by 28 · see citing works

Credit Card Fraud Funds Terrorism

Everett, C. (2003) · Computer Fraud & Security · doi:10.1016/s1361-3723(03)05001-2 · Cited by 11 · see citing works

Explaining prediction models and individual predictions with feature contributions

Štrumbelj, E., & Kononenko, I. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-013-0679-x · Cited by 2,286 · see citing works

A comprehensive survey on support vector machine classification: Applications, challenges and trends

Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.10.118 · Cited by 2,381 · see citing works

A Unifying Review of Deep and Shallow Anomaly Detection

Ruff, L., Kauffmann, J., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3052449 · Cited by 1,080 · see citing works

A survey of multiple classifier systems as hybrid systems

Woźniak, M., Graña, M., & Corchado, E. (2013) · Information Fusion · doi:10.1016/j.inffus.2013.04.006 · Cited by 909 · see citing works

Fraud detection system: A survey

Abdallah, A., Maarof, M. A., & Zainal, A. B. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.04.007 · Cited by 722 · see citing works

Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy

Pozzolo, A. D., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2736643 · Cited by 765 · see citing works

Credit card fraud detection using machine learning techniques: A comparative analysis

Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017) · International Conference on Computing Networking and Informatics (ICCNI) · doi:10.1109/iccni.2017.8123782 · Cited by 644 · see citing works

Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances

Hilal, W., Gadsden, S. A., & Yawney, J. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116429 · Cited by 717 · see citing works

Using generative adversarial networks for improving classification effectiveness in credit card fraud detection

Fiore, U., Santis, A. D., Perla, F., Zanetti, P., & Palmieri, F. (2017) · Information Sciences · doi:10.1016/j.ins.2017.12.030 · Cited by 607 · see citing works

Intelligent financial fraud detection: A comprehensive review

West, J., & Bhattacharya, M. (2015) · Computers & Security · doi:10.1016/j.cose.2015.09.005 · Cited by 599 · see citing works

Unique in the shopping mall: On the reidentifiability of credit card metadata

de Montjoye, Y.-A., Radaelli, L., Singh, V. K., & Pentland, A. “. (2015) · Science · doi:10.1126/science.1256297 · Cited by 570 · see citing works

Sequence classification for credit-card fraud detection

Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.-É., He-Guelton, L., & Caelen, O. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.01.037 · Cited by 593 · see citing works

A Systematic Review on Imbalanced Data Challenges in Machine Learning

Kaur, H., Pannu, H. S., & Malhi, A. K. (2019) · ACM Computing Surveys · doi:10.1145/3343440 · Cited by 492 · see citing works

Credit Card Fraud Detection Using AdaBoost and Majority Voting

Randhawa, K. K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018) · IEEE Access · doi:10.1109/access.2018.2806420 · Cited by 488 · see citing works

Combining unsupervised and supervised learning in credit card fraud detection

Carcillo, F., Borgne, Y. L., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.042 · Cited by 539 · see citing works

A Semi-Supervised Graph Attentive Network for Financial Fraud Detection

Wang, D., Lin, J., Cui, P., Jia, Q., Wang, Z., Fang, Y., Yu, Q., Zhou, J., Yang, S., & Qi, Y. (2019) · Proceedings · doi:10.1109/icdm.2019.00070 · Cited by 483 · see citing works

Feature engineering strategies for credit card fraud detection

Correa Bahnsen, A., Aouada, D., Stojanovic, A., & Ottersten, B. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.12.030 · Cited by 451 · see citing works

An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine

Taha, A. A., & Malebary, S. J. (2020) · IEEE Access · doi:10.1109/access.2020.2971354 · Cited by 449 · see citing works

Fraud detection: A systematic literature review of graph-based anomaly detection approaches

Pourhabibi, T., Ong, K., Kam, B., & Boo, Y. L. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113303 · Cited by 471 · see citing works

Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review

Ali, A., Razak, S. B. A., Othman, S. H., Eisa, T. A. E., Al-Dhaqm, A., Nasser, M., Elhassan, T. A. M., Elshafie, H., & Saif, A. (2022) · Applied Sciences · doi:10.3390/app12199637 · Cited by 433 · see citing works

Random forest for credit card fraud detection

Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S., & Jiang, C. (2018) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc.2018.8361343 · Cited by 380 · see citing works

Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019

Al-Hashedi, K. G., & Magalingam, P. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100402 · Cited by 378 · see citing works

APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions

Vlasselaer, V. V., Bravo, C., Caelen, O., Eliassi‐Rad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.04.013 · Cited by 368 · see citing works

An Experimental Study With Imbalanced Classification Approaches for Credit Card Fraud Detection

Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M.-S., & Zeineddine, H. (2019) · IEEE Access · doi:10.1109/access.2019.2927266 · Cited by 341 · see citing works

Mining corporate annual reports for intelligent detection of financial statement fraud – A comparative study of machine learning methods

Hájek, P., & Henriques, R. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.05.001 · Cited by 338 · see citing works

Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud

Wang, Y., & Xu, W. X. (2017) · Decision Support Systems · doi:10.1016/j.dss.2017.11.001 · Cited by 328 · see citing works

Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities

Truong, H. M. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.02.014 · Cited by 531 · see citing works

Credit Card Fraud Detection Using Convolutional Neural Networks

Fu, K., Cheng, D., Tu, Y., & Zhang, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46675-0_53 · Cited by 317 · see citing works

Deep learning detecting fraud in credit card transactions

Roy, A., Sun, J., Mahoney, R., Alonzi, L. P., Adams, S., & Beling, P. A. (2018) · Systems and Information Engineering Design Symposium · doi:10.1109/sieds.2018.8374722 · Cited by 312 · see citing works

Getting value from Business Intelligence systems: A review and research agenda

Trieu, V.-H. (2016) · Decision Support Systems · doi:10.1016/j.dss.2016.09.019 · Cited by 324 · see citing works

HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture

Zhang, X., Han, Y., Xu, W. X., & Wang, Q. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.023 · Cited by 285 · see citing works

SCARFF : A scalable framework for streaming credit card fraud detection with spark

Carcillo, F., Pozzolo, A. D., Borgne, Y. L., Caelen, O., Mazzer, Y., & Bontempi, G. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.09.005 · Cited by 272 · see citing works

Data Mining for Detecting Bitcoin Ponzi Schemes

Bartoletti, M., Pes, B., & Serusi, S. (2018) · Crypto Valley Conference on Blockchain Technology (CVCBT) · doi:10.1109/cvcbt.2018.00014 · Cited by 264 · see citing works

Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review

Chen, Z., Khoa, L. D. V., Teoh, E. N., Nazir, A., Karuppiah, E. K., & Lam, K. S. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-017-1144-z · Cited by 254 · see citing works

A data mining based system for credit-card fraud detection in e-tail

Carneiro, N., Figueira, G., & Costa, M. A. (2017) · Decision Support Systems · doi:10.1016/j.dss.2017.01.002 · Cited by 253 · see citing works

Phishing Detection: Analysis of Visual Similarity Based Approaches

Jain, A. K., & Gupta, B. B. (2017) · Security and Communication Networks · doi:10.1155/2017/5421046 · Cited by 232 · see citing works

Big Data and Service Operations

Cohen, M. C. (2017) · Production and Operations Management · doi:10.1111/poms.12832 · Cited by 232 · see citing works

Ensemble of deep sequential models for credit card fraud detection

Forough, J., & Momtazi, S. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106883 · Cited by 231 · see citing works

Example-dependent cost-sensitive decision trees

Bahnsen, A. C., Aouada, D., & Ottersten, B. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.04.042 · Cited by 226 · see citing works

The determinants of crowdfunding success: A semantic text analytics approach

Hui, Y., Lau, R. Y. K., & Xu, W. X. (2016) · Decision Support Systems · doi:10.1016/j.dss.2016.08.001 · Cited by 222 · see citing works

Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains

Pes, B. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04082-3 · Cited by 226 · see citing works

Graph Neural Network for Fraud Detection via Spatial-Temporal Attention

Cheng, D., Wang, X., Zhang, Y., & Zhang, L. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3025588 · Cited by 231 · see citing works

Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series

Pelletier, C., Valero, S., Inglada, J., Champion, N., Marais-Sicre, C., & Dedieu, G. (2017) · Remote Sensing · doi:10.3390/rs9020173 · Cited by 227 · see citing works

Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: A Comparative Study

Dhankhad, S., Mohammed, E. A., & Far, B. H. (2018) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2018.00025 · Cited by 213 · see citing works

Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization

Rtayli, N., & Enneya, N. (2020) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2020.102596 · Cited by 213 · see citing works

Predictive analysis and modelling football results using machine learning approach for English Premier League

Baboota, R., & Kaur, H. (2018) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2018.01.003 · Cited by 200 · see citing works

Intelligent financial fraud detection practices in post-pandemic era

Zhu, X., Ao, X., Qin, Z., Chang, Y., Liu, Y., He, Q., & Li, J. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100176 · Cited by 219 · see citing works

A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM)

Erener, A., Mutlu, A., & Düzgün, Ş. (2015) · Engineering Geology · doi:10.1016/j.enggeo.2015.09.007 · Cited by 197 · see citing works

A deep learning approach for credit scoring using credit default swaps

Luo, C., Wu, D. D., & Wu, D. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.12.002 · Cited by 196 · see citing works

Zero-day malware detection based on supervised learning algorithms of API call signatures

Alazab, M., Venkatraman, S., Watters, P., & Alazab, M. (2011) · FedUni ResearchOnline (Federation University Australia) · Cited by 194 · see citing works

Enhanced credit card fraud detection based on attention mechanism and LSTM deep model

Benchaji, I., Douzi, S., Ouahidi, B. E., & Jaafari, J. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00541-8 · Cited by 222 · see citing works

The analytics paradigm in business research

Delen, D., & Zolbanin, H. M. (2018) · Journal of Business Research · doi:10.1016/j.jbusres.2018.05.013 · Cited by 197 · see citing works

A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection

Fanai, H., & Abbasimehr, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119562 · Cited by 184 · see citing works

Cost Sensitive Credit Card Fraud Detection Using Bayes Minimum Risk

Bahnsen, A. C., Stojanović, A., Aouada, D., & Ottersten, B. (2013) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2013.68 · Cited by 180 · see citing works

Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection

Awosika, T., Shukla, R. M., & Pranggono, B. (2024) · IEEE Access · doi:10.1109/access.2024.3394528 · Cited by 234 · see citing works

Performance of machine learning techniques in the detection of financial frauds

Sadgali, I., Sael, N., & Benabbou, F. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.01.007 · Cited by 178 · see citing works

Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison

Khatri, S., Arora, A., & Agrawal, A. P. (2020) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence47617.2020.9057851 · Cited by 174 · see citing works

Data engineering for fraud detection

Baesens, B., Höppner, S., & Verdonck, T. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113492 · Cited by 170 · see citing works

Unsupervised learning for robust Bitcoin fraud detection

Patrick, M., Marivate, V. N., & Twala, B. (2016) · Information Security for South Africa · doi:10.1109/issa.2016.7802939 · Cited by 165 · see citing works

Comprehensive review of text-mining applications in finance

Gupta, A., Dengre, V., Kheruwala, H. A., & Shah, M. (2020) · Financial Innovation · doi:10.1186/s40854-020-00205-1 · Cited by 164 · see citing works

Seoul's Wi-Fi hotspots: Wi-Fi access points as an indicator of urban vitality

Kim, Y.-L. (2018) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2018.06.004 · Cited by 168 · see citing works

An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment: A Survey and Implementation

Choi, D., & Lee, K. (2018) · Security and Communication Networks · doi:10.1155/2018/5483472 · Cited by 161 · see citing works

An intelligent payment card fraud detection system

Seera, M., Lim, C. P., Kumar, A., Dhamotharan, L., & Tan, K. H. (2021) · Annals of Operations Research · doi:10.1007/s10479-021-04149-2 · Cited by 161 · see citing works

Ensemble classification based on supervised clustering for credit scoring

Xiao, H., Xiao, Z., & Wang, Y. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.02.022 · Cited by 160 · see citing works

FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining

Seeja, K. R., & Zareapoor, M. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/252797 · Cited by 157 · see citing works

Insurance fraud detection: Evidence from artificial intelligence and machine learning

Aslam, F., Hunjra, A. I., Ftiti, Z., Louhichi, W., & Shams, T. (2022) · Research in International Business and Finance · doi:10.1016/j.ribaf.2022.101744 · Cited by 156 · see citing works

A Comparative Performance Assessment of Ensemble Learning for Credit Scoring

Li, Y., & Chen, W. (2020) · Mathematics · doi:10.3390/math8101756 · Cited by 152 · see citing works

Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning

Kim, E., Lee, J., Shin, H., Yang, H., Cho, S., Nam, S.-K., Song, Y., Yoon, J.-A., & Kim, J. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.03.042 · Cited by 151 · see citing works

On the suitability of resampling techniques for the class imbalance problem in credit scoring

Marqués, A. I., García, V., & Sánchez, J. S. (2012) · Journal of the Operational Research Society · doi:10.1057/jors.2012.120 · Cited by 149 · see citing works

CoDetect: Financial Fraud Detection With Anomaly Feature Detection

Huang, D., Mu, D., Yang, L., & Cai, X. (2018) · IEEE Access · doi:10.1109/access.2018.2816564 · Cited by 149 · see citing works

Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

Lucas, Y., Portier, P.-É., Laporte, L., He-Guelton, L., Caelen, O., Granitzer, M., & Calabretto, S. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.08.029 · Cited by 144 · see citing works

A Model Based on Convolutional Neural Network for Online Transaction Fraud Detection

Zhang, Z., Zhou, X., Zhang, X., Wang, L., & Wang, P. (2018) · Security and Communication Networks · doi:10.1155/2018/5680264 · Cited by 143 · see citing works

Cost-sensitive payment card fraud detection based on dynamic random forest and k -nearest neighbors

Nami, S., & Shajari, M. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.06.011 · Cited by 140 · see citing works

Modelling and interpreting pre-evacuation decision-making using machine learning

Zhao, X., Lovreglio, R., & Nilsson, D. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103140 · Cited by 138 · see citing works

A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits

Gray, G. L., & Debreceny, R. S. (2014) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2014.05.006 · Cited by 137 · see citing works

Credit card fraud detection and concept-drift adaptation with delayed supervised information

Pozzolo, A. D., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280527 · Cited by 135 · see citing works

Financial Defaulter Detection on Online Credit Payment via Multi-view Attributed Heterogeneous Information Network

Zhong, Q., Liu, Y., Ao, X., Hu, B., Feng, J., Tang, J., & He, Q. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380159 · Cited by 134 · see citing works

Digitalisation and Big Data Mining in Banking

Hassani, H., Huang, X., & Silva, E. S. (2018) · Big Data and Cognitive Computing · doi:10.3390/bdcc2030018 · Cited by 132 · see citing works

Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection

Cheng, D., Xiang, S., Shang, C., Zhang, Y., Yang, F., & Zhang, L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5371 · Cited by 132 · see citing works

Federated learning model for credit card fraud detection with data balancing techniques

Salam, M. A., Fouad, K. M., Bably, D. L. E., & Elsayed, S. M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-023-09410-2 · Cited by 132 · see citing works

Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation

Xiang, S., Zhu, M., Cheng, D., Li, E., Zhao, R., Ouyang, Y., Chen, L., & Zheng, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26702 · Cited by 130 · see citing works

Fraud detection using self-organizing map visualizing the user profiles

Olszewski, D. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.07.008 · Cited by 128 · see citing works

Using machine learning for direct demand modeling of ridesourcing services in Chicago

Yan, X. “., Liu, X., & Zhao, X. (2020) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2020.102661 · Cited by 128 · see citing works

Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture

Malik, E. F., Khaw, K. W., Belaton, B., Wong, W. P., & Chew, X. (2022) · Mathematics · doi:10.3390/math10091480 · Cited by 127 · see citing works

Auto loan fraud detection using dominance-based rough set approach versus machine learning methods

Błaszczyński, J., Filho, A. T. D. A., Matuszyk, A., Szeląg, M., & Słowiński, R. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113740 · Cited by 126 · see citing works

Detecting Financial Fraud Using Data Mining Techniques: A Decade Review from 2004 to 2015

Mousa, A. (2021) · Journal of Data Science · doi:10.6339/jds.201607_14(3).0010 · Cited by 123 · see citing works

Predicting overall survivability in comorbidity of cancers: A data mining approach

Zolbanin, H. M., Delen, D., & Zadeh, A. H. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.04.003 · Cited by 121 · see citing works

Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization

Carcillo, F., Borgne, Y. L., Caelen, O., & Bontempi, G. (2018) · International Journal of Data Science and Analytics · doi:10.1007/s41060-018-0116-z · Cited by 128 · see citing works

A Survey on Credit Card Fraud Detection Using Machine Learning

Popat, R. R., & Chaudhary, J. (2018) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei.2018.8553963 · Cited by 120 · see citing works

A review of data mining applications in crime

Hassani, H., Huang, X., Silva, E. S., & Ghodsi, M. (2016) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11312 · Cited by 114 · see citing works

Generative Adversarial Networks and Markov Random Fields for oversampling very small training sets

Salazar, A., Vergara, L., & Safont, G. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113819 · Cited by 114 · see citing works

A framework for understanding artificial intelligence research: insights from practice

Bawack, R. E., Wamba, S. F., & Carillo, K. (2021) · Journal of Enterprise Information Management · doi:10.1108/jeim-07-2020-0284 · Cited by 126 · see citing works

Time-Aware Attention-Based Gated Network for Credit Card Fraud Detection by Extracting Transactional Behaviors

Xie, Y., Liu, G., Yan, C., Jiang, C., & Zhou, M. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3158318 · Cited by 108 · see citing works

Intention-aware Heterogeneous Graph Attention Networks for Fraud Transactions Detection

Liu, C., Sun, L., Ao, X., Feng, J., He, Q., & Yang, H. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467142 · Cited by 107 · see citing works

Data Mining-Based Ethereum Fraud Detection

Jung, E., Tilly, M. L., Gehani, A., & Ge, Y. (2019) · IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain.2019.00042 · Cited by 106 · see citing works

Learning Transactional Behavioral Representations for Credit Card Fraud Detection

Xie, Y., Liu, G., Yan, C., Jiang, C., Zhou, M., & Li, M. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3208967 · Cited by 106 · see citing works

Using Variational Auto Encoding in Credit Card Fraud Detection

Huang, T., Guangquan, C., & Kuihua, H. (2020) · IEEE Access · doi:10.1109/access.2020.3015600 · Cited by 104 · see citing works

Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble

Wang, H., Xu, Q., & Zhou, L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0117844 · Cited by 103 · see citing works

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Stojanović, B., Božić, J., Hofer-Schmitz, K., Nahrgang, K., Weber, A., Badii, A., Sundaram, M., Jordan, E., & Runevic, J. (2021) · Sensors · doi:10.3390/s21051594 · Cited by 114 · see citing works

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 101 · see citing works

A novel credit card fraud detection using supervised machine learning model

Siraj, M., haqqani, M. W., & Quadry, K. M. (2024) · International Journal of Multidisciplinary Research and Growth Evaluation · doi:10.54660/.ijmrge.2024.5.1.313-324

A Study on Credit Card Fraud Detection using Data Mining Techniques

Suresh, G., & Justin Raj, R. (2018) · International Journal of Data Mining Techniques and Applications · doi:10.20894/ijdmta.102.007.001.004 · Cited by 17 · see citing works

Effective Credit Card Fraud Detection Using Data Mining Techniques

Xian, O. J. (2024) · doi:10.13140/rg.2.2.28279.94889/1

A Systematic Review of Data Mining Approaches to Credit Card Fraud Detection

Mekterović, I., Brkić, L., & Baranović, M. (2018) · WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS · Cited by 13 · see citing works

A Comparative Analysis of Credit Card Detection Models

Kim, K. C. L., Mustapha, A., Palaniappan, V., Mun, W. K., & Kasinathan, V. (2023) · Springer proceedings in physics · doi:10.1007/978-981-99-2850-7_18 · Cited by 3 · see citing works