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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A Comprehensive Review of Techniques for Detecting and Preventing Card-Not-Present (CNP) Fraud
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“As the default events become complicated, expert experiences become limited, and tree-based models, e.g., random forests [16] and gradient boosting decision tree [17], [18] are adopted, which rely heavily on feature engineering.”
E-Commerce Site Payment Fraud Detection using Machine Learning Technique
Immanuel, T. B., Manikandan, K., Muthukumar, P., & Arikesh, A. (2025) · International Conference on Circuit, Power and Computing Technologies · doi:10.1109/iccpct65132.2025.11176484 · Cited by 1
“In 2008, merchant online fraud detection reached an amount of 4 billion [1].”
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Samantaray, R. R., Fathima, S., Azeez, A., Ahmed, M., & Anjum, A. (2025) · International Conference on Electrical, Electronics, and Computer Science with Advance Power Technologies - A Future Trends (ICE2CPT) · doi:10.1109/ice2cpt66440.2025.11340224 · Cited by 1
“Traditional supervised learning methods, such as Logistic Regression, Support Vector Machines (SVM), Decision Trees, and Random Forests, have been widely applied for their simplicity and interpretability [2].”
Federated Learning for Credit Card Fraud Detection: A Comparative Study of Logistic Regression, Random Forest, and XGBoost
Hallal, T. E., & Mourabit, Y. E. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-16281-6_28 · Cited by 1
Detecting Credit Card Fraud using Random Forest and CRISP-DM: A Machine Learning Approach
Lim, M. J., Alexander, P. W., Karyanto, V. M., Christie, T. C., & Oktavia, T. (2025) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss66954.2025.11389400 · Cited by 1
“Random Forest is effective for imbalanced datasets and mitigates overfitting [11].”
Challenges in Identifying Illicit Actors in Financial Networks
Aljundi, A. A., Adiga, A., Potter, P. B. K., swarup, S., Vullikanti, A. K. S., & MARATHE, M. V. (2025) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tps-isa67132.2025.00050 · Cited by 1
“…cannot be found [22], (3) credit card frauds, which can occur through fraudulent activity, e.g., phishing attacks, or through physical theft [23], and (4) money laundering [21], which takes place in three phases: placement, when funds are introduced into the financial system; layering, when…”
Are You, You? Seamlessly Fighting Identity Fraud with Keystroke Dynamics
Kim, D., Valacich, J. S., Jenkins, J. L., Wilson, D. W., Kumar, M. N. V. R., & Weisgarber, P. (2026) · Information Systems Research · doi:10.1287/isre.2023.0088 · Cited by 1
HADA : A Hybrid Anomaly Detection Approach Using Unsupervised Machine Learning
Thiong'o, F., Nderu, L., Mwangi, R. W., & Oteyo, I. N. (2026) · Engineering Reports · doi:10.1002/eng2.70696 · Cited by 4
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“We use advanced feature engineering on features providing very important behavioural and temporal signals for fraud, alongside approaches such as custom class weighting, to help improve model performance with respect to fraud detection[2].”
SCWO-SNN: Snow carpet weaver optimization based spiking neural network for fraud detection in UPI transactions using federated learning
Kolli, C. S., Jagannathan, V., Lanka, D. R., Rajyalakshmi, C., Radha, V. K. R., & Reddy, V. V. K. (2026) · Computer Standards & Interfaces · doi:10.1016/j.csi.2026.104166 · Cited by 1
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“Traditional fraud detection systems primarily focus on identifying anomalous or suspicious transactions using statistical models and supervised learning techniques [2].”
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Alanezi, M. A., Homeed, M. T., Mohamed, Z., & Zeki, A. M. (2020) · International Conference on Data Analytics for Business and Industry (ICDABI) · doi:10.1109/icdabi51230.2020.9325705 · Cited by 1
“[6] [6] Evaluate three DM methods to detect fraudulent transactions SVM, RF, and LR RF had the highest accuracy percentage 96% and AUC95% [8] [8] Comparing five models Baysein Network, Discriminant Analysis, LR, ANN, and SVM ANN had the highest accuracy with 75%.”
BackgroundMethod
Detection of Credit Card Fraud in a Brazilian database using Autoencoder Neural Network
de Azevedo, F. L., Komati, K. S., & Júnior, H. S. (2021) · Anais do ... Simpósio Brasileiro de Automação Inteligente/Simpósio Brasileiro de Automação Inteligente · doi:10.20906/sbai.v1i1.2796 · Cited by 1
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Bhavani, D. J., Sri, K. J. N. I., Padma, D., & Kumar, M. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-7330-6_14 · Cited by 1
Analysis and prediction for credit card fraud detection dataset using data mining approaches
Arun, G. K., & Rajesh, P. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns5.9534 · Cited by 1
“However, single-tree model is possibly sensitive to specific training data and easy to overfit [15] and [16].”
Background
Enhancing performance of a LENN based CCFD model with a hybrid TOPSIS-ReliefF based feature selection technique
Dash, R., Dash, R., & Rautray, R. (2022) · Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON) · doi:10.1109/odicon54453.2022.10009935 · Cited by 1
“The relevant literature presents several reviews on application of machines learning techniques for revealing fraudulent activities [1-3].”
Background
Credit Card Detection by Applying Interpretable Tree-Based Machine Learning Models
Xiong, S. (2023) · Atlantis highlights in engineering/Atlantis Highlights in Engineering · doi:10.2991/978-94-6463-210-1_34 · Cited by 1
Innovation in Large Organizations: Structuring Organizations to Combat Emerging Threats: Exploration Through Case Studies
Kirby, C. (2023) · Annals of theoretical psychology · doi:10.1007/978-3-031-29807-3_9 · Cited by 1
A Class Balancing Based Machine Learning Approach for Fraudulent Credit Card Transaction Detection
Patra, G. R., Iyer, S. P., Satyaprakash, S., & Mohanty, M. N. (2023) · International Conference on Circuits, Power and Intelligent Systems (CCPIS) · doi:10.1109/ccpis59145.2023.10291883 · Cited by 1
Traditional and Machine Learning Methods for Credit Scoring: an introduction
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A Synthesis on Machine Learning for Credit Scoring: A Technical Guide
Akil, S., Sekkate, S., & Adib, A. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-54376-0_9 · Cited by 1
Artificial Intelligence and Data Mining Techniques
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“Some of the algorithms are Fisher Discriminant Analysis [11], Support Vector Machine [12], Hidden Markov Model [13] etc.”
Method
Data Mining Applications for Banks’ Business Intelligence Maturity
Ranjbarfard, M., & Ahmadi, S. (2020) · Journal of Digital Information Management · doi:10.6025/jdim/2020/18/5-6/163-172 · Cited by 1
Tiered Financial Fraud Detection Utilizing Precision Stratified Random Forest Assembly
Gardner, C., Lo, D. C.-T., Chern, J.-C., Paschos, P., & Ng, C. (2019) · IEEE International Conference on Big Data Intelligence and Computing (DataCom) · doi:10.1109/datacom.2019.00047 · Cited by 1
“Studies in fraud detection such as [5, 12, 13, 14] focus on creating a single model which can perform better at predicting fraud than all other singular models.”
Background
Privacy-Preserving Behavioral Anomaly Detection in Dynamic Graphs for Card Transactions
Damoun, F., Seba, H., & State, R. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0576-7_22 · Cited by 1
“…survey studies on anomaly detection [6], including those using graph-based methods [7–9], and those specifically focused on fraud detection [10, 11], highlight the need to expand data collection to identify rare and unusual patterns or outliers within datasets that differ significantly…”
Fraud Detection in Monetary Transactions
Nair, R. R., Kaveri, V., Sivakumar, S., Srinithi Janani, R., Babu, T., & Rajesh, R. (2024) · doi:10.1109/icait61638.2024.10690586 · Cited by 1
“In response to recent high-profile cases of financial statement fraud, this research investigates the effectiveness of various data mining methods in identifying fraudulent activities within Chinese companies [16].”
Background
A Study on Anomaly Detection and Systemic Risk Early Warning in Guangdong Financial Market Based on Big Data and Deep Learning.
Qu, Y., Peng, Y., Xie, W., Luo, F., & Zeng, L. (2024) · doi:10.1145/3695080.3695146 · Cited by 1
“Methods based on network analysis have also been applied to identify systemic financial risks, such as constructing financial networks to analyze systemic risk propagation paths and key nodes [5].”
WorldCom Skandalı
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Sozib, H. M., Farouk, M. O., Hossain, M. F., Uddin, M. M., Mia, M. R., Adrita, T. A., & Hasan, M. M. (2024) · International Conference on Progressive Innovations in Intelligent Systems and Data Science (ICPIDS) · doi:10.1109/icpids65698.2024.00045 · Cited by 1
Fraudulent event detection in credit card data operations using SVM-RBF kernel function machine learning classification technique
Kokate, S., & Chettyi, M. S. R. (2025) · Intelligent Decision Technologies · doi:10.1177/18724981241305118 · Cited by 1
Credit Card Fraud Detection
Kalal, V. C. (2025) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem48811 · Cited by 1
Online Payment Fraud Detection using Machine Learning
Mohanambika, S., Sushmitha, S., & Lincy, A. (2025) · International Conference on Innovative Trends in Information Technology (ICITIIT) · doi:10.1109/icitiit64777.2025.11041104 · Cited by 1
The Role of Ai and Machine Learning in Financial Fraud Detection
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Fraud Detection System Based on Graph Data Structures
Bronskaya, V. V., Игнашина, Т. В., Bashkirov, D., Balzamov, D. S., KONDRAT'EVA, M. I., Garipov, K., & Aetov, A. U. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-95649-2_52 · Cited by 1
Development of a community e-portal constellation: Queensland Smart Region Initiative.
Parker, D., Downie, G., & Manville, G. (2012) · UEA Digital Repository (University of East Anglia) · Cited by 1
IMPROVING TELEMARKETING INTELLIGENCE THROUGH SIGNIFICANT PROPORTION OF TARGET INSTANCES
Tan, D., Liew, S., & Yeoh, W. (2014) · Journal of the Association for Information Systems · Cited by 1
Hybrid Approach for Improvising Credit Card Fraud Detection Based on Collective Animal Behaviour and SVM
Dheepa, V., & Dhanapal, R. (2013) · Communications in computer and information science · doi:10.1007/978-3-642-40576-1_29 · Cited by 1
An Accurate Tax Fraud Classifier with Feature Selection based on Complex Network Node Centrality Measure
Matos, T., de Macêdo, J. A. F., Monteiro, J. M., & Lettich, F. (2017) · International Conference on Enterprise Information Systems · doi:10.5220/0006335501450151 · Cited by 1
Evolutionary Propositionalization of Multi-Relational Data — Research Notes
Kassarnig, V., & Wotawa, F. (2018) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194018400260 · Cited by 1
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Jiang, H., Ji, J., Ge, Q., & Li, C. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-981-32-9050-1_63 · Cited by 1
Fraud Detection in Mobile Payment System using Machine Learning: A Comprehensive Survey
Gawas, B. G. (2019) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2019.4175 · Cited by 1
“56% 2011 [3] SVM, Random forest, logistic regression 96%, 93.”
Credit Card Fraud Detection Using Correlation-Based Feature Extraction and Ensemble of Learners
Thaseen, I. S., & Lavanya, K., V (2019) · Algorithms for intelligent systems · doi:10.1007/978-981-15-0633-8_2 · Cited by 1
Big data and criminology from an AI perspective
Gerritsen, C. (2020) · Routledge eBooks · doi:10.4324/9781351029704-3 · Cited by 1
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Kaur, G. (2021) · Advances in business information systems and analytics book series · doi:10.4018/978-1-7998-7716-5.ch007 · Cited by 1
A Novel Information-Entropy-Based Feature Extraction Method for Transaction Fraud Detection
Zhu, D., Yan, C., Guang, M., & Xie, Y. (2021) · Intelligent Autonomous Systems · doi:10.1109/icoias53694.2021.00031 · Cited by 1
Grammar-Based Multi-objective Genetic Programming with Token Competition and Its Applications in Financial Fraud Detection
Li, H., & Wong, M. (2021) · Natural computing series · doi:10.1007/978-3-030-79553-5_11 · Cited by 1
Analysis of Various Boosting Algorithms Used for Detection of Fraudulent Credit Card Transactions
Zainab, K., Dhanda, N., & Abbas, Q. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-0882-7_98 · Cited by 1
A Sine Cosine Learning Algorithm for Performance Improvement of a CPNN Based CCFD Model
Dash, R., Rautray, R., & Dash, R. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-9873-6_19 · Cited by 1
Artificial Intelligence in the Economic Sector: prevent and account
Antunes, M. J., & Sousa, S. A. D. (2021) · doi:10.47907/livro2021_4 · Cited by 3
New Perspectives of Pattern Recognition for Automatic Credit Card Fraud Detection
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Credit Card Fraudulent Detection Using Machine Learning Algorithm
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Chaaouan, H., Bouhadda, M., Alami, R. E., Essahlaoui, A., Ghzaoui, M. E., Qjidaa, H., & Jamil, M. O. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-29857-8_93 · Cited by 1
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“These scoring engines were inspired by neural network [1, 2, 3], decision tree [4, 5, 6], random forest [7], network approach [8] and deep neural network [9].”
Method
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Showalter, S., & Wu, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.01486 · Cited by 3
“Despite the fact that experiments on computationally efficient algorithms [30] are continually conducted and published, their industrial practicality is seldom emphasized.”
BackgroundMethodInfluential
GMOTE: Gaussian based minority oversampling technique for imbalanced classification adapting tail probability of outliersPreprint
Yang, S. J., & Joon, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.03855 · Cited by 3
“However, there are problems in various domains such as credit card fraud [6], software defect prediction [43], text mining [33], pattern recognition [46], and gene mining [48] if data follows a biased distribution.”
Background
Essays on Predictive Analytics in E-Commerce
Urbanke, P. (2016) · doi:10.53846/goediss-5801 · Cited by 1
Automating Software Development using Artificial IntelligencePreprint
Nagulapati, V., Rapelli, S. R., & Fiaidhi, J. (2020) · doi:10.36227/techrxiv.12089139 · Cited by 1
Predicción de transiciones laborales de la actividad a la inactividad en el mercado laboral argentino: un enfoque de aprendizaje automático
Staudt, A. (2022) · doi:10.35537/10915/139421 · Cited by 2
Detecção de fraudes em cartão de crédito: um caso de uso de modelos supervisionados no e-commerce brasileiro
Cristovão, R. B. (2023) · doi:10.11606/d.55.2023.tde-23082023-102023 · Cited by 1
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Ekellem, E. A. F. (2023) · doi:10.36227/techrxiv.24707151 · Cited by 1
Causal GNNs and the Anthropology of Data: An Ethical Approach to Fairness and Transparency in Financial AIPreprint
Vallarino, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5196394 · Cited by 1
Um processo para modelagem e aplicação de técnicas computacionais para detecção de fraudes em transações eletrônicas
Santiago, G. P. (2014) · doi:10.11606/d.45.2014.tde-23052014-144032
Users Shopping Behavior Mining of B2C E-Commerce
Gan, P. (2013) · Advanced materials research · doi:10.4028/www.scientific.net/amr.765-767.1568
“Between them there are difference that is final shopping decisions are made by the former while the customer behavior that making shopping decisions are provided by the latter information [3].”
Background
A Novel Statistical Approach to Detect Card Frauds Using Transaction Patterns
Lee, C. C., & Yoon, J. W. (2015) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2014edp7071
A KDD-Based Methodology to Rank Trust in e-Commerce Systems
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“Due to the importance of the fraud detection problem, we may distinguish several works that discuss the subject [15], [3], [21], [17].”
Background
DOCTEUR DE L'UNIVERSITÉ DU LUXEMBOURG EN INFORMATIQUE
Bahnsen, A. C. (2015)
Verhaltensanalyse zur Erkennung von Missbrauch mobiler Geldtransferdienste
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An Empirical Investigation of Factors Affecting Resistance to Using Multi-Method Authentication Systems in Public-Access Environments
Marnell, J. W. (2016) · NSUWorks (Nova Southeastern University)
Data Mining and Knowledge Extraction in the Risk Based Insurance Audit: A Case Study (City of Tehran)
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“Also, bank card abuse as a different type of financial fraud was studied by Bhatachari et al., who used support vector machines and random forest in order to detect such abuses [17].”
Background
Quantitative Finance in the Post Crisis Financial Environment
Oden, K. D. (2016) · Palgrave Macmillan US eBooks · doi:10.1057/978-1-137-59442-6_16
Understanding and predicting method-level source code changes using commit history data
Heron, J. (2016) · e-scholar@UOIT (University of Ontario Institute of Technology)
A study on error correction of multiple criteria and multiple constraint levels linear programming based classification
Wang, B., & Shi, Y. (2017) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3106426.3109429
“Fortunately, with the evolution of data mining and other data-driven technologies, we can apply machine learning techniques on credit card client classi � cation problem [2, 6].”
Method
An evaluation of inter-organisational identity theft knowledge sharing practice in the UK retail sector
Chohan, R. (2016) · CLOK (University of Central Lancashire)
A fractal statistical fraud detection analysis in databases
Pension Fund of the Russian Federation, S. S. 4., & Hripunov, P. (2018) · International Conference on Information Technology and Nanotechnology (ITNT) · doi:10.18287/1613-0073-2018-2212-443-445
“The main approaches to fraud detection today are: neural networks [2,3], logistic model [4], support vector machine [4], decision trees [5], genetic algorithm [5], text mining [6], Bayesian belief network [7], featureless approach [12] and others.”
Background
Hybrid Data Mining to Reduce False Positive and False Negative Prediction in Intrusion Detection System
Palanisamy, B., Panja, B., & Meharia, P. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-03405-4_1
Managing Trust of E-customers in Online Shopping – A Case Study of Saudi Arabia
Ansari, Z. A. (2019) · European Journal of Business and Management · doi:10.7176/ejbm/11-6-06
“…identity of the e-customers, credit card fraud, fishing for sensitive information and non-delivery of the merchandise are important issues of concern growing in number as well as in geographical reach (Bhattacharya et al., 2011; Demery, 2010; Gravish & Tucci, 2006; Gravish, 2008; Ries, 2010).”
Background
A Primer of Statistical Methods for Classification
Dey, R., & Mulekar, M. S. (2019) · STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health · doi:10.1007/978-3-030-11431-2_6
Crime Case Reasoning Based Knowledge Discovery Using Sentence Case Relative Clustering for Crime Analyses
Begam, M. R., & Sengottuvelan, P. (2018) · International Journal of Engineering & Technology · doi:10.14419/ijet.v7i3.27.17662
“They get initial centroid from this initial cluster look at the use of clustering procedure for a statistics quarrying method to help notice the criminalities patterns [3, 4] and speed up the process of solving the crime.”
Method
Prediccion de postulantes que cometeran fraude interno en una compania con algoritmos de aprendizaje supervisado
Espinoza-Montalvo, S. (2019) · Interfases · doi:10.26439/interfases2019.n012.4637
Machine Learning Techniques to Detect Fraud in Credit Cards on the HPCC Systems Platform
Prasad, A., Dev, R., Shobha, G., & Shetty, J. (2019) · International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) · doi:10.1109/csitss47250.2019.9031032
A Federated F-score Based Ensemble Model for Automatic Rule ExtractionPreprint
Li, K., Zheng, F., Tian, J., & Xiang, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03533
“Meanwhile, traditional machine learning models also have similar issues (Bhattacharyya et al., 2011; Ngai et al., 2011).”
Background
Developing Effective Fraud Detection Methods for Online Auction
張昭憲, 劉祐宏, & 李青芬 (2020) · Tamkang University Institutional Repository (TKUIR)
Hybrid Models in Automobile Insurance: Technology Adoption and Customer Relations
Tian, X. (2019) · University of North Texas Digital Library (University of North Texas) · doi:10.12794/metadc1538717
AN INTELLIGENT CREDIT CARD FRAUD DETECTION SYSTEM
Ekwealor, O. U., Alex, A. O. I., Ezuruka, E. O., & Uchefuna, C. I. (2021) · International Journal of Software & Hardware Research in Engineering · doi:10.26821/ijshre.9.2.2021.9207
Selective Multi-source Transfer Learning with Wasserstein Domain Distance for Financial Fraud Detection
Sun, Y.-F., Lan, L.-J., Zhao, X.-Y., Fan, M., Guo, Q., & Li, C. (2021) · Communications in Computer and Information Science · doi:10.1007/978-981-16-1160-5_38
A Comparison between Classifiers on Credit Card Fraud Detection Problem
Shi, W. (2020) · International Conference on E-Business and E-commerce Engineering (EBEE) · doi:10.1145/3446922.3446925
“And the model will be doing better in overfitting avoidance, flexibility and stability [8].”
Method
Itsy Bitsy SpiderNet: Fully Connected Residual Network for Fraud DetectionPreprint
Afanasiev, S., Smirnova, A., & Kotereva, D. (2021) · arXiv.org · doi:10.48550/arxiv.2105.08120 · Cited by 2
“The development of anti-fraud models is most often solved as a binary classification problem (fraud/non-fraud), where classical algorithms are used, such as SVM, Logistic Regression, Random Forest, and others [5].”

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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