The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Credit Card Fraud Detection - Machine Learning methods
Abstract
Credit card fraud refers to the physical loss of credit card or loss of sensitive credit card information. Many machine-learning algorithms can be used for detection. This research shows several algorithms that can be used for classifying transactions as fraud or genuine one. Credit Card Fraud Detection dataset was used in the research. Because the dataset was highly imbalanced, SMOTE technique was used for oversampling. Further, feature selection was performed and dataset was split into two parts, training data and test data. The algorithms used in the experiment were Logistic Regression, Random Forest, Naive Bayes and Multilayer Perceptron. Results show that each algorithm can be used for credit card fraud detection with high accuracy. Proposed model can be used for detection of other irregularities.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
Fernandez, A., Garcia, S., Herrera, F., & Chawla, N. V. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11192 · Cited by 2,219
Credit card fraud detection using machine learning techniques: A comparative analysis
Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017) · 2017 International Conference on Computing Networking and Informatics (ICCNI) · doi:10.1109/iccni.2017.8123782 · Cited by 625
Pumsirirat, A., & Yan, L. (2018) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2018.090103 · Cited by 315
Dhankhad, S., Mohammed, E., & Far, B. (2018) · 2018 IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2018.00025 · Cited by 208
Classification of Imbalanced Data by Using the SMOTE Algorithm and Locally Linear Embedding
Wang, J., Xu, M., Wang, H., & Zhang, J. (2006) · 2006 8th international Conference on Signal Processing · doi:10.1109/icosp.2006.345752 · Cited by 197
Using deep networks for fraud detection in the credit card transactions
Kazemi, Z., & Zarrabi, H. (2017) · International Conference on Knowledge-Based Engineering and Innovation · doi:10.1109/kbei.2017.8324876 · Cited by 78
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
Credit Card Fraud Detection on the Skewed Data Using Various Classification and Ensemble Techniques
Mishra, A., & Ghorpade, C. (2018) · IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) · doi:10.1109/sceecs.2018.8546939 · Cited by 72
Credit Card Fraud Detection Based on Whale Algorithm Optimized BP Neural Network
Wang, C., Wang, Y., Ye, Z., Yan, L., Cai, W., & Pan, S. (2018) · International Conference on Computer Science & Education (ICCSE) · doi:10.1109/iccse.2018.8468855 · Cited by 66
Credit Card Fraud Detection Using Learning to Rank Approach
Kalaiselvi, N., Rajalakshmi, S., Padmavathi, J., et al. (2018) · doi:10.1109/iccpeic.2018.8525171 · Cited by 25
Cited by
Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms
Alarfaj, F. K., Malik, I., Khan, H. U., Almusallam, N., Ramzan, M., & Ahmed, M. (2022) · IEEE Access · doi:10.1109/access.2022.3166891 · Cited by 439
A Review on Financial Fraud Detection using AI and Machine Learning
Kamuangu, P. (2024) · Journal of Economics, Finance and Accounting Studies · doi:10.32996/jefas.2024.6.1.7 · Cited by 61
Metadata
- Authors
- Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., & Anderla, A.
- Venue
- 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH)
- Publisher
- IEEE
- Published
- 2019-03
- Pages
- 1-5
- OpenAlex
- W2946712695
- Cited by
- 323
- References
- 10
- FWCI
- 9.62
- Type
- conference paper
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Credit Card Fraud Detection - Machine Learning methods. https://app.cochat.ai/papers/10.1109/infoteh.2019.8717766
- The paper
- Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., & Anderla, A. (2019). Credit Card Fraud Detection - Machine Learning methods. 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH). https://doi.org/10.1109/infoteh.2019.8717766
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
