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Data mining for credit card fraud: A comparative study
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“This methodology was first proposed by Whitrow et al. (2009) and has been used by a number of studies (Bhattacharyya et al., 2011; Jha et al., 2012; Dal Pozzolo et al., 2014; Bahnsen et al., 2016).”
From Data engineering for fraud detection (2021) · cites this paper as Bhattacharyya et al., 2011
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Cited by
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Explaining prediction models and individual predictions with feature contributions
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“Applications range from credit scoring [11] and fraud detection [5] to financial auditing [4] and efficiency analysis [18].”
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“Comparing RF against SVM and logisti regression [190], RF was the best performer in all experimental conditions as measured by almost all performance measurements.”
BackgroundMethodFraud 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 840
Metadata
- 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
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2011-02
- Volume
- 50
- Issue
- 3
- Pages
- 602-613
- ISSN
- 0167-9236 (ISSN-L)
- 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
- 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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