Adaptive Fraud Detection
Adaptive Fraud Detection is a dynamic fraud detection framework that continuously evolves its analytical models and decision rules in response to emerging fraud patterns, new transaction behaviors, and feedback signals, thereby distinguishing itself from static or rule-based systems through its capacity for real-time learning and system-wide adaptation.
48
Publications
2.2K
Citations
150
Authors
64
Institutions
Publications per year
2017–2026
32
Parents
48
An Integrated Cluster Detection, Optimization, and Interpretation Approach for Financial Data
Tie Li, Gang Kou, Yi Peng et al. · IEEE Transactions on Cybernetics · 2021 · 220 citations · Full text
Computing, Data Science, Finance +17
A hybrid model for plastic card fraud detection systems
M. Krivko · Expert Systems with Applications · 2010 · 97 citations
Business, Accountancy, Accounting +14
1–5 of 48
150
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h-index
2
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2
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1
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University of California, Santa Barbara, United States
1 publications · 46 citations
1
h-index
1–5 of 150
64
University of Virginia
Charlottesville, United States
2 h-index
9 publications
471 citations

University of Leicester
Leicester, United Kingdom
2 h-index
2 publications
407 citations
Tongji University
Shanghai, China
2 h-index
6 publications
194 citations
University of Alabama in Huntsville
Huntsville, United States
2 h-index
2 publications
62 citations
Columbia University
New York, United States
2 h-index
2 publications
62 citations

Guru Gobind Singh Indraprastha University
Delhi, India
2 h-index
4 publications
88 citations

Beijing Institute of Technology
Beijing, China
1 h-index
1 publications
17 citations
Thuongmai University
Hanoi, Vietnam
1 h-index
2 publications
32 citations
1–8 of 64
4 publications · 429 citations
3
h-index
2 publications · 105 citations
2
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2 publications · 128 citations
2
h-index
1 publications · 67 citations
1
h-index
1 publications · 32 citations
1
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1–5