Predictive Fraud Modeling
Predictive Fraud Modeling is a computational method that belongs to the class of fraud detection techniques and is distinguished by its reliance on statistical and machine learning algorithms to identify the likelihood of fraudulent activities before they occur by analyzing historical and real-time data patterns.
221
Publications
15.9K
Citations
695
Authors
307
Institutions
Publications per year
2017–2026
150
Parents
221
Data mining for credit card fraud: A comparative study
Siddhartha Bhattacharyya, Sanjeev Jha, Kurian Tharakunnel et al. · Decision Support Systems · 2010 · 963 citations
Business, Computer Science, Finance +16
Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach
Yang Bao, Bin Ke, Bin Li et al. · Journal of Accounting Research · 2019 · 450 citations
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307

Royal Roads University
Victoria, Canada
4 h-index
7 publications
288 citations
KU Leuven
Leuven, Belgium
4 h-index
9 publications
412 citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
3 h-index
4 publications
2.7K citations

Brunel University of London
London, United Kingdom
3 h-index
3 publications
202 citations
Shandong University of Science and Technology
Qingdao, China
3 h-index
11 publications
684 citations

University of Hassan II Casablanca
Casablanca, Morocco
3 h-index
9 publications
653 citations

Shanghai Jiao Tong University
Shanghai, China
3 h-index
4 publications
593 citations
Bentley University
Waltham, United States
3 h-index
3 publications
68 citations
1–8 of 307
10 publications · 681 citations
10
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6 publications · 809 citations
6
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5 publications · 2.7K citations
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Repository · 4 publications · 221 citations
4
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4 publications · 148 citations
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