Predictive Analytics For Fraud
Predictive Analytics for Fraud is a methodological framework within fraud risk assessment that employs statistical and machine learning techniques to identify patterns and forecast the likelihood of fraudulent activities based on historical and real-time data.
160
Publications
11.6K
Citations
408
Authors
202
Institutions
Publications per year
2017–2026
109
Parents
160
The accuracy, fairness, and limits of predicting recidivism
Julia Dressel, Hany Farid · Science Advances · 2018 · 1K citations · Full text
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
1–5 of 160
408
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Fraunhofer Institute for Secure Information Technology, Germany
2 publications · 81 citations
2
h-index
1–5 of 408
202

Royal Roads University
Victoria, Canada
4 h-index
7 publications
288 citations

Hefei University of Technology
Hefei, China
3 h-index
8 publications
184 citations

Shanghai Jiao Tong University
Shanghai, China
3 h-index
4 publications
593 citations
Indiana University
Bloomington, United States
3 h-index
4 publications
222 citations
New York University
New York, United States
3 h-index
6 publications
117 citations

Beijing Institute of Technology
Beijing, China
2 h-index
6 publications
172 citations
Fraunhofer Institute for Secure Information Technology
Darmstadt, Germany
2 h-index
5 publications
197 citations
Bentley University
Waltham, United States
2 h-index
2 publications
45 citations
1–8 of 202
Repository · 4 publications · 159 citations
8
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5 publications · 306 citations
5
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Repository · 4 publications · 221 citations
4
h-index
3 publications · 1.9K citations
3
h-index
2 publications · 278 citations
3
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1–5