Predictive Fraud Analytics
Predictive Fraud Analytics is a data-driven methodological framework within health care fraud detection that employs statistical modeling and machine learning techniques to identify anomalous patterns and forecast potential Medicare fraud prior to human investigation.
46
Publications
2K
Citations
138
Authors
68
Institutions
Publications per year
2017–2026
33
Parents
46
Finding Needles in a Haystack: Using Data Analytics to Improve Fraud Prediction
Johan Perols, Robert M. Bowen, Carsten Zimmermann et al. · The Accounting Review · 2016 · 210 citations
Business, Accountancy, Accounting +17
A survey on statistical methods for health care fraud detection
Jing Li, Kuei-Ying Huang, Jionghua Jin et al. · Health Care Management Science · 2008 · 169 citations
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations
2021 · 93 citations · Full text
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68
Florida Atlantic University
Boca Raton, United States
4 h-index
6 publications
400 citations

Berlin School of Economics and Law
Berlin, Germany
2 h-index
2 publications
97 citations

University of Twente
Enschede, The Netherlands
2 h-index
3 publications
134 citations

San Diego Supercomputer Center
San Diego, United States
2 h-index
4 publications
194 citations
University of Lisbon
Lisbon, Portugal
1 h-index
1 publications
93 citations

Monash University
Melbourne, Australia
1 h-index
1 publications
86 citations
Sogei (Italy)
Rome, Italy
1 h-index
1 publications
14 citations
Maharshi Dayanand University
Rohtak, India
1 h-index
1 publications
21 citations
1–8 of 68
1 publications · 56 citations
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2 publications · 56 citations
2
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1 publications · 32 citations
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HCMS · 1 publications · 169 citations
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1 publications · 25 citations
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