Social Network Analytics for Supervised Fraud Detection in Insurance-Reference-Cited by-同舟云学术

Social Network Analytics for Supervised Fraud Detection in Insurance

Published:2021-02-06 Issue: Volume: Page:
ISSN:0272-4332
Container-title:Risk Analysis
language:en
Short-container-title:Risk Analysis

Author:

Óskarsdóttir María¹,Ahmed Waqas²,Antonio Katrien³⁴⁵⁶,Baesens Bart³⁷,Dendievel Rémi²,Donas Tom²,Reynkens Tom³⁵

Affiliation:

1. Department of Computer Science Reykjavik University Reykjavik Iceland

2. AG Insurance Brussels Belgium

3. Faculty of Economics and Business KU Leuven Leuven Belgium

4. Faculty of Economics and Business University of Amsterdam Amsterdam The Netherlands

5. LRisk, Leuven Research Center on Insurance and Financial Risk Analysis KU Leuven Leuven Belgium

6. LStat, Leuven Statistics Research Center KU Leuven Leuven Belgium

7. Department of Decision Analytics and Risk University of Southampton Southampton UK

Publisher

Wiley

Subject

Physiology (medical),Safety, Risk, Reliability and Quality

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/risa.13693

Reference53 articles.

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2. A robust unsupervised method for fraud rate estimation;Ai J.;Journal of Risk and Insurance,2013

3. The impact of co‐offending;Andresen M. A.;The British Journal of Criminology,2009

4. 2015 John Wiley & Sons Hoboken NJ B. Baesens V. Van Vlasselaer W. Verbeke Fraud analytics using descriptive predictive and social network techniques: A guide to data science for fraud detection

5. 2006 Springer Berlin Heidelberg C. M. Bishop Pattern recognition and machine learning

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