Cost-Sensitive Learning for Medical Insurance Fraud Detection With Temporal Information

Author:

Shi Haolun1ORCID,Tayebi Mohammad A.2ORCID,Pei Jian2ORCID,Cao Jiguo1ORCID

Affiliation:

1. Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada

2. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Reference30 articles.

1. Graph analytics for healthcare fraud risk estimation

2. Knowledge discovery from massive healthcare claims data

3. Detection of fraudulent claims using hierarchical cluster analysis;khurjekar;Proc IIE Annu Conf Inst Ind Syst Engineers,2015

4. Healthcare fraud detection: A survey and a clustering model incorporating geo-location information;liu;Proc 29th World Continuous Auditing Reporting Symp,2013

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4. A Spatio-temporal Neural Network for Medical Insurance Fraud Detection;Lecture Notes in Computer Science;2024

5. FraudNet: A Network-centric Method for Balancing and Detecting Anomalies in Healthcare Insurance;2023 4th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI);2023-12-15

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