TY - GEN
T1 - A critical analysis of the application of data mining methods to detect healthcare claim fraud in the medical billing process
AU - Obodoekwe, Nnaemeka
AU - van der Haar, Dustin Terence
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - The healthcare industry has become a very important pillar in modern society but has witnessed an increase in fraudulent activities. Traditional fraud detection methods have been used to detect potential fraud, but in certain cases, they have been insufficient and time-consuming. Data mining which has emerged as a very important process in knowledge discovery has been successfully applied in the health insurance claims fraud detection. We performed an analysis of studies that used data mining techniques for detecting healthcare fraud and abuse using the supervised and unsupervised data mining methods. Each of these methods has their own strengths and weaknesses. This article attempts to highlight these areas, along with trends and propose recommendations relevant for deployment. We identified the need for the use of more computationally efficient models that can easily adapt and identify the novel fraud patterns generated by the perpetrators of healthcare claims fraud.
AB - The healthcare industry has become a very important pillar in modern society but has witnessed an increase in fraudulent activities. Traditional fraud detection methods have been used to detect potential fraud, but in certain cases, they have been insufficient and time-consuming. Data mining which has emerged as a very important process in knowledge discovery has been successfully applied in the health insurance claims fraud detection. We performed an analysis of studies that used data mining techniques for detecting healthcare fraud and abuse using the supervised and unsupervised data mining methods. Each of these methods has their own strengths and weaknesses. This article attempts to highlight these areas, along with trends and propose recommendations relevant for deployment. We identified the need for the use of more computationally efficient models that can easily adapt and identify the novel fraud patterns generated by the perpetrators of healthcare claims fraud.
KW - Assessment
KW - Fraud detection
KW - Healthcare
KW - Supervised learning
KW - Unsupervised leanring
UR - http://www.scopus.com/inward/record.url?scp=85056909366&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-02849-7_29
DO - 10.1007/978-3-030-02849-7_29
M3 - Conference contribution
AN - SCOPUS:85056909366
SN - 9783030028480
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 320
EP - 330
BT - Ubiquitous Networking - 4th International Symposium, UNet 2018, Revised Selected Papers
A2 - Pollin, Sofie
A2 - Alouini, Mohamed-Slim
A2 - Sabir, Essaid
A2 - Boudriga, Noureddine
A2 - Rekhis, Slim
PB - Springer Verlag
T2 - 4th International Symposium on Ubiquitous Networking, UNet 2018
Y2 - 2 May 2018 through 5 May 2018
ER -