TY  - CONF
AV  - none
ID  - scholars8531
TI  - Framework for the identification of fraudulent health insurance claims using association rule mining
SP  - 99
KW  - Association rules; Crime; Health insurance
KW  -  Data-mining techniques; Fraud detection; Health insurance fraud; Insurance claims; Insurance companies; Insurance frauds; Insurance providers; Medical treatment
KW  -  Data mining
N1  - cited By 19; Conference of 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017 ; Conference Date: 16 November 2017 Through 17 November 2017; Conference Code:134594
N2  - Deliberate cheating by concealing and omitting facts while claiming from health insurance providers is considered as one of fraudulent activities in the health insurance domain which has led to significant amount of monetary loss to the providers. In view of the above, careful scanning of the submitted claim documents need to be conducted by the insurance companies in order to spot any discrepancy that indicates fraud. For this purpose, manual detection is neither easy nor practical as the claim documents received are plentiful and for diverse medical treatments. Hence, this paper shares the initial stage of our study which is aimed to propose an approach for detecting fraudulent health insurance claims by identifying correlation or association between some of the attributes on the claim documents. With the application of a data mining technique of association rules, this study advocates that the successful determination of correlated attributes can adequately address the discrepancies of data in fraudulent claims and thus reduce fraud in health insurance. © 2017 IEEE.
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781538607909
Y1  - 2017///
EP  - 104
VL  - 2018-J
A1  - Kareem, S.
A1  - Ahmad, R.B.
A1  - Sarlan, A.B.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047435296&doi=10.1109%2fICBDAA.2017.8284114&partnerID=40&md5=32f134dd6a775decec634b9c90eb8b70
ER  -