Mining Community Based Health Insurance for Fraud Detection Using Data Mining Techniques

ZELALEM, MENGISTU

dc.contributor.author	ZELALEM, MENGISTU
dc.date.accessioned	2022-03-18T07:13:19Z
dc.date.available	2022-03-18T07:13:19Z
dc.date.issued	2021-09
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/13224
dc.description.abstract	Community-based health insurance schemes (CBHIs) apply the principles of insurance to the social context of communities, guided by their preferences and based on their structures and arrangements. CBHIs can help communities manage health care costs and provide access to basic health care for the poor and other vulnerable groups. Typically, CBHIs are organized and managed by a local community organization. The CBHI plan establishes agreements with various health providers, thereby forming a network of facilities. Most schemes cover basic health care services (e.g., antenatal care, deliveries, and child health care) and family planning services, while some schemes may also cover costs of hospital treatment. Insurance fraud is an act that can be seen in different insurance types including CBHI insurance. Fraud in the case of CBHI is done by misrepresenting facts to get unauthorized benefit from the expenses covered under CBHI in the society. Globally companies are spending high amount of claim costs due to insurance fraud. It is a concern for companies to have a system that could differentiate frauds from incoming claims. Data mining tools and techniques can be applied in different fields one of which is fraud detection. This research is conducted for the purpose of testing the applicability of data mining techniques in detecting fraud suspected CBHI claims in the case of west gojjam zone. A six step hybrid process model is used to guide the entire knowledge discovery process. J48 decision tree and Naive Bayes classification algorithms are used to build predictive model. Several experiments are conducted and the resulting models show that the J48 decision tree is found to work well in detecting fraud with 93.06% c l a s s i f i c a t i o n accuracy. A prototype is developed based on the rules extracted from the J48 decision tree model. Finally recommendations and future research directions are forwarded based on the results achieved.	en_US
dc.language.iso	en_US	en_US
dc.subject	INFORMATION TECHNOLOGY	en_US
dc.title	Mining Community Based Health Insurance for Fraud Detection Using Data Mining Techniques	en_US
dc.type	Thesis	en_US