| dc.description.abstract | This paper is about a system which applies a modified K
-
Means algorithm[12] to flag out suspicious claims for further
scrutiny has been developed. The Java programming Language and my
SQL database tools were used. The K
-
Means algorithm is
well known for its efficiency in clustering large data sets. However, a major limitation of this al
gorithm is that it works
only with
numeric values, thus the method cannot be used to cluster real
-
wo
rld data containing categorical values. To counter this, data
sets were converted to numeric data whereby ailments were listed and matched with patients. The pre
sence of the ailment was
represented by a one (1) and the absence was represented by a zero (
0). To get the data, a total of 15 insurance companies in
Kenya out of 31 were randomly selected and a pre
-
tested questionnaire was used to collect data. 15 insurance companies out of
31 is close to 50%, which is a very good representative of the entire
population. 67 % of the respondents indicated that the people
involved in the processing of claims were billing for services that were not rendered. The results
also showed that all the
companies had internal control mechanisms to address the problem and
47% of the respondents said the internal controls were
not efficient. 87% of the respondents indicated that
the common member fraud cases involved m
membership substitution
including card abuse. | en_US |
