IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MIXTURE OF CLINICAL CONCEPTS

Abstract

Patients depend on health insurance
provided by the government systems,
private systems, or both to utilize the
high-priced healthcare expenses. This
dependency on health insurance draws
some healthcare service providers to
commit insurance frauds. Although the
number of such service providers is
small, it is reported that the insurance
providers lose billions of dollars every
year due to frauds. In this paper, we
formulate the fraud detection problem
over a minimal, definitive claim data
consisting of medical diagnosis and
procedure codes. We present a solution
to the fraudulent claim detection
problem using a novel representation
learning approach, which translates
diagnosis and procedure codes into
Mixtures of Clinical Codes (MCC). We
also investigate extensions of MCC
using Long Short Term Memory
networks and Robust Principal
Component Analysis. Our experimental
results demonstrate promising outcomes
in identifying fraudulent records.