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 Memorynetworks and Robust
Principal Component Analysis. Our
experimental results demonstrate
promising outcomes in identifying
fraudulent records