Show Me Your Claims and I'll Tell You Your Offenses: Machine Learning-Based Decision Support for Fraud Detection on Medical Claim Data
Show Me Your Claims and I'll Tell You Your Offenses: Machine Learning-Based Decision Support for Fraud Detection on Medical Claim Data
Files
Date
2022-01-04
Authors
Matschak, Tizian
Prinz, Christoph
Rampold, Florian
Trang, Simon
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
Health insurance claim fraud is a serious issue for the healthcare industry as it drives up costs and inefficiency. Therefore, claim fraud must be effectively detected to provide economical and high-quality healthcare. In practice, however, fraud detection is mainly performed by domain experts resulting in significant cost and resource consumption. This paper presents a novel Convolutional Neural Network-based fraud detection approach that was developed, implemented, and evaluated on Medicare Part B records. The model aids manual fraud detection by classifying potential types of fraud, which can then be specifically analyzed. Our model is the first of its kind for Medicare data, yields an AUC of 0.7 for selected fraud types and provides an applicable method for medical claim fraud detection.
Description
Keywords
Decision Support for Healthcare Processes and Services,
digital health,
fraud detection,
healthcare,
machine learning
Citation
Extent
9 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 55th Hawaii International Conference on System Sciences
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.