Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford's Law Distributions
Abstract
Benford’s Law [1] specifies the probabilistic distribution of digits for many commonly occurring phenomena, ideally when we have complete data of the phenomena. We enhance this digital analysis technique with an unsupervised learning method to handle situations where data is incomplete. We apply this method to the detection of fraud and abuse in health insurance claims using real health insurance data. We demonstrate improved precision over the traditional Benford approach in detecting anomalous data indicative of fraud and illustrate some of the challenges to the analysis of healthcare claims fraud.
Cite
Text
Lu and Boritz. "Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford's Law Distributions." European Conference on Machine Learning, 2005. doi:10.1007/11564096_63Markdown
[Lu and Boritz. "Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford's Law Distributions." European Conference on Machine Learning, 2005.](https://mlanthology.org/ecmlpkdd/2005/lu2005ecml-detecting/) doi:10.1007/11564096_63BibTeX
@inproceedings{lu2005ecml-detecting,
title = {{Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford's Law Distributions}},
author = {Lu, Fletcher and Boritz, J. Efrim},
booktitle = {European Conference on Machine Learning},
year = {2005},
pages = {633-640},
doi = {10.1007/11564096_63},
url = {https://mlanthology.org/ecmlpkdd/2005/lu2005ecml-detecting/}
}