Embedding for anomaly detection on health insurance claims

Jiaqi Lu, Benjamin C.M. Fung, Kwok Wai CHEUNG

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Properly analyzing health insurance claims data could lead to significant business insights and benefits for health service providers and insurance companies. Yet, health insurance data is often high dimensional and contains complex interleave sequences of claims. Instead of conducting machine learning tasks directly on the raw data, a better approach is performing the tasks on high-quality embeddings of the raw data. Driven by the real business need of Solution Segic Inc., a Canadian technology company in the group insurance industry, we extract health insurance claims embeddings with neural networks in the context of anomaly detection. We propose and thoroughly examine six embedding components that are customized based on different possible assumptions made on the data. One of our proposed embedding components, EC-ReStepRec, significantly outperforms other candidates on two anomaly detection tasks. This is the first embedding study done on health insurance claims for anomaly detection.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020
EditorsGeoff Webb, Zhongfei Zhang, Vincent S. Tseng, Graham Williams, Michalis Vlachos, Longbing Cao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages459-468
Number of pages10
ISBN (Electronic)9781728182063
DOIs
Publication statusPublished - Oct 2020
Event7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020 - Virtual, Sydney, Australia
Duration: 6 Oct 20209 Oct 2020

Publication series

NameProceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020

Conference

Conference7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020
Country/TerritoryAustralia
CityVirtual, Sydney
Period6/10/209/10/20

Scopus Subject Areas

  • Computer Vision and Pattern Recognition
  • Decision Sciences (miscellaneous)
  • Statistics, Probability and Uncertainty
  • Analysis
  • Discrete Mathematics and Combinatorics

User-Defined Keywords

  • Embedding
  • Health insurance claims
  • Machine learning
  • Representation learning

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