@inproceedings{6c51b0bf68bf40b4953ba7d75890a004,
title = "Embedding for anomaly detection on health insurance claims",
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.",
keywords = "Embedding, Health insurance claims, Machine learning, Representation learning",
author = "Jiaqi Lu and Fung, {Benjamin C. M.} and Cheung, {William K.}",
note = "Funding Information: This research is supported by the Engage Grants (EGP 529904-18) from the Natural Sciences and Engineering Research Council of Canada (NSERC) with McGill REB file number: 146-0818.; 7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020 ; Conference date: 06-10-2020 Through 09-10-2020",
year = "2020",
month = oct,
doi = "10.1109/DSAA49011.2020.00060",
language = "English",
series = "Proceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020",
publisher = "IEEE",
pages = "459--468",
editor = "Geoff Webb and Zhongfei Zhang and Tseng, {Vincent S.} and Graham Williams and Michalis Vlachos and Longbing Cao",
booktitle = "Proceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020",
address = "United States",
}