English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Insurance companies must manage millions of claims per year. While most ofthese claims are non-fraudulent, fraud detection is core for insurancecompanies. The ultimate goal is a predictive model to single out the fraudulentclaims and pay out the non-fraudulent ones immediately. Modern machine learningmethods are well suited for this kind of problem. Health care claims often havea data structure that is hierarchical and of variable length. We propose onemodel based on piecewise feed forward neural networks (deep learning) andanother model based on self-attention neural networks for the task of claimmanagement. We show that the proposed methods outperform bag-of-words basedmodels, hand designed features, and models based on convolutional neuralnetworks, on a data set of two million health care claims. The proposedself-attention method performs the best.

A Self-Attention Network for Hierarchical Data Structures with an Application to Claims Management