An explainable attention network for fraud detection in claims management

DOI10.1016/j.jeconom.2020.05.021OpenAlexW3091163231MaRDI QIDQ2673178

Helmut Farbmacher, Leander Löw, Martin Spindler

Publication date: 9 June 2022

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2020.05.021

zbMATH Keywords

embeddingsmachine learninghealth insurancefraud detectioncategorical datadeep learningclaims management

Mathematics Subject Classification ID

Statistics (62-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)

Uses Software

• word2vec
• Adam
• Tensor2Tensor
• BERT

Cites Work

• Unnamed Item
• Unnamed Item
• Mixtures of \(t\)-distributions for finance and forecasting
• A neural network demand system with heteroskedastic errors
• An alternative approach to estimating demand: neural network regression with conditional volatility for high frequency air passenger arrivals
• Optimal contracts and competitive markets with costly state verification
• Multilayer feedforward networks are universal approximators
• Statistical fraud detection: a review
• Detecting Management Fraud in Public Companies
• Optimal Auditing with Scoring: Theory and Application to Insurance Fraud
• Artificial neural networks: an econometric perspective^∗
• Learning representations by back-propagating errors
• Random forests
• Statistical Medical Fraud Assessment: Exposition to an Emerging Field

This page was built for publication: An explainable attention network for fraud detection in claims management