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Whitening-based Adversarial Bi-Regressor for Cross-platform Insurance Claims Prediction

Year: 2025

Type: conference-paper

Abstract: Insurance claims prediction aims to predict claim amounts for auto-adjusting premiums. Deep learning has shown great potential in this field but typically requires large amounts of labeled data. In our cross-platform insurance claims business, obtaining labeled data is highly challenging due to privacy and confidentiality concerns. Our business focuses on providing trained models to partners (other insurance companies) without accessing their data labels. This creates a challenge: applying models trained on our private data directly to partners' data. This problem falls under Unsupervised Domain Adaptation (UDA). Additionally, since we aim to predict claim amounts, it specifically belongs to the Unsupervised Domain Adaptation Regression (UDAR) task.

DOI: 10.1145/3701716.3715455

Authors: Xiaoyu Liu, Yiqing Wu, Dingyuan Zhu, Yifan Wu, Daixin Wang, Xiaobo Qin, Fuzhen Zhuang

Institutions: Beihang University, Chinese Academy of Sciences, Institute of Computing Technology

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: National Natural Science Foundation of China, Universitas Brawijaya, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities

Awards: G245388120, G5191158251, G5251169464, G6617559040, G7273531870, G8136708401

OpenAlex ID: W4411549681

FWCI:

Cites: 15

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Related to: 10