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Advanced Federated Learning for Insurance Applications (ART/362CP)

Project Title:
Advanced Federated Learning for Insurance Applications (ART/362CP)
Project Reference:
ART/362CP
Project Type:
Platform
Project Period:
30 / 03 / 2023 - 30 / 06 / 2024
Funds Approved (HK$’000):
7,085.035
Project Coordinator:
Dr Arvin Wai-kai TANG
Deputy Project Coordinator:
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Deliverable:
Research Group:
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Sponsor:

Insurance Authority

Description:

Advanced Federated Learning for Insurance Applications (AFLIA) is a fintech approach to building a new insurance data insight platform which can provide additional insights from various sources of personal data for the insurance industry to create new products, e.g., personalized health premiums, unserved segments, etc., for Hong Kong citizens. The traditional approach based on data from policyholders could not create changes in the industry. The alternative data from other parties for health data usually reside in different service providers, such as the health industry and IoT devices and all of them involve privacy issues in sharing. The project aims to (1) publish a white paper with the IA for the insurance industry, (2) Build an insurance data insight platform to provide insurers with additional insights, which powers product innovation and facilitates financial inclusion for the insurance sector. The outcome of this project (1) Provides tools for insurers to power product innovation and facilitate financial inclusion in various segments (2) The advanced federated learning platform for confidential data will be the base for the insurance industry to serve various segments. (3) This project’s experiences and results can be shared with members of the International Association of Insurance Supervisors for international InsurTech development.

Co-Applicant:
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Keywords:
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