WTW sharing data in the Insurance value chain

Global risk management company pilots a data trust to facilitate multi-party data sharing with clients.

The Problem

The insurance industry is a data-driven sector: Actuaries use information about assets, customers, environmental factors and historical loss data to assess risk. Advancing data technologies and the availability of myriad new sources of data offer the potential for dramatic improvements in efficiencies and the sharing of data between industry participants will be a key factor. Risk management and insurance company, WTW states that data sharing between companies ‘has the strategic potential to reshape the insurance value chain’ . They cite examples such as the potential for fraud-prevention if industry-wide claims data is pooled, and the potential to build more robust risk models through sharing environmental risk data.

A recent survey of re(insurance) industry experts found that a majority of respondents believe that there will be significant change in data sharing in the industry within three to five years. However, WTW identified various barriers to sharing data in the insurance industry such as issues of commercial confidentiality, absence of multi-lateral legal frameworks, challenges in implementing data privacy regulations and a lack of technological solutions.

 

The Solution: WTW insurance data trust

To facilitate multi-party data sharing with its clients, WTW set up a data trust to act as a fiduciary for the shared data. The partners developed an ethical and legal framework for the data sharing and employed a distributed data architecture to allow the data to reside with the data-owning companies.

The WTW activities highlight the potential within the insurance industry to transform commercial efficiency through data sharing. In this case study WTW and partners took on various data stewardship roles themselves but if data sharing is to become widespread across the industry there will be a need for data stewardship service providers.

 

Potential Benefits

  • Fraud prevention
  • Improved risk models
  • Operational efficiencies
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