How can Big Data Analytics be a worth for an insurance company?

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An interview with Alessandro Testa of HDI

We interviewed Alessandro Testa, the HDI responsible of the AEGIS project to have an insight of his context and needs, and certainly his expectations from the AEGIS platform. Further information about the Insurance Demonstrator are available at the Demonstrator Page.
How did the interest for the AEGIS project arise?
HDI Assicurazioni is always looking for new technologies in order to enhance its services. The AEGIS project is an opportunity for HDI to better exploit “the Big Data related world” being part of a building up innovative process and first tester of the AEGIS platform.
Which are the benefits you foresee for your business?
The AEGIS Big Data analytics will allow the company to improve its operational efficiency, with the analysis of growing volumes of structured and unstructured data from both internal and external data sources. The HDI Data Scientists working with the AEGIS platform will be able to create detailed reports for various needs of the company, improving customer support, allowing personalised offers more precise, and pricing strategies. With AEGIS HDI will develop a Mobile App to communicate with their customers and alert and/or support them in case of danger. The analytic tools provided by AEGIS will drive to a more efficient means for business plans and marketing strategies.
Which are from your point of view the main challenges?
The insurance sector is very competitive and nowadays the data analytics are central for the development of an accurate and efficient business plan. Moreover, all of the strategies of the company come from an analysis, but to cover the many features of the market it is necessary to have both of the proper data/datasets and the proper analytic tools. The close collaboration between the HDI Data Scientists and the AEGIS platform developer will be the key point for the identification of the most efficient analytic tools, considering also the type of the report of the analysis that has to be easy understood by the HDI employees with different knowledge and background. The quality of the data will be pursued through the selection of trusted data sources, such as the Italian Authorities web sites (e.g. Protezione Civile), or the Italian official stats websites (e.g. Istat – Istituto Nazionale di Statistica).
HDI customers are the ending beneficiaries of the AEGIS platform analytics and it will be fundamental to involve them during the testing and evaluation phases. Since they have to install the Mobile App and to accept the secondary use of their data, they will be all volunteers.
How will you develop the Insurance Demonstrator?
The insurance demonstrator will be developed in three different scenarios:

  • Personalised early warning services for asset protection
  • Financial impact, customer support and services
  • Marketing strategy and pricing support services

The first and the second scenarios (early and medium demonstrator), arisen from the detection of an event that has already happened (close to real time notification) or is expected, will include the analysis of the financial impact for the company, the pricing strategy and support and services to the customer possibly involved.
The third scenario will exploit the analysis related to the previous versions to better understand the HDI market: historical in-house datasets and open data (mainly from Public Italian Registers) will be analysed, providing to the Company models to enhance and ease the business planning and advertising campaigns.
Which are the main issues related to the scenarios?
The main issue is related to the data treatment: we need some of the data of our customers to perform analysis; we will consider only the data necessary for the success of the specific scenario and we will anonymise them before the upload on the AEGIS platform. Moreover we will consider only customers that gave a specific consent for research purposes, and the only customers that will be contacted will be volunteer.

Blog post authors: GFT+HDI