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How will Big Data impact the insurance profession?

Everyone is talking about Big Data. The term has existed for years, but over the past few months we have been hearing it everywhere. What is Big Data? What can we gain from it? How should insurers take it into account in order to adapt their offers? 

How will Big Data impact the insurance profession?

What is Big Data?

The Definition of Big Data

Every day, we generate a certain amount of data (approximately 2.5 trillion bytes*). Whole segments of our lives and our world are now digitally stored (geolocation, web browsing, email, social networks, bank transactions…). We can legitimately talk about the “digitalisation of the world”.

This data is processed via minimal-cost storage solutions together with software that uses algorithms**. The analysis of these volumes of data makes it possible to pick out the relevant information and thus make comparisons.

How is Big Data different from traditional data? 

Big Data has existed for a long time! According to Zouheir Guedri, founder of Data&Data (a Village by CA start-up specialised in counterfeit detection),

"10 years ago, there wasn’t any Big Data technology, yet the term already existed. At the time, we used ‘Big Data’ to talk about the challenge of real-time processing of data that was very large and diversified. But we did not know how to do it. Today with the new technologies, this problem was transformed into an opportunity. All the former obstacles have disappeared."

Big Data is different from traditional data (first and last name, age, address, etc.) in that there has been a massive input of more detailed data which is more personal and very diversified (text, sound, video, tweet, etc.).

Data use and privacy

As the CNIL explains, "it would seem that ‘big data’ definitely presents a unique aspect in terms of its scale, yet it is an extension of the classical processes of cross-referencing data for profiling purposes”***. Big Data is therefore subject to CNIL requirements.According to a PwC study, over half of French people would be willing to provide their general insurance provider with additional personal data, specifically data pertaining to their lifestyle, if they were offered a more personalised cover solution.

The challenges of Big Data for insurance providers 

Data in Insurance

However, the fact that multiple products are sold (home, health, life insurance, etc.) offers insurers the opportunity to cross-analyse a richer set of data.

Furthermore, only insurers have such a clear understanding of the risks (history of claims in terms of frequency and amount). 

Data for improving client relations

Big Data enables the insurer to optimise the client relationship:

  • The insurer can precisely determine a client’s potential interest in a product. He or she can detect the important events in the client’s life in order to propose an offer or offers adapted to the circumstances at each step. The result is an enriched and personalised client relationship which is more proactive in anticipating the client’s real and practical needs.
  • This will also provide a clearer picture of the insured risk. For example, the insurer can detect behaviour patterns that present risks for the client, or fraudulent behaviour patterns. Enriched explanatory variables will make it possible to adapt pricing.
  • The insurer will be able to create new products and new services for clients. This will make it possible to detect danger areas, for example, and inform the client, thus acting preventively to protect the client from a problem, such as an accident or a claim.

This proactive aspect of the insurer’s role - acting before the fact - could reinvent the way we do business. The insurer will no longer be there merely to provide compensation, but also to reduce the client’s risk of accidents, which will lead to a new, personalised relationship between the insurer and insured.

*Source: IBM

** Algorithm: A process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer. Source: Oxford English Dictionary

***Source: CNIL

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