Data has become a hot topic for insurers over the past few years. While I think it is normal that insurers focus more on data and analytics, I don’t think it is a surprise to observe this trend. Indeed, the insurance business has always been about identifying relevant data, turning it into valuable information and making optimal business decisions using this information for instance in customer segmentation, pricing, underwriting and so on. So, how can we explain the current growing interest in data and analytics? We believe that there are three factors at play.

1. Extracting value from new data sources
Today, insurers can tap into multiple sources of customer and risk relevant data and this was not the case 10 years ago. In contrast to their traditional approach of collecting customer data internally, insurers are now able to acquire and exploit external data sources with or without their customers’ consent.

How important do you think leveraging the following data sources is nowadays for insurance businesses (underwriting, sales, customer experience)? (In % of respondents; n=144)

Source: Leveraging Consumer Data and Smart Technologies in Insurance: Mind the Gap!, Celent, January 2017

The challenge faced by insurers is not solely about the type of data used, but also how they plan to integrate any new external data sourced into their current systems and business processes. Typical questions that need to be addressed include: Do you need to work with data aggregators or specific IT vendors who can complement their internal data with additional data sources? Do you need to transform the relationship you have with customers about their data, and explore ways to make it easier for them to share it with you through app permissions and devices (such as activity trackers, exercise data, health data, etc.)? Do you need to rethink IT architecture to allow for external data sources integration? Are there regulations that you have to comply with to leverage certain types of data?  Where do you choose to draw line on transparency with how data is being used for improving the offer versus identifying fraud?

2. Becoming proficient in new modelling techniques and tools
There are a multitude of technology choices from which insurers can make a better job of analysing data. At Celent, we have observed a growth in the number of vendors offering tools and expertise in data analytics targeted to financial services and particularly insurers. Simply, for our purposes, we categorize them as either: data storage, data quality, reporting, data visualisation, artificial intelligence and advanced analytics (see: Data Vendors in Insurance: A Snapshot).

Even since writing this report, we’ve witnessed a further increase in the supply. Not only are technology solutions becoming more numerous but they are also becoming more specialized, offering sophistication in machine learning, logical reasoning, live field testing, etc. These new more specialized solutions are fast becoming a source of competitive advantage. Notably, they allow insurers to make more informed and relevant decisions around sales and marketing, pricing, underwriting, claims fraud mitigation and other domains.
So, in addition to the question of how to integrate external data, there is also the question of which technologies and tools to use in order to transform data into valuable information – in a market of solutions that are expanding.
Furthermore, on top of this, insurers also need to consider the crucial human element – that is, what new internal skills and capabilities do you need to build or source in order to take full advantage of the data opportunity?  And, how do you stay ahead of market developments?

3. Acting with speed
There is another factor required to help an insurer gain competitive advantage through data and analytics, and that is ‘speed’. This may be the most critical factor of all. The days of batch processing are over and we are fast entering a world of temporary advantage where the time value of data may need to be measured in nano-seconds.  Real-time fraud detection and aggregator-led dynamic pricing are two such areas where speed is already critical today.
Insurers should increasingly expect a world where sophisticated modelling technologies are cheap and easily accessible. In such a world, exploiting all sorts of data, wherever it comes from, becomes a “must have” for doing business. Massive compute power is now available to the smallest new market entrant within minutes through cloud technologies and automation. Size and scale is no longer the advantage that it once was.

Final Thoughts
Overall, insurers need to think about their data strategy keeping in mind these three factors and trying to anticipate the direction that the market will take. Of course, there are many more detailed questions behind these three factors (I have just listed a few of them here). As the technology quickly evolves, there is no alternative to taking the time to carefully identify the challenges involved and determine the necessary actions to invest finite resources and time when it comes to data.