Experimenting with external data: What's the real motivation?

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22 October 2013
Jamie Macgregor
Recently, we held a UK CIO Roundtable on the myths versus realities of ‘Big Data’. The roundtable was made up of a mix of insurers from both the P&C and Life industry. I guess that it will come as no surprise that finding new ways to extract insight from data is a hot topic. In a low interest rate environment, improving results from the core disciplines of sales, underwriting and expense management take on a greater level of importance within many firms, and data is at the heart of this. For many firms, the business drivers for investment in data and analytics have not really changed in recent years, i.e.
  • Risk mitigation – Minimising the frequency and impact of losses, through fraud detection and loss scenarios.
  • Growth / Maintaining market position – Identifying profitable segments and understanding the propensity to take a particular course of action.
  • Improving service – Identifying ways to improve the quality of service through ensuring that the right data is in the right place at the right time to aid decision making.
What has changed, however, is the increased focus on using data to make a difference to business performance coupled with a growing interest in the search for and use of new alternative external sources of data to augment with existing internal data, such as social profiles, health app data, public records, etc. Unsurprisingly, many of the firms represented stated that they were active in experimentation with new external data sources, albeit on a small scale. What I personally found fascinating was when one insurer, having been questioned about the motivation for experimentation in new sources of data, gave the following refreshingly honest answers:
  • Competitive threat – Concern that a competitor or start-up could use data in a way that threatens their current position, rendering the way they underwrite and service customers obsolete. Telematics in auto-insurance and use of public health records & personal health tracker apps for enhanced annuities being two examples.
  • Regulatory threat – Concern that local market regulators may extend anti-discrimination laws to prevent the use of existing rating factors used for pricing risk, such as age. The origins for this concern were triggered by the European Union’s decision to implement the Gender Directive that came into law at the end of last year. (For non-EU citizens, the Gender Directive prevents insurers from using gender for pricing within Europe. Since its implementation, there has been an increased interest in telematics across the region as insurers look to use the data generated to predict driving behaviour).
When I reflect on this further, the insurer’s stance makes perfect sense to me. For years, established players have relied on their scale and the history captured within their systems to provide them with a competitive edge. All of the time that market rules of engagement remain unchanged, the insurer is better off prioritising investment towards making use of the data it already has, rather than place a big bet on a risky source of new data or a new data led propositions for unproven markets. However, in maintaining a capability to search for and then experiment with these new sources of data, this insurer has enabled themselves to be better placed to respond when the threat finally occurs. This is a fast follower strategy (or ‘Fast Second’ to use a phrase coined by Constantinos C. Markides in his book of the same name). For incumbent players in service industries, this strategy can often prove to be highly successful as a response to a disruptive play by a new entrant. Perhaps today more than in recent history, it could be argued that new entrants and small agile competitors using alternative sources of data to gain a competitive advantage are becoming a real threat to established insurers. We have seen early examples within the industry already, such as with the Climate Corporation’s entry into the crop insurance market (and then its subsequent acquisition by Monsanto for an eye watering ~$1B earlier this month), the positioning of personal health apps to assist in managing risk for health insurance, and the growth of telematics for ‘pay how you drive’ insurance models. Once these new non-traditional data sources and data-led propositions start to gain real market traction, the key to success for industry incumbents may not lie in being first but instead in first being aware followed by being the fastest to execute.

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