Big data in the insurance industry has grown exponentially in the past decade. Before any data scientists came along, actuaries were using giant computers with less computing power than today’s smartphones to calculate mortality rates. With the continuous advancements in the machine learning (ML) and artificial intelligence (AI) spheres, insurers now have access to more data than they can consume and analyze properly.
As with all new changes, there are positive and negative effects on society. To ensure regulations and governing activities sufficiently protect consumers from harm, the National Association of Insurance Commissioners (NAIC) created the Big Data (EX) Working Group along, with the Artificial Working Group of the Innovation and Technology (EX) Task force.1 These are two examples of checks and balances we are seeing to protect consumers from Big Data that are doing more harm than good.
The examples above will only continue to get more efficient and accurate, but as an organization, how do you tap into your full potential at scale? A recent webinar, "The Future of Data Science”2 gave an interesting viewpoint on how insurers require a holistic, end-to-end transformation for the organization, not solely for actuarial or analytic teams. This requires not only a data governance framework, but also a people and process viewpoint. Insurers must move from an analytics garage where big data is prioritized but activity is siloed, to an analytics factory, leveraging data at scale across all decisions. Below are three pillars to the outline of an analytics factory:3
1. Vision: Set a vision and strategy including new business models that include areas, such as cloud analytics, agile test-and-learn pilots, and real-time insights.
2. Use Case: Build use cases that use commercial levers (cross-/upsell, acquisition, risk protection, etc.) and internal optimization (dynamic B2C pricing, workforce planning).
3. Foundations: Cultural design with strong technical and businesspeople, not just in the machine room, but also in the board room to ensure an end-to-end and financial impact perspective.
The right people need to be in place for this factory to work and we have seen an increase in data positions being filled at major insurers4 to support this. To create a good foundation, insurers must actively cultivate this blend of skills from data scientists to analytics engineers. To bring all these skilled resources together and ensure alignment on vision, analytics-driven leadership needs to be in place to lead and choose the right use cases to accomplish quick wins that prove potential and gain org-wide buy-in.
In summary, we are living in a world where every customer action and decision is being tracked for one reason or another. Organizations, like the Big Data Working Group, will continue enforcing regulations to make sure customers’ privacy is protected and used with their best interest in mind. Insurers need to look internally to unlock their true potential from the data they have at their disposal but don’t have the necessary structure, process and/or tools to leverage.
NEOS has an extensive track record of successfully helping companies deliver organizational transformations in the data space. We can help you get the right people, with the right skills, and into the right roles to drive change, reduce risk, and ultimately get your organization to a point where analytics is in the DNA of the entire organization.
1. https://content.naic.org/cipr_topics/topic_big_data.htm
2. Sudaman Thoppan Mohanchandralal, Regional Chief Data & Analytics Officer at Allianz Benelux
3. https://www.datacamp.com/resources/webinars/future-of-data-science-in-insurance-1