Using data is nothing new to insurance carriers and actuaries. Yet the boom in advanced analytics is driving fundamental change.
According to Transforming into an Analytics-driven Insurance Carrier, a February 2016 perspective from the minds of McKinsey & Company, although insurance carriers and actuaries have been using analytics for decades, advanced analytics has emerged as a hot topic in the media and at industry conferences in recent years.
Executives at large and small carriers alike have been building centers of excellence (COEs), with dedicated staff focused on advanced analytics, also known as data science. These investments have delivered successes in some areas, including the use of claims modeling in workers’ compensation, catastrophe modeling in property insurance, sophisticated rating algorithms in personal auto, and fraud identification in both property-and-casualty (P&C) and life-insurance claims, according to McKinsey & Company. What's more, progress has been slower in other lines of business, such as general liability, most specialty lines, and other elements of life insurance.
Overall, carriers have seen mixed results from newly established COEs; there have been clear wins in some cases, while in others, the jury is still out. However, industry executives broadly agree that advanced analytics can be used to drive value in insurance. Even many seasoned underwriters have conceded that widespread use of data can yield big benefits.
McKinsey & Company:
4 Stages of Advanced Analytics Adoption
- Building insights. Initially, companies develop models that demonstrate how analytics can add new insights and deliver clear added value. However, these models are often developed in isolation from the business, and the company struggles with frontline adoption.
- Capturing value. As the analytics function matures, model builders work closely with frontline staff, who become involved in the nuts and bolts of building the model. The focus shifts from developing models to their adoption, and the models begin to come to life. Even if their insights are not fully applied, the models are seen as tools that enhance, rather than hamper, decision making.
- Achieving scale. The company has put in place a COE and established mature and transparent processes related to the COE’s work and the value it delivers. The COE also has a clearly defined process for bringing analytics solutions to market rapidly, working collaboratively with IT. An established set of centralized capabilities is emerging, including third-party-data procurement, model libraries and code sharing, and analytics-talent attraction and retention. Clear analytics leadership has been established within each major business unit and function.
- Becoming an analytics-driven organization. Analytics becomes the backbone for conducting business, shifting from an enabling role to one that is central to the business, and the impact of analytics is measured as part of core business results. Analytics drives underwriting, product development, claims, and distribution, and barriers between siloed functions dissolve. The system becomes more complex, with greater involvement of third parties. The talent strategy for these organizations focuses on analytic skills.
As the COE scales up, senior management makes it a critical corporate priority, paying close attention to the portfolio of initiatives and gaining a basic understanding of how the initiatives have achieved tangible impact.
As part of the annual planning cycle, executives personally encourage line leadership to contribute proactively to the pipeline of analytics ideas, and the success of analytics initiatives becomes measured as a part of performance management. To fulfill this role effectively, top managers need to build a basic understanding of the techniques, tools, and technologies that drive the use of analytics for leading insurance carriers today.