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3 Reasons Why Insurance Needs Data & Analytics

By Dax Craig, President & CEO, Valen Analytics

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Dax Craig, President & CEO, Valen Analytics

Denver, CO based Valen Analytics is a predictive analytics company that engages in building, hosting, and monitoring predictive solutions for property and casualty insurance companies

In the 1980s, the credit card industry underwent a technological makeover that allowed early adopters of data and analytics to grab profitable market share, while those who clung to traditional models struggled to stay competitive. For example, Capital One revolutionized the industry by leveraging credit score and consumer segmentation patterns to find the best risks. Likewise, Progressive used a similar strategy to find the best risks in what was previously considered a poor risk population – a move that gained them a dominate market share position. Now, the insurance industry faces significant challenges to traditional business models with market forces at work that make analytically-driven approaches to risk selection and pricing more important than ever.

Leveling the Playing Field

Large insurance companies have a distinct advantage that they can build robust databases to discern trends within discrete segments of their portfolio. Oftentimes, small and mid-size carriers cannot build the same data assets required for advanced data and analytics, but there are now viable third party resources that allow them to level the playing field.

As carriers leverage internal and external data sources, CIOs serve a critical role in ensuring data quality and governance. Important business decisions will be made that require vast amounts of diversified data in order to avoid selection bias. Selection bias occurs when predictive models are built on databases with inadequate volume and sample bias based on a carrier’s risk appetite, both of which will lead to inaccurate conclusions.

Consumer Demographics

At 77 million strong, Millennials are a major market force and demonstrate different buying behaviors than previous generations. This budget-conscious generation chooses urban living, prefers multiple modes of transportation and prefers to rent instead of owning.

It’s important to account for the heightened expectations Millennials have of their insurer.  If you are not able to provide timely quotes, excellent customer service and real-time information, they will look elsewhere and may use social media to vent their frustrations. To accommodate these high expectations, CIOs must consider the right system infrastructure and online tools to serve this demographic and nurture long term customer relationships.

New Competition

According to an Accenture survey, a majority of consumers are open to purchasing insurance from companies other than traditional insurers, including Google and Amazon. Google has a significant advantage that they already have access to a wide berth of personal data.

With the risk of new competition, insurers must utilize the best technology available to stay competitive. Accenture also revealed that 50 percent of commercial lines underwriters desire more intuitive tools that provide information the moment a decision needs to be made. The trend is clear in insurance and across industries – the use of predictive models along with increased use of external data offer significant market differentiation. Carriers are increasing their investment in data and analytics to take the next leap forward.

Next Steps to Consider

If you are considering adopting

analytics, keep in mind that organizations do better when they start with manageable projects and grow the level of analytical sophistication over time.

• Define your organization’s distinctive capability and the direct tie to how predictive modeling will be used to outperform the competition.

• Facilitate a process so that several groups within your organization are exposed to analytics with the goal of developing an enterprise-level approach over time. Success ultimately depends on getting the entire organization using and believing in the power of analytics to drive decisions.

• Then, focus on securing senior level commitment. It takes a proof of concept and solid business case with a defendable ROI to get buy-in at the top.