December 15, 2017

Dax Craig: Entering ‘Phase 2’ of Predictive Analytics: What is it and When is a Carrier Ready?

By Dax Craig, CEO and President of Valen Analytics

Dax CraigMore than half of P/C insurers are using predictive analytics for risk selection and pricing – nearly double its current use in other areas of business. Early adopters are becoming increasingly savvy in their ability to obtain actionable insights and improve underwriting performance. What began – and still is in many cases – a division between “the haves and have-nots” of predictive analytics, will soon become a division between those who use them in one segment of their business, and those who apply them more broadly across the enterprise. Work comp is likely the first line in commercial insurance to face more advanced use of analytics across the board.

The Phase 1 Journey
The majority of carriers who have implemented analytics in work comp for risk selection and pricing are on the same phase of implementation that we refer to as “Phase 1”. This often translates into significant improvements in a portfolio when an insurer begins to use analytics and is equipped with better real-time data, explained in a way underwriters intuitively understand and can put into action. In the lifespan of this phase, carriers tend to focus on three areas.

The first area is attributing analytics to a specific business problem and then using the advantage of more data to either adequately price a risk or drop it from the portfolio. The second focus is on profitable growth, where the carrier can be more competitive with better risks to win over the good business. This helps prevent the insidious threat of adverse selection, where a competitor armed with more data wins the good business, leaving the incumbent carrier unknowingly with the poorer performing policies that are often underpriced. Lastly, now that that the carrier better understands what’s in their portfolio and sees a certain pattern and level of consistency, they begin to standardize risk selection and pricing strategies. As a basic example of this, most carriers using analytics will elect to use straight-through processing for small premium policies that rank in the top 30% of their portfolio, allowing their underwriters to focus on larger premiums and higher risk policies. Some analytics providers allow for business rules to be integrated directly within the models to make this a more streamlined process.

Entering Phase 2
After the low hanging fruit has been tapped and the ROI for predictive analytics has proven out, carriers naturally begin looking for ways to achieve similar results in other areas of their organization. This involves taking data-driven insights and learned tactics already applied in “Phase 1” and using them in other parts of the organization where key decisions are made. Claims triage, claims fraud and marketing are good places to start. The application of analytics in new areas follows a similar approach in terms of the process, implementation, and having a clear measurement for success. What’s unique about entering into Phase 2 is the elevation of a company’s analytics strategy with the overall corporate strategy. It’s a clear signal that a company is ready to evolve into a truly data-driven organization.

But with any new undertaking, especially in the insurance industry, there is often an innate fear of, “when is the right time to move forward, and is it worth the risk?” Here are some indicators that a carrier is ready:

  • A sustainable analytics strategy – Is your analytics strategy from Phase 1 clear and organized enough to be replicated across the enterprise? Were the success metrics and implementation plan understood and agreed upon by the whole organization? Did you set short-term goals and build on them once accomplished? Mastering all three areas of Phase 1 listed earlier is a good indicator the strategy is effective.
  • Justify with ROI – Despite the effectiveness of a single analytics project, getting the C-suite on board can be difficult. This is why the ability to measure and manage every step of a predictive analytics implementation in the first phase is imperative. Showcasing improvement to the bottom line as a result of an initial analytics project instills confidence and sets the stage for future approval in expanding these initiatives.
  • Be sure you have enough of the right data – An important point in both Phase 1 and 2 is to be sure you have an adequate data set so that your model produces accurate results. At Valen, we call this a data first strategy. Whether you are improving an existing portfolio, growing into new markets, or expanding into departments, make sure there is enough data to support building a model. Consult with IT, actuarial or a predictive analytics provider to ensure there won’t be a selection bias or a sample size issue.
  • Using these readiness indicators, carriers can begin to strategize the best time for them to take the leap into the next phase of their predictive analytics implementation. While it requires a larger commitment, most carriers will follow this progression in order to stay competitive and reap increasing benefits.

    About Dax Craig
    Dax Craig is the co-founder, president, and chief executive officer of Valen Analytics. Based in Denver, Colorado, Valen is an advanced data and analytics provider for the property and casualty insurance industry. The company leverages its large consortium data assets to help carriers price insurance policies more accurately and achieve lower loss ratios.

    Prior to founding Valen in 2004, Dax was founder and CEO of Xertex Technologies, which was acquired by global leader in the wireless antenna industry, Centurion Wireless. Dax proceeded to serve as vice president of global business development at Centurion, where he was directly responsible for global business development including sales, market definition, market segmentation, market research, strategic planning, and market development.

    Dax graduated from the University of Tulsa with a bachelor’s degree in business administration and marketing. He earned his MBA in finance from the University of Colorado at Boulder.

    About Valen Analytics
    valen analyticsValen Analytics is an advanced data and analytics provider for property and casualty insurance companies. We work with insurers who are actively looking to improve underwriting profits by driving growth and/or lowering their loss ratio. Our customers are focused on increasing competitive pressures, fighting adverse selection with innovative solutions, and raising awareness for the impending “experience gap” in underwriting with initiatives such as Tomorrow’s Talent Challenge. Our customers span many lines of business including Homeowners, Workers’ Compensation, Commercial Auto and Telematics, Commercial Package, Commercial Property, and BOP. Learn more about Valen at www.valen.com.

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