By Dax Craig, CEO and President, Valen Technologies
In today’s fluctuating market, there is increasing pressure on insurance operations to achieve profitability. A topic we find ourselves talking about frequently with insurance executives is investment income. Many carriers are facing increasingly complex challenges in growing surplus because of the low interest rate environment. And, most haven’t built their companies to produce a combined ratio of fewer than 100. With surplus shrinking, carriers are talking about the need to reengineer their operations and equip their underwriters with the necessary tools to create profitability. That conversation naturally leads to evaluating the efficacy of technologies now available to more accurately evaluate and price risk. Dowling & Partners Securities, LLC recently released a special report on predictive analytics and said that the “use of predictive modeling is still in many cases a competitive advantage for insurers that use it, but it is beginning to be a disadvantage for those that don‘t.” The question for many remains: Is this right for my organization and what do we need to do to use analytics successfully?
There are important considerations to review internally when deciding whether to incorporate predictive analytics across your organization:
- Ensuring senior level commitment
- Determining if your organization is equipped to utilize predictive analytics
- Analyzing what information and resources you have to build predictive models
- Having the right IT resources available for predictive analytics
Senior Level Commitment
Whether you are the CEO rallying your executive team or you are presenting a business case to the CEO, it’s imperative to agree upon success metrics and set up a proof of concept to demonstrate the expected return on investment. You can measure success from advanced data and predictive analytics with loss ratio improvement, pricing competitiveness, accuracy, efficiency, or premium growth as examples. Whatever metrics best suit your organization, securing agreement from senior managers is imperative before deciding to move forward. Also critical is outlining the plan and process for how to integrate the model within the existing underwriting workflow. These questions can facilitate productive conversations:
- How will you prove that a predictive model will work?
- What are the agreed upon metrics to measure success?
- What is the plan to integrate the model within your existing underwriting workflow?
- What management reporting will you put in place?
In addition to having senior management commitment, it is important to ensure that your organization is equipped to utilize the results of predictive modeling and analytics. Take into account your organization’s culture and determine how to create an environment that encourages adopting analytics as a key underwriting tool to make better informed decisions.
- What will work for your culture: A top-down or bottom-up approach?
- What is the level of transparency you want to provide?
- What is your training program to integrate the model during the pilot and full implementation phases?
- Will you educate and gain support from your agency partners? If so, how?
Evaluating and organizing your data assets is another integral part of incorporating predictive analytics. By researching and exploring various models and what goes into them, you can assess your own data assets and resources for building predictive models. Not only must you examine using external as well as internal data, but you will need to go step-by-step to determine:
- Do you plan to build a predictive model internally or use a consultant to build a custom model?
- Do you plan to use a vendor that builds predictive models on industry-wide data?
- How will you evaluate the amount of data you need to build a predictive model?
- Will you use external data sources in addition to your internal data? If so, which ones and how will they help build a more robust model?
One of the biggest stumbling blocks is available IT bandwidth and deciding when to team up with an external vendor who can take a big load off of IT and still insure a seamless integration. Use these questions to define IT requirements:
- Are you planning to use internal resources to implement a model?
- How will underwriting workflow change?
- Will you build the scoring infrastructure internally or leverage vendor solutions?
- Are you planning on using an external vendor to build a model? If so, how much internal IT resources will the vendor require?
- How long will it take to get to market based on the IT resources required internally and externally?
With this being a critical time for carriers to improve operational efficiencies and underwriting profitability, predictive modeling is one technology that can help. Beginning this process can seem daunting at first, but if you follow a process and drive productive conversations with your team, you can start with manageable steps to successfully incorporate.
As mentioned, a critical step in moving forward with predictive analytics is getting C-level commitment by providing answers and proof of concept. I’ll be exploring this in more detail in my post next week. We have several more questions you can use as a complete checklist to assist with key decisions. Simply visit the Valen website to download the full Organizational Readiness for Predictive Analytics checklist.
About Dax Craig
Dax Craig is the co-founder, president, and chief executive officer of Valen Technologies. 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 Technologies, Inc.
Valen Technologies, Inc. provides turnkey predictive analytics to the property and casualty insurance industry. Valen’s proprietary consortium database, called Valen Network Data, is used to deliver predictive modeling products that improve risk selection, pricing, underwriting, audit, and inspection processes. Valen Network Data is comprised of nationwide data that includes policy-level information for Workers Compensation, Homeowners, Premium Audits, Commercial Auto and BOP, combined with disparate, non-industry data sources carefully mined to maximize usefulness. Our suite of products that includes PropertyRightTM, InsureRight®, UnderRight®, RateRight®, and AuditRight® are delivered in a fully hosted environment. Learn more about Valen at www.valen.com and http://propertyright.valen.com.