Unlocking the value of your insurance organization’s data

Recently, there has been a growing focus within the insurance industry on expansion into digital/direct to consumer distribution channels, and a focus to make the online customer experience more streamlined and seamless. Over the course of the past 5 to 10 years, there has been a notable decline in customer loyalty because of the increase in competition due to price or other differentiations. This has subsequently led to a need for improved data discovery, or machine learning, and predictive analytics related to customer segmentation and identifying the ideal customer target to improve customer acquisition.

Over time, we will begin to see changes in the people and risks that we are insuring thanks to technological innovations like artificial intelligence and autonomous vehicles. These changing “customers” will drive an evolution of insurance products to meet the future operating model and stay competitive within the industry.

As organizations look for ways to optimize their risk selection and underwriting processes, adapting to and utilizing different data assets will help ensure your organization has a complete picture of your risks. There will be an increased use of “insurance adjacent data” in rating and underwriting and leveraging actual risk data for more accurate pricing and underwriting decisions. As always, there is a continued focus on frictionless and seamless experiences – most of which are delivered through utilizing data to push your organization forward and minimizing the need for tedious tasks like data entry.

Mobile technology has become much more prevalent across the insurance industry as many companies have started accepting mobile claims submissions, digital pricing quotes and delivering through an omnichannel experience for customers. This accelerates the opportunity to leverage data assets to produce a variety of advanced analytics.

Leveraging a customer portal is assisting to drive continued engagement. Many insurance organizations are moving away from a transactional customer environment and focusing on a value-added, customer service experience and support environment. Pairing this customer engagement channel with our data assets and emerging technologies such as artificial intelligence and machine learning, you’re able to predict and/or recommend solutions to your customers based on their wants and needs, which will ultimately lead to higher customer retention.

Back-office functions such as finance, actuarial and IT have continuously grown to become more efficient in recent years through process simplification and automation. Simple, more task-based automations are used now, however this will eventually evolve into decision automation by leveraging artificial intelligence, machine learning and data reasoning. Because of recent economic factors, there has been a doubling down among much of the industry on cost effectiveness and rationalization assessments and an enhanced focus on efficiency initiatives and opportunities to automate, outsource or a hybrid model.

Data maturity and organizational transformation often go hand in hand. As your organization gets better at harnessing data, there will be a noticeable transformation in your people, processes and overall performance. Data analytics maturity happens when an organization enhances how it leverages key information to make critical business decisions. The four main stages of data analytics maturity include:

1. Descriptive analytics: All organizations start by viewing their data to determine “what happened.” In this phase, users are analyzing events that have already taken place and their ability to act on those events is minimal. However, this data does offer insights into what to do going forward.
2. Diagnostic analytics: Once you have looked at the data to understand what happened, it is important to understand why it happened. Are there trends in the data that are presenting themselves and need to be addressed? For example, are you able to see that a specific customer, product or territory is performing well? The analysis at this phase must provide a level of detail that allows the end user to draw connections to events and present that information quickly enough to make actionable decisions.
3. Predictive analytics: With predictive analytics, you can look towards the future and understand “what will happen?” This type of insight requires the detailed and timely information created in the diagnostic phase. The tools and methods at this phase will use advanced technologies to both predict and group given outcomes, leveraging both historical data sets as well as current information.
4. Prescriptive analytics: This includes automated decisions made through machine learning and artificial intelligence. An example of these systems is accelerated underwriting. The information moving through those systems moves so fast in such a massive volume that it would be impossible for a human to do the analysis – instead, sophisticated algorithms make them.

Every day, organizations are using their data to make the best, informed decisions possible. Having the right data programs in place is often quite literally the difference between success and failure. In the diagram above, the outer ring, comprised of data strategy and data governance, focuses on the strategic and operational needs an organization has when building a data-driven culture. The inner ring, comprised of data modernization, visualization and advanced analytics, illustrates the technical tools, platforms and models used to execute against the strategies and policies created in the outer layer.

Data strategy is much bigger than adopting a tool, it’s about building a strong and lasting capability within your organization. Knowing what to measure, having processes in place for measuring efficiently at scale, organizing the right people and implementing the right tools and technology are all central to designing an effective data strategy. The benefits can be felt across your entire organization and you will be able to create focus and efficiency, make better decisions and enjoy a stronger, more informed market advantage.