Is Your Insurance Company on the Cutting-Edge of Data Analytics?

Insurance companies have an incredible amount of data. Many leading organizations are harnessing the power of their data and the insights that can be uncovered when the team, the infrastructure, and the commitment are in place to make more strategic decisions in a more efficient manner. 

The insurance-focused Johnson Lambert advisory and audit teams regularly hear from our clients and contacts in the industry on current and future plans of insurers’ data analytics initiatives, and we share here a summary of our recent observations.

How Insurance Companies are using Data Analytics 

Insurance companies are focusing on analytics to differentiate themselves in the market, mitigate risk, and improve efficiency. Examples include:

• Underwriting decision support to move from after the fact analysis to decision support for policy selection and pricing
• Claims trend analysis and workflow efficiency
• Enhance the customer experience through workflow prioritization and automation
  • Workflow prioritization to ensure teams are focused on the items that drive the highest revenue, or are highest risk
  • Automation to eliminate manual, cumbersome tasks that may be subject to error 
• Enhance predictive analytics capabilities and robust operational dashboards to support all levels of the organization, especially enterprise risk management activities 
• Movement toward continuous auditing for Internal Audit functions 

Keys to Success

Companies with a clear business objective driving their analytics initiatives are successful. These companies understand that analytics will support differentiation, and they are able to identify the resources and methods needed to advance their initiatives. A clear business objective drives the talent, data, supporting tools, and process requirements. 

Partnership with the business leadership team is also critical to define your company’s analytic requirements: 

What questions do the business leaders need answered?

How do they make decisions consistently?

How do you get the data to the people that need it, when they need it and in an easy-to-digest format?

If the business leaders are not engaged from the beginning, adoption may be poor and eventually result in investments that do not pay off, frustration, and wasted time. 

Other factors for success are patience and persistence, which support team learning and user adoption. 

Creating A Unified Team

The business objective will determine the team resources required and how to get started. The type of candidate to lead the team and the support structure depend on the goals you are trying to accomplish and the desired timeline. 

In many cases, an insurance company’s Chief Actuary leads the Data Analytics team, which is separate from the Actuarial team. Some teams report directly to the CFO and provide support to the business. Other teams have one group focusing on technology and data and another group of business analysts that work with the business to define reporting requirements. In most cases, a core analytics team is supplemented by consultants to further mature the processes and provide subject matter expertise. 

A common theme is resource constraints and the ability to retain qualified personnel. Many companies are transforming their systems and retiring legacy systems. This limits the capacity of IT to support additional requests, like those from Internal Audit. 

Challenges

The number one challenge is access to data. Most companies have the data, but do not have easy access to the data. 

In some cases, systems may not be capturing the data needed to support analytics, and custom fields are needed. Data may also be buried in notes, pdfs, or unstructured data. 

A good deal of data wrangling may be needed to get clean data in one place from multiple data sources. Data validation is a critical process, and the foundation is strong data governance. This process includes a formal data dictionary with agreement across the company on data definitions. 

Tools To Solve Data Headaches

Even once you have clear objectives, the right team established, and you’ve figured out how to access clean data, you also need to ensure you have the right tools to optimize the data and to best explore the insights your data can provide you. Example tools supporting data analytics initiatives that may connect to enterprise data warehouses or obtain data extracts from source systems:

• Programming languages including SQL, R and Python 
• Power BI, Cognos, and Alteryx are common analytics tools we see clients utilizing; ACL was a popular tool for audit functions, but many teams have stopped using this tool due to lack of technical skills and experience
• Power BI and Tableau are the primary visualization tools in use 
• Excel continues to be one of the most popular tools used throughout the industry 

As you progress in your data analytics journey, keep these trends in mind to better guide your data analytic journey, so that you can make sure you are realizing the most value from your organization’s investment of time, talent, and money.

The Johnson Lambert Differential

Is your insurance company looking for guidance on how to get started with capitalizing on the power of the data at your fingertips? The Johnson Lambert team can demonstrate some of our top uses of analytics tools to support your internal control programs. Contact us to learn more and schedule a demo.

At Johnson Lambert, we are committed to providing our clients with the highest quality of service. For 35+ years, we have developed a niche focus to become experts in the unique aspects of IT risk, and control processes. Our client service and engagement models focus on your success. Our team delivers high quality project management skills and methodology.

For other ways we can support your business through changes, challenges, and opportunities, learn more about Johnson Lambert’s advisory and consulting practice.