The New Age of Insurance

Insurers have at their disposal incredible amounts of data, and powerful analytics can turn it into business intelligence better than traditional tools.

data analytics

The world generates 2.5 quintillion bytes of computer data per day. That’s 25 followed by 16 zeros. What’s more, 90% of today’s data was created in the past two years, meaning that big data’s role is massive and growing.

In the insurance industry, all this data positions companies to provide their clients with unsurpassed coverage and service if they can access the information on-demand in practical formats, making it work for everyone’s benefit.

Applying this intelligence can affect business in multiple ways, to:

  • Target customers more efficiently
  • Improve decision-making
  • Lower exposure
  • Discover and implement ways to add tangible value to operations
  • Reduce costs

What’s necessary is on-demand access to data that provides insight and context to business intelligence, information that should not take an advanced degree in technology or systems analysis to interpret results. Analytics designed to thrive in a user experience environment and empower analysts to conceptualize data will serve data-intensive industries like insurance the best.

Addressing complexity

Insurers have at their disposal incredible amounts of data: Intelligence on their customers; demographics by geography, age and gender; market trends; and historical information are just a few of the numerous categories. Promptly gathering this information in helpful formats for employees with varying levels of responsibility and analytic skills is the challenge that insurers and many other organizations face. Powerful analytics can deliver this business intelligence better than traditional tools.

Users need to understand the data and apply it to their tasks. Staff swiveling in and out of different business intelligence tools to perform their assignments is less efficient than using well-designed analytics that allow them to stay within the workstream when gathering, assessing or reporting information.

For example, some auto customer data can be stored on one platform within a property and casualty insurer while claims history is accessible on different software. Vehicle features and values may be drawn from other databases, while driving records need to be pulled from one or many public safety departments from across the country. Such a puzzle is rich in data but complex in a traditional workflow. Having well-designed analytics built into the system simplifies the process of quoting customers, especially in a 24/7 environment.

See also: 3 Digital Customer Service Strategies for 2022

Engaging applied intelligence

Merely providing intelligence is not all that analytics can do.  

Engaging dashboards, visualizations and other delivery methods create a vibrant workspace for the user, offering context to otherwise jumbled or contradictory information. Users adopt attractive tools more extensively, use them more frequently and instinctively ask more of the instrument and the data it delivers. 

Embedding predictive analytics to intelligence gathering strengthens the usefulness of the data for timely decision-making. All these actions increase productivity. 

Analytics allow professionals to apply their individual business talents to tasks without needing a high degree of technical expertise to gather or interpret the data, serving entry-level professionals through senior executives. 

Inexperienced underwriters can more quickly assess customer applications, rejecting or connecting them to favorable coverage options at an appropriate premium, and claims analysts can identify fraud more effectively. The same analytics tool in the hands of veteran executives can give them other valuable insights such as data that readily demonstrate to regulators that products are appropriately priced and reflect exposure, that they meet acceptable loss ratio and combined ratio norms and that the insurer is a trusted partner in protecting the market.

These are simple examples of well-designed analytics directly affecting business success at multiple levels.

Intuitive self-service

There are valid reasons to compartmentalize intelligence in today's business environment, but the versatility of built-in analytics allows a wide variety of access levels to personas or individuals. Access is based on roles and need-to-know, not how well a person understands code or software. 

As people's business roles, responsibilities and talents change, the tool serves intuitive self-service. Access to intelligence is acquired by applying amassed business acumen, not technological know-how about obsolete software.

Customizing analytic tools allows people to dedicate themselves to productivity rather than repetitive problem-solving and troubleshooting. Actuaries can spend the workday assessing market risks and exposure, creating innovative product offerings or adjusting investment strategy on the book of business instead of figuring out new software updates on old business intelligence platforms.

See also: Improving Customer Experience In 2022

Expect more and deliver

Employees will use the tools at their disposal, whether those tools are adequate or outstanding. However, those that enrich the user experience and directly drive business value get the best adoption rates and results. 

The more comfortable people at an enterprise-wide feel with their tools, the better they’ll perform. Powerful analytics, including predictive analytics, streamline assessment, planning, forecasting and overall decision-making experiences. They allow insurers to effectively connect the benefits of business intelligence to the customer and the organization’s goals.

The sooner insurers adopt analytics to leverage big data, the more quickly they will improve efficiency, reduce costs, meet and exceed customer expectations and boost expectations in an increasingly competitive marketplace.

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