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How Insurance Companies Can Integrate & Aggregate Disparate Data Sources to Launch Advanced Analytics Capabilities

How Insurance Companies Can Integrate & Aggregate Disparate Data Sources to Launch Advanced Analytics Capabilities

Insurance companies can enjoy enormous benefits from advanced analytics, improving claims processing, fraud detection, underwriting, and customer care.

In an era where competition has forced prices ever downward and customers face few hurdles for switching providers, insurance companies have an imperative to find ways to extract more value and fuel better business processes with the data they already have. 

The bad news is that many insurance organizations are currently relying on legacy technology and siloed data sources, which makes advanced analytics nearly impossible. The good news: organizations that find themselves in that scenario have the potential to enjoy enormous gains when they take the leap toward unified data. 

Here’s a look at how data integration and aggregation can lay the groundwork for advanced analytics and transform an entire insurance organization.

The Problem: Dispersed and Siloed Data

Dispersed and siloed data is all too common among  insurance organizations, thanks to years of acquisitions, mergers, and tech adoption that meets a specific division’s needs rather than serving the entire organization.

Unfortunately, this state creates inefficiencies and limits the organization’s capabilities. When data isn’t contained in a centralized location…

  • It’s difficult to get a full, 360-degree view of customers. Information about billing may be separate from information about claims, which may be separate from policy information.
  • It’s impossible to position yourself as a consultant or advisor. Without that 360-degree view at the ready, insurance companies can’t differentiate themselves by serving the full customer and are left with few alternatives to competing on price – a losing proposition for the long term.
  • Leaders don’t have the real-time insights necessary for key business decisions. When analysts have to manually process data requests, business leaders are effectively trying to lead by looking in the rear-view mirror. When errors get introduced, outcomes are even worse.

Obviously, this state is a problem for insurance organizations.

But it’s even more serious given the reality of insurtech startups. These digital-native companies don’t have the technical debt of the incumbents they’re competing against. They have much better customer visibility and are much more agile.

While incumbents may have better and more extensive data from their decades in business, insurtechs are alluring to customers because of slick front ends and streamlined experiences that appeal to a population used to Amazon and Netflix.

The Solution: Integrating and Aggregating Data to Enable Advanced Analytics

For most insurance organizations, integrating and aggregating data is a three-part challenge:

  1. Unifying existing datasets
  2. Standardizing data
  3. Establishing processes for unifying and standardizing future data the company acquires

Let’s look at unifying data first. This involves merging data from multiple sources into a data lake that acts as a single source of truth. It also often requires identifying where data points are missing and figuring out how to collect and integrate those data points.

This isn’t always easy when multiple teams and organizations are involved. In our past work with insurance clients, we’ve found it especially important to make sure that that data collection isn’t dependent on an organization’s partners. While some partners can send data through an API, for example, others can’t – and the organization needs a way to get that data regardless.

We’ve found success by establishing rules for data collection that include workarounds: if data point X isn’t sent from a partner, for example, we develop protocol Y to scrape it from another source.

Then comes the question of standardization. As you can expect, when data comes from multiple sources, it exists in many formats.

For example, different sources might represent a customer’s name in various formats: “last, first,” “first last,” “first middle last,” and so on. Without setting a standard convention for representing names, these entries could be read as three separate customers. The second phase of data integration and aggregation, then, is to standardize data so it’s searchable to both humans and algorithms. It’s especially important to ensure that all new data flowing into the centralized data lake follows these protocols.

The Results: Improved Claims, Fraud Detection, Underwriting, and Customer Care

Integrating, aggregating, and standardizing your organization’s data is a bit like organizing your pantry and adding an overhead light. Suddenly, you can see all the ingredients you didn’t know you had, opening up new opportunities to try new, innovative, and delicious recipes.

Here’s a look at four specific areas of an insurance organization that can be transformed via advanced analytics once its data is in order:

  1. Claims predictions and processing: Rather than relying on manual processes, with employees verifying every part of a claim document and analysts running various calculations in hopes of deriving valuable insights, you can turn to automation. Predictive analytics, data visualization, pattern matching, and other advanced analytics processes can give you far more insight, much faster.
  2. Fraud detection: Fraud costs insurance companies $40 billion dollars a year, in large part because of the resources they have to dedicate to detecting and fighting it. With advanced analytics like machine learning and complex event processing in place, though, organizations can streamline many processes that are currently manual. Given that those committing fraud continue to evolve and adapt new technology, it’s particularly important that insurance companies have state-of-the-art solutions.
  3. Underwriting: Advanced analytics makes underwriting more accurate and faster. With the right systems in place, you empower your employees to devote their time to solving complex edge cases.
  4. Customer care: With unified data and systems, you can become a trusted advisor to customers. For example, when results of a life insurance exam turn up a condition not covered by their health insurance, your agents can advise them about their options for gaining coverage. This wins trust, which makes it much less appealing to switch providers if a lower price comes along.

Digital Transformation for Insurance Organizations Starts with Data

The end states promised by the phrase “digital transformation” may sound almost fantastical to an insurance company still using systems designed 20 years ago. But they’re attainable. Even better, we have a proven process for getting there – and it all starts with your data.If you’re interested in learning more about how we can transform your organization with advanced analytics and other components of digital transformation, get in touch today to start the conversation.

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