How Insurance Data Analysis Gives Traction in a Disrupted Industry

Using data to inform decisions in the insurance industry isn’t a new idea; in fact, the very origins of underwriting stem from data analysis, though admittedly not in the way we think of it today, and by necessity using far more guesswork and intuition.

The entire industry has recently been disrupted through very different approaches to insurance data analysis. Agencies that have long relied on manual processes now contend with newer digitally native entrants to the field.

Today, everyone has data—so much data it’s beyond unwieldy. It’s not just that data keeps coming in; it’s multiplying. Without intelligent infrastructure, scalability, and interoperability, data quickly becomes burdensome, complicated, and unpredictable. Laws such as GDPR and CCPA demand better governance and security solutions than many insurance companies planned for when they first began data collection in earnest.

All of this means your existing data tools may not be up to the task of giving you actionable insights anymore, and you may find yourself wondering where the big payoff is as you work for your data—collecting it, managing it, maintaining compliance—instead of the other way around.

How can an MSP help a client with cybersecurity and data compliance?

A robust IT managed service provider (MSP) has access to cybersecurity engineers who use their deep expertise of specific regulations to help clients achieve compliance with all relevant data privacy and protection standards such as GDPR, HIPAA, SOC2, and others as they come online.

Context is everything—especially in insurance data analysis

While the term context can be hard to pin down (and easy to ignore), it matters very much. In its simplest form, context is the surrounding situational information that allows people to make informed decisions. Big, raw data hanging out in data lakes does not provide context. Smart insurance data analysis solutions, however, absolutely do.

Many insurance companies already have the information that would provide the necessary context to take their data power to the next level, but it exists in silos that can’t communicate with each other. Traditional IT infrastructure may not be scalable enough to enable leveraging new data sources. These are foundational concerns that must first be addressed before implementing data analytics solutions.

The problem with data lakes

Data lakes are centralized data depositories that allow users to access data right away from an as-is state with no sorting schema needed first. Because you can deposit data there and go on about your business, many insurance companies never implement the structure that makes scalability and interoperability possible later. For example, tables and how they’re connected may never have been defined. While it was fine at the beginning, that’s an infrastructure issue that will hamstring your progress today.

Data in data lakes rarely gets used in real-world business applications. They’re also hard to keep secure and compliant with ever-emerging government regulations, as companies must have the ability to permanently delete all data pertaining to an individual if requested. That’s tough to execute without the proper framework in place, and even harder to do in a way that won’t disrupt the rest of your systems.

When data storage and operational systems have been patchworked together as needs arise—which is the state many organizations are in today simply because of the rapid evolution of all things data—companies can end up with a bunch of disparate systems that definitely don’t work together and may, in fact, work against each other. But a unified platform can integrate and govern both operational and analytical data. Then the world of predictive analytics at the database level opens up to accelerate and improve decision making.

What advanced data analytics can do for insurance companies

Insurance companies have used credit scores to influence policy decisions and premiums since the 1990s, connecting those scores to a customer’s likelihood to pay on time and the frequency and expense of their claims. Auto insurance companies have offered the good student discount for even longer, concluding that a young person responsible with studies is likely to also be a responsible driver.

Today there’s far more data and sophisticated analysis available to extrapolate accurate insights like turning specific customer behavior into risk factors, and actions of potential customers into buying habits. Gaining a full picture of current and prospective customers enables you to tailor offerings for greater success and make better-informed risk decisions.

  • Achieve a 360-degree customer view
  • Every interaction a customer has with your organization—from a search on your website to a request for a policy quote on through support requests—is connected, in the same place, and accessible to each employee providing the customer experience.
  • Allocate resources in advance
  • Using historical data and predictive models, insurance companies can anticipate needs more accurately and better allocate resources ahead of time.
  • Prevent fraud
  • Advanced analytics can quickly spot red flags that point to fraud, thereby reducing the number of fraudulent claims that reach the point of human evaluation.
  • Reduce expenses
  • Automating processes, efficiently allocating resources, and accurately assessing risks and fraud greatly reduce expenses.

Predictive analytics: AI and machine learning for superior outcomes

AI and machine learning enable insurance companies to do what they’ve always done far more precisely—and comparatively at the speed of light. The efficiency that AI brings to the process of using customer behavior to predict intent is revolutionary. Companies ignore this at their own peril. Companies that embrace it will not only improve processes and decision making but can entirely transform their customers’ experience.

That’s a big deal for an industry that has remained largely unchanged until now. Here’s how AI and predictive analytics impact various types of insurance data analysis.

  • Life insurance companies can use predictive analytics to identify high-risk behavior that may be an indicator of misrepresentation of health risks on an application.
  • Health insurance companies using predictive analytics can incorporate data from fitness trackers like Apple watches or Fitbit devices to expand understandings of customer heath statuses.
  • Most auto insurance companies offer a discount if a driver is willing to record their driving data via app for a specified period of time. More recently, companies like Root are disrupting the market with a model that sets premiums purely based on driving data. The potential for data extraction directly from automobiles themselves presents a whole new level of accuracy for AI and predictive analytics.

Smart analytics solutions matter

To remain relevant in a disrupted, competitive industry, you must evaluate your current insurance data analysis capabilities. Is your current data state working for you or against you? Is your data secure?

Learn how data analytics can accelerate your insurance company’s growth. Talk to one of our experts today to learn more about how quickly our Rapid Prototype Team can build your solution.

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