Five Ways Data Analytics is Transforming the Insurance Industry

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Insurance professionals collect a huge amount of data from their customers.

And then, they hand it over to underwriters, who evaluate risks of insuring that person using various actuarial data, claims data, and a bunch of other techniques (some of which are proprietary to the insurer). From there, the gross and net premium is calculated, a policy is issued, and we’re off to the races.

Problem is, the way things have been done up until now isn’t scalable.

Data sits in what is essentially a siloed computer system. Underwriters don’t (or can’t) communicate with agents and brokers very easily. And, insurance agents can’t easily share data across different offices.

It’s also somewhat difficult to analyze the data and use it. Once it’s pulled off the application, it just sits there in a database somewhere. And, that information costs money in lost sales.

This is why large insurance companies and many brokers are upgrading their systems— so they can take advantage of Data Analytics and use it to its fullest potential. It’s beginning to play a larger and more important role in every aspect of the industry.

Here’s how Data Analytics is transforming a once static insurance industry.


There’s a trend in the industry towards being more client-centric. Instead of “father knows best,” clients want a trusted consultant who can help them get the insurance they actually need. Data Analytics can help brokers fulfill that role. Intelligent insurance management platforms are now allowing agents to use technology that gives them actionable insights based on customer data.

For example, intelligent management platforms feature smart dashboards that you can access as an agent to get a complete overview of each client’s portfolio. If one of your clients has a gap in coverage, the system will automatically alert you and give you an opportunity to bring added value to your client. Instead of blindly cold-calling, you only call when you know your client is actually missing something they need. Not only does this cause the client to feel like you’re looking out for him, but you also get more sales.

Prevent and Reduce Fraud, and Waste

Unfortunately, there are policyholders out there who view the insurance company as their own private stash of money. They believe the insurer is out to cheat them somehow, so they decide to cheat the insurer first and “get theirs.”

The increase in fraudulent claims is evidence of this fact. Thankfully, there’s a way to minimize or stop this phenomenon altogether.

Actionable intelligence from Data Analytics can be used to figure out who is most likely to commit insurance fraud before it ever happens. For example, an agent can monitor data in real-time from various social media platforms to see if a policyholder might be engaging in fraud. If that policyholder files a claim for a tree branch falling on his house in the middle of winter due to snow accumulation, and he’s snapping photos of his awesome rooftop Christmas decorations and posting them to Facebook, there’s obviously a discrepancy.

Help Pricing Premiums

When insurance companies price policies and premiums, one problem they run into is accuracy of the data they have on file. Insurance companies rely on something called “The Law of Large Numbers” to make statistical predictions of insurable events.

Because of this, they cannot predict individual accidents or incidents. And, they don’t need to. They just need to know, statistically, how things will shake out.

This may work fine for the agent, but not so well for a policyholder, because a good driver might end up being lumped in with a bunch of bad drivers, and they’ll all be charged the same premium for their policy.

To be more competitive, insurance businesses have come up with a unique way to extract actionable insights from Data Analytics to track individual policyholder behavior and price policies accordingly.

For example, an insurance company might use predictive modeling to predict the probability of a policyholder being involved in an accident or having their car stolen. Insurers can gain actionable intelligence on an individual policyholder by monitoring their driving habits and behaviors and then comparing them against other policyholders in their database. For auto insurance, a small box can be installed inside vehicles. Or, an app can be downloaded onto a smartphone. The insurer can then monitor driving habits over time.

The data that’s collected can then be used to reprice policy premiums so the policyholder pays a fair premium for coverage. This is a brilliant way to “kill two birds with one stone”.

First, policyholders who are responsible drivers will love the idea of paying lower premiums and are incentivized to have their driving behaviors monitored. The insurance company benefits because it’s one of the few legitimate ways they can fight back against adverse risk selection. In other words, bad drivers are disincentivized to have their driving habits and behaviors monitored.

So, as more good drivers are monitored, more bad drivers are found, and their policies are repriced by exclusion.

Self-Servicing of Policies

Self-servicing is the next major innovation in the insurance industry. Most agents are actually a little worried it will make them obsolete. However, in most cases, this fear is unwarranted.

Companies, including brokerages, that offer a customer portal for policyholders to manage their own policies will find it takes a lot of work off their plate and makes customers happier.

Retention rates may even increase as brokers and agents hand more control over policy management to the policyholders.

Life insurers, like MassMutual, and property and casualty companies, like Geico and Allstate, have adopted customer portals, and brokers and policyholders are better off for it.

Why? Because brokers can automatically track how much insurance is in-force on a policyholder, and how much premium is being collected per policy and per customer. And, using Data Analytics, brokers and agents can automate the process of making smart recommendations to customers right at the moment they are buying a new policy or making changes to an existing one.

There’s another hidden benefit of Big Data for brokerages and agents. Many life insurance businesses, for example, want to move away from complicated and expensive medical underwriting. But, traditionally, this has been nearly impossible. With Data Analytics, and with customers managing their own policies, insurers can (and are in fact starting to) use a multitude of non-medical data points to eliminate the traditional medical underwriting process. Meaning, underwriting can be more streamlined, leading to faster policy issue times.

How Data Affects Internal Processes at Insurance Businesses

Wouldn’t it be nice if insurance businesses could accurately determine which lines of insurance were most profitable and which ones weren’t worth selling?

Real-time Data Analytics can give you that information, and then some.

With Data Analytics, insurance businesses can now figure out:

  • How profitable their book of business is
  • Tweak sales practices to improve those profits
  • Reduce wasted time with policyholders
  • Increase per-agent and per customer profitability
  • Maximize overall performance

All that’s needed is implementing a reliable Cloud-based data-driven broker management platform that can aggregate, analyze and visualize all your data and give you easy access to a variety of practical reports, helping you make better decisions in real-time.

And, at the end of the day, that’s how you stay in business.

Data Analytics isn’t going anywhere—in fact, it is taking us forward. Embracing the high-tech trend will only benefit the industry with a more accurate, fair, and convenient way to deal insurance.


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