predict insurance fraud

What’s Next for Risk Analytics in Insurance?

Risk analytics in insurance is evolving. We’re entering an era where insurers need to accurately assess levels of risk and fraud faster, with less data, and online. 

With that, insurance companies must rely on the digital body language of their online users to gauge the honesty on both sides of the screen: agents and applicants. In this article, we’re sharing how to be prepared.

Go ahead — skip to the good stuff

  • How risk analytics are used in insurance
  • How insurance companies are currently assessing risk
  • Benefits of using risk analytics
  • Challenges of using risk analytics

How are risk analytics used in insurance?

Generally speaking, insurers use analytics to predict and reduce risk, improve customer experiences, and optimize internal operations (i.e. underwriting, claims processing, employee productivity, etc.) 

Insurance companies can also use more advanced algorithms to investigate the validity of claims. The more advanced the algorithms and software insurers use, the more accurate results they see on the other end. Basically, you get out what you put in — makes sense. 

Maryville University illustrated the process of setting up predictive analytics in seven stages:

  1. Define the objectives
  2. Collect the data
  3. Validate the findings  with statistical modeling
  4. Analyze the data
  5. Create predictive models for future objectives
  6. Deploy the predictive model (ideally so it’s automated)
  7. Monitor the results

And while the practice of predictive analytics isn’t groundbreaking, the new methods of collecting and analyzing data are. Risk analytics driven by AI and machine learning provide the most accurate results because it means that their predictive models have the ability to “get smarter” with access to thousands of more data points than traditional models. It takes out costs associated with labor efforts that would otherwise go into manually collecting data points, analyzing them, and then adjusting the predictive models based on their findings.

Use cases for analytics in insurance

While our main focus in the article is on risk analytics,  AI-powered models have the potential to directly impact every department — from the research stages to payouts, and all the critical steps in between. Here are a few of the main ways we’ve seen it work.

    • Detecting fraud: Understand fraudulent behavioral patterns and predict fraudulent users or transactions before they impact the business. 
    • Predicting risk: With smarter predictive models powered by AI, insurers have the ability to instantly predict if a user is a liability, a high-risk customer, low intent searcher, or displaying bot activity. 
    • Underwriting: AI and predictive analytics working together help underwriters reduce bias, human error, and ultimately boost the insurer’s bottom line with straight-through processing.
    • Improve customer experiences: Predictive risk analysis can also help create a seamless experience that increases customer satisfaction, net promoter scores, and lifetime value. 
    • Claims processing: With the right models in place, insurers can analyze user behavior during the digital claims process to predict their intent and create better outcomes.
    • Optimize operations: Predictive models can help manage resources, set pricing, predict client retention, predict turnover, and predict revenue and expenses. 

How are insurance companies assessing risk right now?

In 2021, 55% of U.S. customers between the ages of 30-49 bought insurance online, and 44% for ages 18-29 — both brackets are expected to increase. As companies continue to accelerate their digital transformations and move their user experiences fully online, it’s easier than ever for risky and fraudulent applications to sneak past their defenses. For example, common risky behaviors that fly under the online radar are robot applications, smokers’ amnesia, soft fraud, and copy/paste fraudsters. 

While the contents of data collection vary depending on the type of insurance carrier, these are the main datasets insurers use to assess risk:

  • Interpersonal behavioral cues (for in-person)
  • Motor vehicle reports
  • Credit scores
  • Financial history
  • Utility bills
  • Social media activity
  • Job history
  • Past insurance coverage
  • Criminal records
  • Invisible bias (regarding manual underwriters)

We mentioned the problem with outdated analytic models in the section above, and the same can be said for outdated data collection. The problem with outdated data collection is that it’s (also) expensive, prone to human error, and doesn’t give you the full picture. Online migration to the faceless internet possesses a threat to these outdated datasets because it makes it more difficult to analyze risk like you used to.

Benefits of using risk analytics in insurance

The quality of insurers’ risk management has a ripple effect on every aspect of the company; all departments and every customer. Here are a few examples of the benefits of improved risk analytics. 

Lower rates for customers.  Accurate risk predictions mean customized, reliable, and generally lower premiums. Everyone benefits from more accurate risk predictions. 

Better-qualified applicants. There’s a wide range of potential customers filling out your online applications — high-value, high-risk, and every level in between. With machine learning technology, risk models can collect, assess, and sort out the best candidates to offer straight-through underwriting.

Custom-fit policies. Why cross-sell homeowners insurance with add-ons to the renter who’s just looking for a baseline policy for their car? In the process of risk analytics in insurance powered by AI, you have the capability to get to know your customers better. As a result, you can cross-sell policies your customers actually want. 

Less leakage/losses from fraud. As we all know, fraud is a massive problem in the industry, to the tune of billions of dollars lost. Accurate risk assessment helps predict fraud so insurers can avoid doing business with low-intent customers. 

How do the customers benefit? 

Customers benefit from improved risk analytics in insurance in many ways. For starters, they’ll want to stay longer because they’re getting a product with great value at an affordable rate. They feel like the policy has everything they need and aren’t paying for extras. 

Your service will continually improve as the company refines customer needs through risk analytics, so the customer experiences the benefit of a personalized experience. And finally, even if they aren’t qualified at that moment, risk analytics will give them an answer more quickly. If they aren’t leaving an application process in bad taste, they’re more likely to come back later once they’re qualified.

When the company benefits, the customers do too. Improving the customer experience is essential to increasing the bottom line. There are a ton of factors that go into this, but overall the takeaway is that customers experience higher satisfaction when they interact with platforms personalized to them. You need to know your customers in order to understand them. 


If predicting risk accurately wasn’t an industry-wide challenge, we wouldn’t see billions lost every year from fraud. And then to make things more complicated, the rapid migration to online distribution leaves many insurers feeling a step — or rather, many steps — behind.

Using secure data. No one wants their privacy exploited. Third-party data and ‘cookies’ had their minute in the limelight, but now they’re facing a lot of scrutiny for breaches of privacy and lack of transparency. We recommend looking for a first-party intent data provider that is 100% secure and doesn’t collect personally-identifying information.

Owning your data. Another downfall of third-party data is that you don’t actually own it (and your competitors buy it.) Instead, you’ll want first-party data that’s yours, and only yours. Learn more about the different kinds of data to consider right here

Finding accessible information.  What’s the benefit of collecting great data if you don’t understand the reports you get? (Spoiler: there is none.) The software you choose should be easy to integrate into your systems and easy to use. When you purchase an analytics platform that’s user-friendly, your company can use it across all departments — not just data scientists. 

Choosing the right tools. Risk and fraud departments that are ditching traditional datasets have a hard time sorting through all the new data collection options. Do you piece together a strategy with pre-and post- submit tools? 

Ultimately, your goals need to fall in line with the method of data collection. Our advice is to choose software that can prove a history of accuracy with case studies, is user-friendly, and can be customized to meet your objectives. For more on this, check out our article on The Top 5 Intent Data Providers

Looking for the right risk analytics tool? Look no further. At ForMotiv, we understand that a balance needs to be struck between a seamless user experience and a well-protected application. Schedule a call to see how we can improve your risk analytics.

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