As carriers automate processing, detecting insurance claims fraud with intent data is more pressing than ever. Even with all of its advantages, the digital world takes away our ability to read and react to user body language, tone, and other social cues we once relied on. Now, it’s much harder to understand their motives and intent. We would argue that it’s actually impossible — that is, without behavioral intelligence from intent data

Using the right combination of predictive analytics in insurance and behavioral economics, you can analyze unique behavioral patterns from users’ digital body language to predict their intent. This opens up a whole new realm of possibilities for insurers. In today’s article, we’re focusing on how intent data can detect insurance claims fraud. Better yet, we’re giving you the step-by-step action plan to do it. 

Skip to the Steps

  • Step One: Collect the Data
  • Step Two: Understand the WHY
  • Step Three: Figure out WHEN
  • Step Four: Utilize Real-Time Intervention
  • Step Six: Implement Technology that Can Do it All For You

What is Insurance Claims Fraud?

There are two sectors of insurance that are at the highest risk of insurance fraud: healthcare and automotive. Within these sectors, there are three categories of people who commit insurance claims fraud. First, you have organized criminals who make it their business to receive large payouts from fraudulent claims. Second, there are technicians who overcharge service costs for services they didn’t provide. And finally, we have everyday citizens who make false claims to cover deductibles, save money, or even try to make some extra cash. This is why predictive analytics in insurance is so important along with being able to identify red flags with insurance fraud solutions

What Types of Insurance Claims Fraud are Common?

While the issue of fraud in the industry extends quite far (like, we’re talking an annual $80 billion dollar cost to U.S consumers) fraud during claims happens at very specific moments. Here are the most common types of fraud we see from insurance claims.

Padding or inflating claims: most common in auto insurance, but can also happen in workers’ compensation and healthcare. This happens when an incident has occurred, but the reported details aim to get a bigger payout than necessary. Another way this is referred to is a misrepresentation.

Staging: most common auto insurance and workers’ compensation. This happens when an accident is staged to get the payout, whether that’s an auto accident or physical injury. 

Give Up: is exclusive to the auto industry. This is when a vehicle owner falsely reports a vehicle as stolen but still intends to keep it in their possession.

Jump In: is exclusive to the auto industry. This is when passengers are reported who were, in fact, not involved in the accident. 

Gaming: can happen with any type of insurance. This is when an insurance agent overly coaches their client through the application process or goes back into the claim to change answers. The goal is always to land a higher payout. Insurance agent fraud is more common than anticipated.

Duplication: this can happen in any type of insurance. This is when a customer files multiple claims for the same incident, whether that’s through the same policy, or through multiple carriers. 

Identity Theft: can happen in any type of insurance. In cases of fraudulent claims, this is when customer information is stolen to report a claim with their information. 

Billing Fraud: is most common in healthcare and auto insurance. This is when a professional will bill the insurance for services that never happened, or for services that were not necessary for repair (also known as over-servicing.)

Why Detecting Digital Insurance Claim Frauds is Important

There is one common denominator in all these examples of insurance: they can all be filed online with a blind view of the insurer. A faceless insurance claims fraud process means that insurers miss out on the opportunity to use traditional fraud detection methods to catch fraudulent behavior as they once relied on. To add to the challenge, users and agents can “game” the system to receive lower rates, even if they’re a higher risk profile. This makes preventing insurance fraud that much more arduous.

In short, everyone loses. Carriers lose millions, and customers pay vastly higher premiums (check out the exact numbers right here.) Insurers become defenseless to fraud online, which is why detecting digital insurance claims fraud is now so critical. Luckily, with a complex problem comes an easier solution: using intent data. 

How To Detect Insurance Claim Frauds with Intent Data

Let’s take a quick pause to define what intent data is. Refined to its core, its user data is collected to help a company understand who someone is, why they do what they do, and what they’re likely to do next. (Sounds a little too magical? We promise it’s not.)

So how exactly do you use intent data to detect fraud? We’ll make it easy for you. Here’s a step-by-step guide:

Step One: Collect the Data

The first step in understanding customer behavior is actually collecting digital behavioral intelligence data. Seems like a no-brainer, right? In order to know who we’re working with, we need to start with some data. 

Make sure you have data collection software installed onto every digital platform your user engages with. For instance, your website, email marketing host, social media channels, CRM, company apps, and other cookies. Ideally, you’ll use ONE lightweight technology that won’t slow down your user’s experience. (Using multiple apps means you’re taking away from analyzing the full picture in one place.)

Step Two: Understand the WHY

Once you’ve collected the data, the next step is to analyze it. Look for patterns indicating what’s normal, what’s not normal, and what your users are doing that you didn’t know about. 

Then, take a retroactive look to figure out why they did what they did. In cases of claims fraud, it’s often tied to financial incentives. What areas of their behavior point to this fact that we already know? Are these patterns your system can start to recognize, and there notify you about?

Step Three: Figure out WHEN

Assuming you get comfortable with the why the next step is to figure out when. When did a customer behave in a certain way, or more practically, how soon can we know they are doing a certain behavior? Is there time to be notified?

Step Four: Utilize Behavior Signaling and Real-time Intervention

Retroactive data is helpful for identifying past patterns, but what really moves the needle in actively preventing insurance fraud is behavior signaling and real-time intervention. 

Let’s say, for instance, a user copies and pastes a social security number into an online claim application. This can be considered risky behavior, so you should know about it when it happens — not when the claim is filed and it’s too late. 

How early can we see this behavior so we can do something about it? In most cases, the sooner the better. 

Step Five: Find a Technology That Can Do it All For You

Obviously, you can’t have someone sitting in on every single claim filed through your company (otherwise we wouldn’t have this problem, would we?) So the next best thing to X-Ray vision goggles is a lightweight technology that can do it all for you: Collect the data, analyze it, understand why, figure out when, and intervene in real-time. 

Spoiler alert: ForMotiv can do it all for you. Learn how our insurance data analytics software works. 

Behavioral Intent Data > Insurance Claim Frauds

AI-powered behavior intent data doesn’t stop at detecting insurance claims fraud — it has the potential to predict fraud across the board. Here’s how.

Detecting Bot Applicants

Bot applications and claims are (almost) always associated with fraudulent activity. Behavior analytics and intent data can help you instantly identify bot applications and triage them in real-time before they are approved or shown pre-fill information. 

Scoring Risk in Real-Time

With automated detection intelligence on your website, you have the ability to instantly predict if a user is a liability, a high-risk customer, low intent searcher, or displaying bot activity. If someone is filing a claim with a high-risk profile, it can be flagged before anything gets approved. 

Application Friction

AI-powered fraud detection can do more than predict a user’s intent. It can also react to it. So when a fraudster is detected, AI-powered intent data can dynamically add or remove friction for high-risk or genuine applicants in real-time, not when it’s too late.

Detecting Insurance Claims Fraud with Formotiv

The future of claims is here, and we know that fraudsters are getting smarter. So it’s time to adapt. While online claims save time and provide instant customer gratification, it also means you miss out on the old way of doing business — face to face — where you could once observe your customer’s nonverbal cues to assess the legitimacy of the information. Simply put, AI-powered intent data can read the “digital body language” of a user to anticipate their motives and next moves. 

ForMotiv’s “digital polygraph” analyzes user behavior and accurately predicts whether it’s a risky or fraudulent activity while a user is filing a claim. But we don’t stop there, we enable carriers to dynamically add or remove friction tailored to the individual user. 

We also understand that your means of data collection technology needs to be lightweight, easy to use, and easy to understand. Sound like something your company needs? Come see how it works.

Book a demo to see how we can help you catch insurance claims fraud with intent data today.