If preventable mistakes cost an industry millions of dollars, we’d do something about it, right? Instead of seeing this as “the cost of doing business,” it’s time to consider how we can start sealing off the cause of claims leakage in insurance with the power of AI.

Claims leakage is money insurance companies lose from claims processing mistakes. These mistakes usually happen by human error, manual processing, and/or lack of visibility in the claims process. In some cases, it can even come from fraud. Leakage often goes undetected unless there’s a formal audit over closed claim files – basically when it’s too late. Simply put, claims leakage leads to unnecessary profit loss.

Potential ROI for Reduced Claims Leakage

Right now, claims leakage in insurance costs carriers over 30 billion dollars annually.  It also accounts for up to 10% of all claims paid, and in specific sectors like life insurance, that number can spike up to a whopping 25%. 

No matter how you look at it, leakage directly affects the bottom line of insurance companies. 

While this is a massive problem in the industry, the solution is pretty straightforward: AI and machine learning. 

Real-time behavioral analytics from AI and machine learning can drive 6-7X ROI for insurers, reduce fraud, and seal off the not-so-subtle cause of claims leakage. 

Primary Causes of Claims Leakage in Insurance

According to PwC, there are three main areas where claims leakage is likely to occur:

Failure to detect fraudulent claims: As life insurance companies continue their digital evolution, fraudulent claims are rising, prompting an even greater need for sophisticated fraud detection solutions. Read more about our take on insurance fraud right here.

Errors in payments made to claimants: Manual processes, lack of employee training, and outdated technology all contribute to costly errors in the claims process. 

Missed opportunities: Intervention opportunities pass by because of inconsistent documentation and approaches across claim employees. Since most occurrences of claims leakage aren’t caught until after a case file is closed, there’s little to no room for repair — the money is lost. 

Human Error

Among the three main causes of claims leakage, human error is at the root of the problem. Here’s why. 

Poor training: How do employees know what to look for when they haven’t been taught? Whether it was rushed, uncomprehended, or chalked up to ‘learn-it-as-you-go’, poor training is to blame for human error during claims leakage in insurance. 

Variable decision-making: Employees within the same company come to different conclusions while looking at the same case because, well, they’re humans. 

Outdated (or insufficient) technology systems: Lack of tools for data analysis in the insurance industry lead insurers to make the same mistakes with very little reevaluation. And it’s a costly cycle. 

Despite the constant cycle of human error with claims leakage, insurance companies continue to operate with an overdependence on manual processes driven by the human hand. 

Manual Processes 

Handling claims manually poses more problems for insurance agencies (again, with human error at the root of the problem.) This is why it’s problematic.

Insufficient review processes: A manual review process often leads to overlooked red flags that could otherwise be caught automatically by artificial intelligence.

Wasted time: Tedious manual processing eats away at employee time that could be spent on more productive tasks. 

Lack of real-time monitoring: Real-time monitoring enables agencies to intervene with users as red flags pop up, not after it’s too late. 

Leaves room for human error: Again, with the humans — the cycle of error continues during manual processing. 

Not Enough Visibility in the Claims Process

Claims processors miss a ton of key data when all they see is the claim itself. Where there was once in-person interaction to observe interpersonal clues of fraudulent behavior, filing ‘faceless’ claims online leaves a lot of mystery behind the screen. 

That is, without the help of AI-powered behavioral data.

H2: Using AI to Reduce Claims Leakage in Insurance

AI software like ForMotiv collects hundreds of unseen behavioral cues while users fill an insurance claim online. Using advanced artificial intelligence, AI essentially reads the ‘digital body language’ to access and instantly predict the credibility of a claim in ways humans and even outdated technology cannot. Here’s how ForMotiv does it:

Notice Loss Before it Happens

ForMotiv analyzes how users apply for quotes and buy policies digitally. They collect up to 50,000 behavioral data points and use that data to predict a user’s intent. With those intent scores processing in real-time, ForMotiv can tell carriers which users are most likely to file claims within the first 90-days.

Predict Claim Leakage in Insurance

Is the insurance policy user displaying genuine or high-risk behavior? A submitted claim can’t tell you, but the ‘breadcrumbs’ of the digital user’s behavior during the claims process can. ForMotiv can predict risky or fraudulent claims with over 90% accuracy. Preventing insurance fraud is far easier with digital body language at our disposal.

Intervenes in Real-Time

ForMotiv adds friction in real-time during a suspected transaction of claims leakage. 

Feeds the Bottom Line

And in case you were wondering, ForMotiv has seen ROI as high as 8-figures.

P.S. Did we mention you can have this technology up and running on your website in less than a week? 

Ready to seal off the cause of claims leakage? Schedule a call to see ForMotiv in action. 

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