Insurance fraud costs companies billions of dollars every year. In fact, the Coalition Against Insurance Fraud estimates that it has grown to $80 billion a year, and that figure continues to rise. Fraud drains profits from insurers and impacts the way they evaluate risks. It also has adverse effects on customers, like costing the average family $400-$700 a year in premium increases. It’s a major loss on both ends. So what can we do about it? In today’s article, we’re unpacking the solution: how to predict insurance fraud with different types of behavioral analysis.
(We’re not getting too deep into what insurance fraud is here, but we’ve got a ton of great articles about it on our blog if you’re interested.)
Where Does Insurance Fraud Happen?
Unverified or false information, brokers ‘gaming’ the system, and bogus claims all contribute to heavy losses for carriers. At what point does fraud happen? Insurance fraud can occur during various points of the insurance lifecycle. Here are a few examples.
Application Fraud and Synthetic ID occurs during the process of the application when incorrect information (material misrepresentation) is provided to the insurance carrier. This is the most common type of fraud.
Forgery occurs when the policy details are modified without the knowledge of the insurance carrier.
Fraudulent Insurance Claims can take various forms, like attempts at faking one’s death or the death of a beneficiary to collect a life insurance payout.
Auto Insurance fraud is a major area of concern where frauds occur around stolen vehicles, car accidents, and wrongful claims.
Health Insurance fraud types are wrongful health insurance billing or unnecessary medical procedures.
(Unfortunately, examples of insurance fraud don’t end here. Read about more kinds of fraud right here.)
Insurance Fraud in the Era of Automation
Even though we’re seeing fraudulent claims skyrocket in the last few years, it’s nothing new in the industry — analysts have been trying to predict fraud with predictive models since the industry started. However, the drastic difference we’re seeing today comes from the rise of online automation.
The adoption of automation in the insurance industry has been somewhat of a double-edged sword. While automation helped speed up applications, underwriting, and claims, it also paved the way for new types of fraud along the insurance lifecycle.
Similarly, the “Digital Shift” gave rise to digital experiences and automated premium calculation processes. The move from face-to-face interactions to faceless interactions has made it impossible to understand human behavior and body language and people lie more when they’re online.
This change has made it easier for fraudsters to accomplish their goals as customers are blind to their behavioral signals. Because of this, utilizing cutting-edge AI insurance fraud solutions and partnering with top fraud detection companies has never been more critical than it is today.
Using Artificial Intelligence to Predict Insurance Fraud
So much time and money are wasted chasing false positives and savvy criminals who are hard to catch behind the faceless internet. But this hasn’t stopped companies from throwing tens of billions of dollars at the problem. With sophisticated fraudsters and bots constantly innovating their attacks, insurers are now forced to take a proactive approach in the fight against fraud.
A standalone assessment rarely gives a true picture of an applicant. With that, insurance carriers have taken to a system of continuous review and rescore using predictive analytics and AI. This helps to uncover patterns and anomalies for fraud detection and triggers red flags for appropriate action. Adopting a layered data-driven approach is crucial for maintaining strict fraud security measures.
As no single method can detect all fraud, insurance companies are looking at AI insurance fraud solutions and top fraud detection companies with an arsenal of data-driven tools including data analytics, predictive modeling, and AI to monitor fraud across different touch-points.
What innovative approaches are insurance companies using to prevent fraud? Top insurers are setting the pace with these three methods.
- Predictive behavioral analytics for proactive risk scoring
- Behavioral biometrics for identity verification
- AI and machine learning for continuous data analysis
Predictive and Real-Time Behavioral Analysis
Given fraud is one of the biggest drains on profits for insurance carriers, innovative AI insurance fraud solutions are on the rise along with general insurance analytics software. More accurately, insurers can predict insurance fraud with real-time clickstream analysis and behavioral analytics.
Insurers are investing heavily in insurance analytics software, particularly AI fraud prevention software that uses predictive behavioral analytics to weed out the bad actors in real-time.
What if your application was connected to a Digital Polygraph?
Consider the power of predictive behavior analytics like you would a polygraph. With a traditional polygraph, we don’t know what a person is answering; we just know that if you asked a smoker if they’ve smoked in the last 12 months, you’d likely see a heart rate spike and other biometric signals. The spike in heart rate would be an indicator of a false claim.
Sure, predictive behavioral analytics software isn’t measuring heart rates while a user fills out an online application any time soon; but it’s a great analogy for measuring digital body language to spot risky behavior — a digital polygraph, if you will.
Predictive behavioral analytics fraud software creates a 360-degree view of the customer. It gives carriers a comprehensive ‘digital identity’ of their potential customers to help prevent fraud.
For example, ForMotiv is leading the way using their digital polygraph to measure user behavior and correlate it to fraudulent outcomes. By analyzing millions of behavioral patterns such as keystrokes, typing speed, hesitancy, and form corrections, ForMotiv can baseline normal behavior and spot risky deviations from that behavior, much in the way a physical polygraph works.
Predict Insurance Fraud with Real-Time Risk Scoring
Using AI to enhance risk scoring and automated underwriting algorithms helps identify trends and suspicious patterns in behavior. What does this look like?
Consider, for instance, how a user with the intent of fraud behaves while filling out forms. They’re copying and pasting sensitive information rather than typing it out; they’re delaying with answers they should know like the back of their hand; they’re switching or correcting answers; they’re opening and closing new tabs while searching for information. Real-time risk scoring software can analyze these behaviors as they’re happening in real-time. The digital polygraph can then flag that behavior and alert underwriters, agents, and employees to take the necessary measures to qualify those applicants further.
This new insurance fraud solution helps drive profitable underwriting and premium calculations that benefit the insurance company and its customers, while intelligently reducing risky applications.
Accelerated underwriting makes predicting fraud in real-time even more important
Accelerated underwriting aims to make it easier for high-quality, high-intent customers to get approved faster. In other words: to grow the insurer’s bottom line.
We skip important screening steps that once existed in traditional underwriting. For instance, applicants may be excused from undergoing a health screening or taking a hard credit score, among others. There are clear benefits for straight-through-processing like this, but they’re not always worth it without the ability to predict fraud accurately.
With the push for accelerated underwriting to meet the demands of the modern consumer, predicting fraud is more important than ever. And to take this one step further — predicting fraud in real-time is even more important. After all, hindsight doesn’t help a closed fraudulent case.
Behavioral Biometrics
Since we no longer sit face-to-face with potential customers, we’re faced with the challenge of deciphering digital personas of applicants that vary drastically. AI fraud prevention software helps establish a system of online authentication with a unique “digital identity” of the customer based on past behavior or their digital footprint.
Rather than measuring John Smith versus the millions of other digital users, biometrics focuses on comparing John Smith to John Smith.
Account Takeover
Account takeovers happen when fraudsters hack into a user’s personal data and use it for identity theft, take over accounts, and handle fraudulent finances. It’s incredibly stressful for the victims and leads to a lot of the online fraud we see in insurance.
Companies like Biocatch, NuData, and Secured Touch all help create a digital user identity unique to an individual. They solve different problems than ForMotiv and are beneficial when looking to prevent account takeovers, phishing schemes, and fraud prevention.
It All Boils Down To Knowing Your Customer (KYC)
Know Your Customer is a largely effective fraud prevention tool. Ultimately, using top fraud detection companies to know your customer to reduce fraud is crucial for insurers today.
Not only can it have a major impact on a company’s bottom line, but if done correctly, it can also increase the customer experience, reduce false positives, and help companies convert more genuine customers.
Conclusion
Bottom line is this – to predict insurance fraud, you’ll need a variety of different solutions. There is no silver bullet, so it’s imperative to add as much ammo as you can to your arsenal.
Understanding your users and the data you gather from them has never been more important than it is today. By creating a behavioral baseline of how both genuine and fraudulent users interact with online forms and applications, ForMotiv uses machine learning and predictive analytics to then predict what that particular user’s likely outcome will be.
With ForMotiv’s behavioral intelligence technology, insurance companies can shift from reactive approaches to proactive approaches, eliminating the risk before it becomes more costly.
Behavioral Intelligence is the leading AI predictive risk software available today.
Rather than wait for fraud to occur and deal with it after the fact, insurers are implementing AI insurance fraud solutions that identify potential fraud early in the insurance lifecycle.
Now begs the question: are you waiting for fraud to occur, or are you taking proactive steps to prevent it?
