Insurance Analytics Software: Top 3 AI Insurance Fraud Solutions
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.
Fraud is estimated to cost the average family roughly $400-$700 a year in premium increases.
Unverified or false information, brokers ‘gaming’ the system, and bogus claims all contribute to heavy losses for carriers.
Using cutting-edge AI insurance fraud solutions has never been more important than it is today.
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.
This change has made it easier for fraudsters to accomplish their goals as customers are blind to their behavioral signals.
Insurance fraud costs companies billions of dollars every year.
An estimate by the Coalition Against Insurance Fraud shows it has grown to $80 billion a year, and that figure is rising year over year.
Broadly, insurance fraud is when someone knowingly acquires a benefit or advantage by fraudulent means, or when an entity knowingly denies a benefit to one who is entitled by deceit.
Fraud drains profits from insurers and impacts the way they evaluate risks. This can have adverse effects on legitimate customers.
Examples of insurance fraud
Insurance fraud can occur during various points of the insurance lifecycle.
A few examples of insurance fraud are:
- 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 where the policy details are modified without knowledge of the insurance carrier.
- Claims Fraud can take various forms, like attempts at faking one’s death or the death a beneficiary in order 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.
- Common Health Insurance fraud types are wrongful health insurance billing or unnecessary medical procedures.
What innovative approaches are insurance companies using to prevent fraud?
So much time and money is wasted chasing false positives and criminals who will never be caught.
That hasn’t stopped companies from throwing tens of billions of dollars at the problem, however.
With sophisticated fraudsters and bots constantly innovating their attacks, insurers are now forced to take a proactive approach in the fight against fraud.
The Top 3 innovative insurance fraud solutions using AI today:
- Predictive behavioral analytics for proactive risk scoring
- Behavioral biometrics for identify verification
- AI and Machine Learning for continuous data analysis
1. Predictive behavioral analytics used for fraud prevention
The predictive behavioral analytics fraud software available creates a 360-degree view of the customer.
This insurance analytics software solution 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 is able to baseline normal (good) behavior and spot risky deviations from that behavior, much in the way a physical polygraph works.
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. Users are scored in real-time on a scale of 1-100 based on their presumed risk with scoring constantly updating based on outcomes.
Using A.I. to enhance ‘risk scoring’ and automated underwriting algorithms can help identify trends and suspicious patterns in behavior. For instance, if a user is copying/pasting sensitive information, delaying on questions they should have fluency with, or switching or correcting answers, these can lead to an increased risk score. The Digital Polygraph will flag that behavior and alert underwriters, agents, and employees that they should take the necessary measures to further qualify those applicants.
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. Insurance analytics software like teh Digital Polygraph is essential for staying ahead in a highly competitive market.
With technology like Behavioral Intelligence, which utilizes predictive behavioral analytics, companies can shift from reactive approaches to proactive approaches, eliminating the risk before it becomes 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.
Predictive analytics and AI are applied to detect fraud and assign risk scores to indicate the chances of fraud.
Measuring users’ digital body language is becoming a key component of risk assessment and prevention.
These types of fraud prevention software are emerging as the core of fraud prevention plans in insurance.
2. Behavioral Biometrics
The digital persona of an applicant can vary dramatically.
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.
Companies like Biocatch help create a digital user identity unique to an individual.
This is very beneficial when looking to prevent account takeovers, phishing schemes, and fraud prevention.
3. Using AI for continuous review
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 that include an arsenal of data-driven tools — data analytics, predictive modeling, AI – to monitor fraud across different touch-points.
Insurers are investing heavily in insurance analytics software, particularly AI fraud prevention software that use predictive behavioral analytics to weed out the bad actors in real-time.
Ultimately, using innovative AI insurance fraud solutions to detect and 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, can also increase the customer experience, reduce false positives, and help companies convert more genuine customers.
Understanding your users and the data you gather from them has never been more important than it is today.