Insurance Fraud Detection Software | Top 3 AI Insurance Fraud Solutions
Innovative insurance fraud solutions are on the rise. These new AI-enabled insurance fraud detection solutions are providing a much-needed respite to the growing fraud problem. The Coalition Against Insurance Fraud pegs the yearly cost of life insurance fraud at $90 billion and P&C insurance at fraud at $45 billion and both numbers are expected to grow. Unfortunately for customers, the costs aren’t solely the burden of the carriers. Insurance fraud is estimated to cost the average family roughly $400-$700 a year in premium increases. Needless to say, this is a growing concern for carriers, especially as they grapple with new risks having arisen from the shift to digital applications.
In this article, we will discuss the current state of the problem, highlighting Life and Auto insurance fraud, in particular, and some of the ways top carriers are fighting back.
Life Insurance Fraud
Insurance Fraud Detection
Whether you’re dealing with sophisticated fraudsters, crafty applicants, or bots constantly innovating their attacks, insurers are now forced to take a proactive approach in the fight against fraud. Leveraging the newest insurance fraud detection software is paramount to staying ahead of the growing fraud problem.
The Top 3 Insurance Fraud Solutions:
- Predictive Behavioral Analytics
- Behavioral Biometrics
- Real-Time Intent Data
Predictive Behavioral Analytics for Insurance Fraud Detection
Predictive behavioral analytics is proving to be a potent tool in insurance fraud detection, offering insurers a proactive approach to identify and mitigate fraudulent activities. By analyzing patterns of behavior and interactions, predictive behavioral analytics can unveil anomalies that might signify fraudulent behavior. Here’s how this technology is being used in the realm of insurance fraud detection:
- Behavioral Pattern Analysis: Predictive behavioral analytics scrutinizes historical data to establish normal behavioral patterns for policyholders. This includes understanding how individuals typically interact with the insurance company, submit claims, and engage in other relevant activities. Using claims fraud as an example, predictive behavioral analytics can take into account factors such as the frequency of claims made by a person, the amount of time elapsed between claims, the type of claim submitted, or any other relevant data points related to the claim in order to determine how likely it is that the claim is fraudulent. Any deviation from these patterns could be indicative of potential fraud.
- Real-Time Behavior Monitoring: By continuously monitoring customer behavior in real-time, predictive analytics systems can detect sudden or unusual changes. For instance, a policyholder who has never filed a claim suddenly submitting multiple high-value claims might raise red flags.
- Real-Time Nondisclosure and Misrepresentation Solutions: Using real-time behavioral analytics, carriers can analyze the digital body language of their online applicants to discover when applicants are showing signs of nondisclosure and misrep. How a user physically behaves when answering questions that have a high impact on potential premiums is a key predictor of whether or not they’re telling the truth.
The use of predictive analytics for insurance fraud detection is not only helping insurers prevent losses but also reducing administrative costs and freeing up resources for other areas like improved customer service and product development.
Behavioral biometrics are quickly becoming a key part of the insurance fraud detection landscape. Traditional biometric methods like fingerprint, retinal scans, and facial recognition have been used for years. But now, with the help of behavioral biometrics, insurers can identify potentially fraudulent behavior well before it occurs.
Behavioral biometrics are based on analyzing human behavior, such as keystroke dynamics (the way in which a user types), gait analysis (how someone walks), and voice recognition (how someone speaks). This data is then used to create a unique ‘behavioral fingerprint’ that can be used to detect suspicious activity.
Insurers use behavioral biometrics to track customer interactions with their websites or mobile applications. By carefully monitoring how users interact with the platform, insurers can detect any anomalies that might indicate they are trying to commit fraud. For example, if an individual is clicking on different parts of the website unusually quickly or multiple times in order to access sensitive areas of the system that they wouldn’t normally have access to, this could be flagged up as suspicious behavior and further investigated by insurers.
For example, companies like Biocatch help create a digital user identity unique to that individual. This is helpful in identifying phishing attacks, account takeovers, and account opening protection.
Behavioral biometrics will continue to play an important role in helping insurance companies identify potentially fraudulent behavior and provide better protection against losses from illegitimate claims.
Real-Time Intent Data
Real-time intent data has been a game-changer for insurance companies looking to reduce risk and prevent fraud. Companies like ForMotiv are providing insurers with valuable insights into digital customer behavior that can help them identify potential fraudulent activity and make better decisions when assessing risk.
ForMotiv’s platform uses advanced machine learning and behavioral data science to understand the motives and intent of digital applicants. This unique, first-party behavioral data is used to identify risky behavior associated with certain types of nondisclosure, misrepresentation, premium leakage, risk, and fraud. How users physically behave while filling out digital applications is incredibly telling of their truthfulness and intent. ForMotiv allows carriers to triage applications when there are signs of risk to drive the more appropriate next-best-action. This has proven incredibly valuable as carriers look to accelerate and make instant decisions on policies.
Real-time intent data also allows insurers to gain deeper insights into the purchase intent of an application. With that, carriers can spend time focusing on high-intent shoppers while selectively reducing the time spent on window shoppers or high-risk applications.
Overall, real-time intent data has revolutionized the way in which insurance companies assess risk and prevent fraud, providing them with invaluable insights into customer behavior that can help them keep losses under control and remain competitive in today’s market.
Ultimately, using advanced insurance fraud solutions and detection software 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.
As no single method can detect all fraud, insurance companies are looking at AI insurance fraud solutions and top insurance fraud detection software companies that include an arsenal of data-driven tools — data analytics, predictive modeling, AI, biometrics, real-time intent data, and more.