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

There are several different types of life insurance fraud, including:

Faking death: This occurs when an individual stages their own death in order to collect a life insurance payout. This may involve creating false documents, such as a death certificate or obituary, or even using a body double to convince investigators that the policyholder has died.
Nondisclosure & Misrepresentation: Nondisclosure refers to the intentional or unintentional omission of relevant information by the policy applicant during the application process. This information could be facts or details about the applicant’s health, medical history, lifestyle, or other factors that are relevant for the insurance company to accurately assess the risk and determine the premium. Some individuals may lie about their health history or current health status in order to obtain a life insurance policy or increase the value of an existing policy. This may involve withholding information about pre-existing conditions or submitting false medical records. One of the most common examples of medical misrepresentation is known as, “Smoker’s Amnesia,” in which an applicant fails to disclose their smoking status. Tobacco nondisclosure, for example, is a $4 billion problem and growing. Learn more about identifying nondisclosure and misrep here.
Premium fraud: Premium fraud occurs when an individual fails to pay their life insurance premiums but continues to collect the policy benefits. This may involve falsifying payment records or using a fake name or identity to obtain a policy.
Beneficiary fraud: Beneficiary fraud occurs when an individual falsely claims to be the beneficiary of a life insurance policy. This may involve forging documents or using false identification to collect the policy benefits.
Stranger-originated life insurance (STOLI) fraud: STOLI fraud occurs when an individual takes out a life insurance policy on someone else’s life, with the intention of selling the policy to a third-party investor. This may involve offering the insured individual financial incentives to take out the policy and may be illegal in some states.

As we mentioned before, life insurance fraud costs the industry an estimated $90 billion each year. This includes not only fraudulent claims but also the cost of investigations, legal proceedings, and other related expenses. The impact of life insurance fraud can also be felt by innocent policyholders, as increased fraud leads to higher premiums and reduced coverage options. Hence carriers are searching for new insurance fraud detection software to reduce risk and fraud.

Auto Insurance Fraud

As carriers explore different insurance fraud solutions – there are several different types of auto insurance fraud to consider, including:

Staged accidents: These occur when individuals purposely cause a car accident, or stage a fake accident, in order to file fraudulent insurance claims for medical expenses, vehicle repairs, and other damages. This may involve multiple vehicles and individuals who work together to create a convincing accident scene.
Exaggerated claims: Exaggerated claims occur when an individual files a claim for damages that are much greater than the actual cost of repairs or medical treatment. This may involve inflating the cost of repairs or medical bills or claiming damages that were not actually caused by the accident.
False injury claims: False injury claims occur when an individual files a claim for injuries that were not actually caused by the accident, or exaggerates the severity of their injuries in order to receive a larger settlement. This may involve submitting false medical records or other documentation to support the claim.
Premium fraud: Premium fraud occurs when an individual fails to pay their auto insurance premiums but continues to drive their vehicle and file claims. This may involve falsifying payment records or using a fake name or identity to obtain an insurance policy.
Uninsured motorist fraud: Uninsured motorist fraud occurs when an individual files a claim for damages or injuries caused by an uninsured driver, but the accident was either staged or the individual was not actually injured.
Premium leakage: Premium leakage occurs when an individual provides false information or manipulates their insurance policy in order to receive lower premiums. This may involve lying about their driving record, the number of miles they drive, or the type of vehicle they own, among other factors. This can result in insurance companies losing money on policies that are priced too low and can lead to higher premiums for honest customers. Learn more about premium leakage solutions here.

Premium leakage costs the industry roughly $29 billion each year in the United States alone. The impact of this type of auto insurance fraud can also be felt by innocent policyholders, as increased fraud leads to higher premiums and reduced coverage options.

Insurance Agent Fraud

Unfortunately, insurance agents can also be involved in insurance fraud and misrepresentation, often referred to as “agent gaming.” Here are some examples:

Churning: Churning is a type of fraud that occurs when an insurance agent convinces a policyholder to cancel their current policy and purchase a new one, often in order to generate more commission for themselves. This can result in the policyholder paying unnecessary fees and experiencing gaps in their coverage.
Misrepresentation of policy terms: An insurance agent may intentionally misrepresent the terms of a policy, such as the coverage limits or deductibles, in order to convince a customer to purchase the policy. This can lead to misunderstandings and disputes between the customer and the insurance company when a claim is filed.
Falsifying applications: Insurance agents may falsify information on an insurance application, such as the applicant’s health history or driving record, in order to get the applicant approved for a policy that they may not otherwise qualify for. This can lead to policy cancellations and legal consequences for both the agent and the policyholder.

Other Insurance Fraud Examples

Application Fraud and Synthetic IDs occur 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.
Common Health Insurance fraud types are wrongful health insurance billing or unnecessary medical procedures.

With fraud running rampant, using cutting-edge AI insurance fraud detection software and partnering with top fraud detection companies has never been more important than it is today.

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

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.

Bottomline

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.