Proactively Overcoming Auto Insurance Fraud
Insurance fraud is a problem that just won’t go away – but could that change? Although bad actors will always try to find ways to cheat the system, artificial intelligence (AI) and machine learning (ML) tools are giving insurers new weapons in the fight against fraud. Although insurers are already using AI/ML to detect fraud, we may only be seeing the tip of the iceberg of what’s truly possible.
Fraud Is Everywhere
Insurance fraud is extremely common. According to the Coalition Against Insurance Fraud, fraud occurs in approximately 10% of all property-casualty insurance losses. These losses hurt both insurance companies and the honest policyholders they serve. Each year, it’s estimated that insurance fraud steals more than $300 billion from American consumers.
Many policyholders commit auto insurance fraud. In some cases, fraud is egregious and deliberate, such as when criminal groups stage auto accidents in order to file fraudulent claims. However, in many other cases, fraud is more subtle, and policyholders may not even realize that the “white lies” they’re telling are considered fraud.
A ValuePenguin survey found that 9% of auto insurance policyholders admit to committing insurance fraud. However, this figure doesn’t represent the true extent of the problem because many policyholders don’t realize that filing false claims or misrepresenting information to insurers is fraudulent. Alarmingly, 35% of auto insurance policyholders say they’ve submitted claims for pre-existing damage, and 21% say they have misled their insurers.
Using AI to Fight Fraud
AI and ML have given insurance companies a new way to fight fraud. According to Business Insider, 60% of insurance companies are already using AI and ML to help detect fraud.
AI and ML are effective at detecting fraud because they can scour large data sets to look for red flags associated with fraudulent activity, and they can do so more efficiently and much more quickly than humans could ever hope to. Once a red flag is noted, the AI/ML program can alert humans to undertake a more detailed review.
For example, an algorithm can be programmed to identify anomalies by comparing a vast combination of variables, typically not detectable by human minds.
Predicting the Likelihood of Fraud
With the power of machine learning, insurers don’t have to settle for identifying fraud that’s already occurred. They can also work to identify policyholders who are likely to commit fraud in the future.
AI can assist the underwriting process to identify characteristics associated with an elevated risk of future fraudulent claims. For example, Property Casualty 360 lists a history of frequent crashes as a possible sign of fraud, along with drivers who fail to contact the police or file a police report after a crash. These scenarios could indicate that the driver is involved in staged crashes. An AI program can be used to search for these patterns.
And that’s just the tip of the iceberg. Machine learning can make connections that humans might miss, opening the door to new ways of identifying risky policyholders. For example, our algorithms have discovered a link between a high chance of claims and applicants who self-report that there is no lien on the car title when third-party data reveals that there is a lien. Although it’s difficult to determine the cause of this link, it’s possible that the dishonesty reflects an increased risk of fraud in the future.
AI can assist the underwriting process to identify characteristics associated with an elevated risk of future fraudulent claims.
And that’s just the tip of the iceberg!
How Soteris Helps Insurers Drive Down Loss Ratios
Soteris uses machine learning to assess a multitude of factors, while also spotting discrepancies and potential omissions. We use this information to assign a rate adequacy score to each individual risk, in less than one second. By using Soteris software, auto insurers can leave all segments turned on, enabling them to write more business while also limiting risk and safeguarding profits.
