Reasons why insurers need AI to combat fraud ahead of time.

AI to combat fraud

The insurance industry consists of more than 7,000 companies that collect more than $1 trillion in premiums annually, providing fraudsters with huge opportunities to commit fraud using a growing number of schemes. Fraudsters are successful too often. According to FBI statistics, the total cost of non-health insurance fraud is estimated at more than $40 billion a year.

Fighting fraud is like aiming at a constantly moving target, since criminals constantly hone and change their strategies. As insurers offer customers additional ways to submit information, fraudsters find a way to exploit new channels, and detecting issues is increasingly challenging because threats and attacks are growing in sophistication. For example, organized crime has found a way to roboclaim insurers that set up electronic claims capabilities.

Advanced technologies such as artificial intelligence (AI) can help insurers keep one step ahead of perpetrators. IBM Watson, for instance, helps insurers fight fraud by learning from and adapting to changing business rules and emerging nefarious activities. Watson can learn on the fly, so insurers don’t have to program in changes to sufficiently protect against evolving fraud at all times.

insurers need Artificial Intelligence to combat fraud

Here are four compelling reasons insurers need to begin to address fraud with sophisticated AI systems and machine learning that can continuously monitor claims for fraud potential:

  1. The aging workforce. There are many claims folks who are aging out and will soon retire, taking years of knowledge with them. Seasoned adjusters often rely on their gut instinct to detect fraud, knowing which claims just don’t seem right, based on years of experience. However, incoming claims staff don’t have the experience to know when a claim seems suspicious. Insurers need to seize and convert that knowledge, getting it into a software program or an AI program so that the technology can capture the experience.
  2. Evolving fraud events and tactics. Even though claims people may have looked at fraud the same way for years, the environment surrounding claims is always changing, enabling new ways to commit fraud. Fraud detection tactics that may have worked 6 months ago might not be relevant today. For instance, several years ago when gas prices were through the roof, SUVs were reported stolen at an alarming rate. They weren’t really stolen however — they had just become too costly to operate. Now that gas prices have gone down, this fraud isn’t happening as often. If an insurer programs an expensive rule into the system, 6 months later economic factors may change and that problem may not be an issue anymore.
  3. Digital transformation. Insurers are all striving to go digital and electronic. As they make claims reporting easier, more people are reporting claims electronically, stressing the systems. At the same time, claims staffing levels remain constant, so the same number of workers now have to detect fraud in a much higher claims volume.
  4. Fighting fraud is not the claim handlers’ core job responsibility. The claim adjuster’s job is to adjudicate a claim, get it settled and make the customer happy. Finding fraud puts adjusters in an adversarial situation. Some are uncomfortable with looking for fraud because they don’t like conflict. A system that detects fraud enables adjusters to focus on their areas of expertise.

In the past, insurance organizations relied heavily on their experienced claims adjusters to identify potentially fraudulent claims. But since fraudsters are turning to technology to commit crimes against insurance companies, carriers need to turn to technology to help fight them. Humans will still be a critical component of any fraud detection strategy, however. Today, insurance organizations need a collaborative human-machine approach, since they can’t successfully fight fraud with just one tactic or one system. To fight fraud, humans need machines, and machines need human intervention

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