Fraud Prevention System Catches More Who Are Innocent Than Guilty
Is there a better way to detect fraud than the current fraud prevention systems?
By Xan Johnson, Marketing –
With the arrival of the COVID-19 pandemic, the number of claims for unemployment benefits has skyrocketed. What has been interesting, though, is seeing how the current measures put in place to counteract fraudulent claims have held up. The simple response — not so well.
Through these systems applicants submit various documents that prove their identity. The problem is that even the most minor of discrepancies flags the application as being potentially fraudulent, and it is given to an employee for a manual review. Simple things, like having a middle initial instead of a full middle name, can mean waiting several more months. A nonprofit organization reported that around 40% of applicants were flagged for a manual review. This work starts to add up, and employees are left with 10 times more work than what they had before the pandemic. With so many applicants and so many false positives, there needs to be a better system to seek out fraudulent behavior.
The main problem with this system is that it is preventing innocent people from not receiving needed money, while barely catching any criminals. 40% of people are flagged as fraudulent, yet less than 0.5% of applicants are found guilty after further review. Is it worth making millions of people wait to receive their aid just to catch a few criminals? Sending in an application and receiving financial aid, a process that should only take a few weeks, ends up taking months.
One possible solution to this dilemma is a new technology created by the company Converus®, called EyeDetect®. It is a lie detection device that can determine if an individual is guilty of fraudulent behavior in just 30 minutes by tracking involuntary pupil dilations caused by lying. It may appear similar to a polygraph, only it is much less invasive, more economic and there is no room for human error. Imagine the time and money that can be saved if a machine could check for fraud as opposed to humans. The government employees would have the final say, but implementing EyeDetect in this domain could help sift out those who were wrongfully flagged as fraudulent.
