Employment Fraud Outed by Fraudbot™
Employment Fraud – What Is It?
Employment fraud (or employer fraud) is when an individual or group misrepresents their employment in one or more ways. This can include their occupation, income, or even claiming to work for completely fictitious companies. We call these bad employers.
In 2020 Point Predictive noticed a dramatic rise in employment-related fraud due to the COVID pandemic and the resulting unemployment. But as the unemployment rate began to decline, employment fraud continued to increase.
Why? Because it works! It’s a fraudster’s cash cow with no signs of stopping.
Employment Fraud Red Flags
Organized fraud rings increasingly use synthetic identities to commit fraud. Synthetic identities—a mix of real and fake data—are harder to detect. Because fake employment information likely means the identity information is fake as well, it’s important to pay attention to:
- Application volume that doesn’t make sense. The employer is a small shop or shipping business and has more employees than the local Walmart.
- Profession and industry that don’t align. The stated occupation has nothing to do with the industry the employer is supposedly in, such as “Logistics Manager” for a local marketing agency.
How Does Point Predictive’s Fraudbot Work?
Fraudsters often recycle bad employers until they are identified. But Point Predictive’s Fraudbot captures these much faster than your typical fraud analyst can. The Bad Employer Fraudbot leverages Point Predictive’s proprietary data repository to identify the fictitious employers that borrowers—and dealers—use. It does this by:
- Clustering associated risks across lenders to pinpoint similar fraudulent employers. And by being able to review data across the industry, rather than in distinct silos, Fraudbot can identify emerging fraud in the blink of an eye.
- Fraudbot can pick up on not just employer names, but also common phone numbers and addresses. It then adds this information to the Point Predictive negative list which accelerates Fraudbot’s learning.
For example, Fraudbot picked up on a fictitious employer with an increasing velocity of applications across multiple lenders. This particular employer had a high concentration of applications with synthetic properties, enabling lenders to focus on all loan applications from that employer. Point Predictive’s fraud analysts noticed that the business had a very limited web presence and didn’t exist within the state they were supposedly registered in. The employer was also associated with another company that was operating out of a three-bedroom residential home in Houston.
We discovered that the owner of this business had an extensive rap sheet that included fraud and other financial crimes. And while the company identified itself as a small trucking company, Point Predictive discovered over 100 unique “employees” applying for loans from the data contained within the proprietary data repository.
Total Exposure Mitigated: 348 unique applications valued at over $7MM.
Benefits of the Bad Employer Fraudbot
Since launching the Bad Employer Fraudbot, Point Predictive fraud analysts have been able to identify over 1,000 bad employers valued at over $310M in overall exposure. This number continues to grow at an average of 50-100 employers per week.
Let’s face it. Fraud adapts faster than most lenders can catch it. Fraudsters don’t have to worry about bureaucratic red tape or other regulations. The quicker technology evolves, the quicker fraud will, too.
It is becoming increasingly difficult to stay ahead of the fraud curve through strictly manual processes. We’re trying to find a needle in a haystack — Fraudbot shrinks the haystack.
Learn more about Point Predictive’s Artificial and Natural Intelligence [AI+NI]TM approach to improving fraud detection.
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