Identity Resolution for Employee Screening Beats Background Checks
Good help is hard to find, so the saying goes. Now thanks to identity resolution solutions, retailers are benefiting from this corollary: Hardened criminals are also good to find. Before the hire and even after the hire, it’s good for a company’s bottom line if they can identity potential problems before they happen.
According to the DOJ, employee theft is growing 15 percent annually and the Department of Commerce says that a third of all employees steal from employers. Hence the nationwide rise in employers making background checks a pre-condition to hire. But there are several inherent problems in standard background checks. For example, job candidates can:
1. Lie about convictions
2. Use a fake identity
3. Have hidden relationships with known criminals
4. Be the perfect employee but certain life changes after the hire can make a thief out of an honest person.
Lying on resumes and job applications is widespread. The San Francisco Chronicle pointed to a survey a while back that found that a fourth of all job candidates lied or gave erroneous information. But background checks only disqualified 13 percent of these applicants. The hard part in catching a liar is that the proof is often concealed in various data silos or legacy systems that can’t be accessed. Since 9/11, the authorities have gotten much better at sharing conviction records. Unfortunately, way too many shoplifter apprehensions don’t lead to convictions. Retailers do, however, keep databases of shoplifting suspects. But that data is worthless if it can’t be accessed due to complex data management issues or even simple constraints like the inability to access data at a sister store.
For employment pre-screening, identity resolution solutions solve this problem by easily gliding across multiple data sources (e.g., lists of known shoplifters, bad check writers, vendors, returns, perpetrators of organized retail crime, and LERPnet) and finding linkages that indicate fraud.
Identity resolution also solves the problems with fake identities and hidden relationships. By applying sophisticated similarity search techniques to resolve multiple identities, a single view of an individual highlights otherwise hidden relationships discovered by analyzing multiple attributes like common phone numbers and addresses.
But what about when good employees go bad due life changing events like financial difficulties, divorce or addictions? Could any of these circumstances have motived this Target Loss Prevention pro to allegedly turn off the security cameras and steal $14,000 worth of merchandise? While it’s true that personal issues do not necessarily a thief make. But if your heretofore great employee is being added to negative databases in his off-time, wouldn’t you want to know about it?
For employee monitoring, an identity resolution solution can check periodically to detect employees who may have been added to these sources of information after being hired.
Background checks are good in theory. But as Yogi Berra said, “In theory, there is no difference between theory and practice. But in practice, there is.”
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