New Technologies for Better Background Screening

Background screening is the need of the hour. With banks getting scammed by employees and a lot of cab drivers being accused of unruly conduct, employers need to be extra careful with whom they hire. It is difficult for HR professionals to conduct background screening on candidates all by themselves, especially when it comes to conducting online address verification online of the employees. A lot of the candidate’s information is available online in public databases which when accessed can save on physical resources. However, to manually go through all the available data can be very time-consuming.

Here’s how the technology works for background screening:

1. Quick Data Accessibility: With artificial intelligence (AI), large amounts of data can be processed in just a few minutes. By entering a person’s name into a search engine, all matches to that name from databases across the web will be thrown up in a matter of seconds. More finely tuned systems can streamline search results even further and give you results that match more than one criterion. If a simple Google search can take you to a person’s Facebook, Twitter, LinkedIn and Instagram profile, imagine what can be done with search algorithms that have been developed specially for background verification.

2. Smarter Data Analysis: Combing AI with machine learning can now take background screening to a new level by prioritizing data according to relevance. It is no longer enough to get all the information about a person available online. No one has the time to select what data is useful to a background verification and what is not. New technologies can now analyse data and present the hiring company with the more useful data first. Criminal records, address verification, past employment verification can all be screened without having to find out where the candidate holidayed last summer.

3. Analysis of risk and threats: One of the main purposes of a background verification is to make sure that you are hiring a trustworthy candidate. What new technologies can do is to pick up on phrases like ‘termination of employment’ and gather all data relevant to that point. A search result like this makes it much easier for HR to understand why the person left/was fired from their last place of employment and if they are reliable enough to hire for the new role. If an employee was fired on suspicion of siphoning funds, but it was not proven, and no police complaint was made, then it won’t show up in criminal records. When machine learning analyses risks and threats, this shortfall can be overcome.

4. Automatic Scoring: By taking into account certain key performance factors when doing a background verification, AI can quickly and efficiently generate a score for the candidate. Looking at past performances, employment history, mentions of awards, accolades or published papers, the program could give the applicant a higher score. On the other hand, furnishing false details or discrepancies in the past records could bring down a candidate’s score considerably.

AuthBridge has a background screening system in place that uses advanced AI and machine learning techniques to pull up records of potential employees. By using the system, it take