Employee background checks are more critical than ever in a time when businesses all over the world are afflicted by vulnerabilities and cyberattacks. Now, every recruiter is obligated to thoroughly investigate a candidate’s background, personal details, past experiences, educational background, references, work history, medical history and criminal histories.
In keeping with current regulations, background verification contributes to the development of a higher sense of confidence and dependability between the employer and the candidate (employee). The conventional methods for checking an employee’s past was labor-intensive, paper-based, expensive, and fully controlled by external background screening companies.
Thankfully, Artificial intelligence (AI) is changing this often-ignored aspect for the better — background checks on employees today are now intuitive, automated, and considerably more accurate. In fact, the employer has much more control over the entire BGV process and complete transparency.
So, in what ways has AI & ML improved employee background checks. Let’s take a look.
#1 Data Can Be Retrieved From Multiple Sources
Businesses that have always depended on conventional methods of background verification are wary of the utilization of artificial intelligence and machine learning for background screening because the application of this specific technology in HR is relatively new.
However, there is no denying that it has assisted businesses in collecting employee background verification information from a number of sources, such as websites, social media and networking platforms, networking platforms. Background checks supported by AI and ML have actually been revolutionary. For instance, it can automatically obtain and interpret employee records in numerous languages from different regions.
When one takes into account hiring for remote and work-from-home positions, automated collection, processing, and verification of employee data becomes even more crucial. Additionally, AI and ML-powered employee screening systems can gather information from social networking sites and record any new digital signatures provided by prospective candidates.
#2 Data Sifting and Evaluation of Threats
The quantity of data pouring in from numerous historical records and sources is immense. A crucial component of the employee background verification process, AL and ML assist in sorting through, cleaning, interpreting, and analysing the employee data.
As a result, false positives and duplicates are eliminated, leading to a focused and concentrated insight gathering process.
By casting a much wider net and evaluating a range of hazards, AI and ML offer algorithms that can precisely map the history of any prospective employee with problematic conduct. Consequently, an organisation’s security, legal, and regulatory frameworks are intact.
#3 Deep Data Analysis in Real-Time
The dependence on historical data, a significant flaw in conventional employment background checks, can be fixed with AI and ML. AI assists in keeping the search current, completely up to date, and in line with the most recent developments by frequently updating databases and scanning all documents instantaneously.
With AI-assisted BGV, you can perform background checks more successfully by covering more data and analysing more data points than ever before.
The abundance of of data and the power to process it is one of AI’s greatest strengths. In fact, these unavailability (or poor execution) of these very USPs of AI & ML was a stumbling block for many organisations.
But AI and ML BGV also offer more than what’s possible. For instance, it aids in the discovery of patterns and connections among various data elements. Had you utilised conventional employee screening tools and platforms, you could have all the information but not inferences to draw.
Speaking of conventional, you will have to stay alert and carry out manual cross-checking too. Think of it as an extra layer of protection or a step to expedite background screening — you won’t have to feed a candidate’s credentials into the verification system if their background feels off. That’s weeding out candidates you don’t want, lessening the burden on the BGV platform and saving time in the process.
So, how do you manually cross-check? Easy, you watch out for these 3 red flags.
Short Stints With Many Companies
When conducting a background check on someone who is “job-hopping,” the results may appear as intermittent, with brief spells of employment at various companies. Similar to prolonged jobless gaps, these brief employment spells at more than one organisation in a short span of time can indicate a lack of dependability.
There are good reasons to work for a company for brief periods of time, such as internships, part-time, or contractual jobs. Ask the candidate or employee about it if you detect this pattern in their employment history.
They might have been forced to move because of a significant employment dispute like harassment or a family emergency that occurred out of state.
Hiring the individual might not be beneficial if they can’t explain why they worked there for such a brief period of time.
Failed Drugs Test or Criminal Record
There are still some industries where medical and drug tests are required, even though they are only required for a very small share of roles these days.
It is certainly relevant for positions where personnel may handle heavy machinery — drug usage poses a significant risk to the organisation and other employees. If this really is the case, a positive drug test result is a critical telltale sign that companies should not ignore.
Cross-referencing criminal histories is also essential. However, bear in mind that not all felonies warrant punishment, and need not hinder someone from obtaining employment, particularly when they have taken the required legal action to make things right.
Nevertheless, some criminal histories could be dangerous for your company.
If you run a logistics company, you probably don’t want to hire somebody with a risky driving history. Similar to how those with a history of embezzlement should avoid accounting, anyone accused of assaulting someone should avoid working with women, children, or the elderly.
If a mishap had to happen at your establishment, looking for convictions connected to your line of work can assist in minimising your company’s liability.
Employment Gaps and Bad Credit History
If a person’s resume shows substantial employment gaps you should exercise utmost caution, for prolonged and repetitive employment breaks can reveal a candidate’s failure to maintain employment.
Long periods of unemployment could be an indication that a staff member is untrustworthy if you want them to be able to play a steady, long-term position in your business.
Additionally, due diligence can reveal work details that may not be listed on an applicant’s CV. While this is frequently done for harmless reasons, such as the fact that a previous job is unrelated to the current application, it could also indicate unfavourable behaviour and personality at the previous place of employment.
It’s believed that about a quarter of employers check candidates’ credit reports as part of the selection procedure. It’s a widespread assumption that businesses run credit checks to seek out credit score information which is untrue.
Numerous pieces of information, such as previous employers’ names and educational affiliations are included in credit histories. Credit reports can be used to confirm someone’s identity and get a glimpse of their financial situation.
Financial security may not be a major concern for some businesses, but it might be for others. Just think about it; would you ever want to hire a finance professional who has made bad financial calls all his life?
Employee screening has come a long way. Today, with the help of AI and ML, employee vetting has become fast, accurate, and cost-effective.
Keeping in mind that fraudsters today have become extremely creative, automated employee screening and being ever watchful of the red flags can make all the difference.