Next Generation Background Screening, The Future is Here!

The word “robot” has come a long way in the century, following its first usage in a Czech play in 1920. It has been over half a century since the invention of the first modern digital and programmable robot. Artificial intelligence (AI) has transformed how various industries function, and the background screening industry is no exception.

The background screening tech market can be broadly categorized into two categories. “Transactional Background check” includes verification of the applicant based on the information provided by the applicant. This is typically done with state-of-the-art tech solutions to manage global screening. “Transformational background screening” leverages superior employee experience, executive reporting, integration with third-party verification providers, and seamless interaction with the Human resource information system (HRIS) and Applicant Tracking System (ATS).

The application of AI in the transactional background changes how searches are performed. Let us try to understand this with the help of an example. There are quite a few factors that impact the turnaround time from the type of background check, country of the search to be conducted, available information from the candidate, etc.

It is common for organizations to use background check reports before making a hiring decision and sometimes even provide a joining date basis the estimated completion time of the background check. Any change in the estimated completion time impacts several choices made on the ground.

We looked at the data of all completed orders at Neeyamo so far and found some interesting patterns.

Fig.1 Correlation matrix for Country and type of check initiated for the applicant

The above graphic explains the correlation between the search country and the check type, academic verification, employment verification, criminal records check, etc. It becomes easy to predict the tentative completion date based on the historical data. And if there are any deviations, they can be used to train the model further. This keeps the HR personnel ahead of the game and helps them plan better.

 

Fig.2 Source clusters against Actual time taken to complete a background check

Background verification is typically obtained from various sources across the globe. These sources can be government bodies, open-source databases, local authorities, private sector organizations, etc. After our analysis, we were able to cluster the average time taken to complete the background checks. The above image makes it easy to understand how close we are to predicting the turnaround times for background checks globally.

AI, analytics, and big data are paradigm changes in how HR and background screening work. This approach also gives HCM a critical insight throughout the screening process.

While all this can be achieved by leveraging technology, a significant challenge for organizations getting ready for next-generation ERPs (Enterprise Resource Planners) is managing the global verification processes on multiple stand-alone platforms. Only a few corporations have been successful in developing integrated screening platforms.  Thus, companies that offer integrated HR and applicant experience would establish themselves as leaders in the market, and those focusing merely on one or two aspects might perish.

Neeyamo is already gearing up with state-of-the-art screening solutions thanks to our robust technology, expertise, and efficiency, coupled with coverage across 190+ countries.

To know more about how Neeyamo is leveraging AI, big data, analytics, and creating a one-stop tech-enabled solution for global background screening, get in touch with us at irene.jones@neeyamo.com

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