Author: Tarun

Employee Background Verification – Discrepancy Statistics for February 2017

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Here are the high level monthly discrepancy statistics for February 2017.
We observed that 3.8% of the overall number of cases had discrepancies (could be major or minor).

Case-Feb2017

Next, let us look at the breakup of the discrepancies to identify further which types of components (checks) were the source of these discrepancies. These discrepancies were in education and employment checks, with employment discrepancies being the most significant and accounting for 63.6% of all discrepancies.

Component-Feb2017

Three mega projects revisited – Aadhaar, NAD, CCTNS

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3-mega-projects-background-checks

It was more than six years ago that we had published this blog covering the three mega projects which had the potential to tranform background verifications in India.

Let’s review the status of these projects.

Aadhaar is now a mainstream identity platform, which is being used in a wide variety of processes and applications. In fact, Aadhaar is one of the largest such platforms in the world and shows no signs of stopping in terms of enrollments and usages. We have also released our offering for Aadhaar verification.

National Academic Depository was put on high priority by the current government and has been launched. More universities and records are being added to it. We continue to stay tuned for its enrollment for background verification providers.

Crime and Criminal Tracking Network and Systems has been an ongoing project for quite some time. The project status sheet indicates that the State Citizen Portals for the project has been launched in various states. Once again, we continue to stay tuned for its availability for background verification providers.

In summary, the three mega projects have done quite well and with the inclusion of Aadhaar as a unique key across the three projects, it will become possible to have a unified view across different types of background checks.

 

Employee Background Verification – Discrepancy Statistics for January 2017

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Here are the high level monthly discrepancy statistics for January 2017.
We observed that 1.03% of the overall number of cases had discrepancies (could be major or minor).

Discrepancy-Case-Jan17

Next, let us look at the breakup of the discrepancies to identify further which types of components (checks) were the source of these discrepancies. These discrepancies were in address, education, employment and reference checks, with education discrepancies being the most significant and accounting for 50% of all discrepancies.

Discrepancy-Component-Jan17

Employee Background Verification – Discrepancy Statistics for December 2016

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Here are the high level monthly discrepancy statistics for December 2016.
We observed that 1.22% of the overall number of cases had discrepancies (could be major or minor).

Dec16-Case

Next, let us look at the breakup of the discrepancies to identify further which types of components (checks) were the source of these discrepancies. These discrepancies were in education and employment checks, with employment discrepancies being the most significant and accounting for 75% of all discrepancies.

Dec16-Component

Employee Background Verification – Discrepancy Statistics for November 2016

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Here are the high level monthly discrepancy statistics for November 2016.
We observed that 2.47% of the overall number of cases had discrepancies (could be major or minor).

Nov-Discrepancy-Case

 

Next, let us look at the breakup of the discrepancies to identify further which types of components (checks) were the source of these discrepancies. These discrepancies were in education and employment checks, with education discrepancies being the most significant and accounting for 67% of all discrepancies.

Nov-Discrepancy-Component

 

Employee Background Verification – Discrepancy Statistics for October 2016

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Here are the high level monthly discrepancy statistics for October 2016.
We observed that 1.63% of the overall number of cases had discrepancies (could be major or minor).

Oct-Discrepancy-Case

 

Next, let us look at the breakup of the discrepancies to identify further which types of components (checks) were the source of these discrepancies. These discrepancies were in education and employment checks, with education discrepancies being the most significant and accounting for 71% of all discrepancies.

Oct-Discrepancy-Component

Employee Background Verification – Discrepancy Statistics for August 2016

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Here are the high level monthly discrepancy statistics for August 2016.
We observed that 6.92% of the overall number of cases had discrepancies (could be major or minor).
Discrepancy-Statistics-Aug2016-Fig1
Next, let us look at the breakup of the discrepancies to identify further which types of components (checks) were the source of these discrepancies. These discrepancies were in education and employment checks, with education discrepancies being the most significant and accounting for 80% of all discrepancies.

Discrepancy-Statistics-Aug2016-Fig2

Employee Background Verification – Discrepancy Statistics for June 2016

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Here are the high level monthly discrepancy statistics for June 2016.
We observed that 6.41% of the overall number of cases had discrepancies (could be major or minor).
Monthly-Discrepancy-June16-Image1
Next, let us look at the breakup of the discrepancies to identify further which types of components (checks) were the source of these discrepancies. These discrepancies were in education and employment checks, with education discrepancies being the most significant and accounting for 89% of all discrepancies.
Monthly-Discrepancy-June16-Image2

Employee Background Verification – Discrepancy Statistics for May 2016

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Here are the high level monthly discrepancy statistics for May 2016.
We observed that 5.99% of the overall number of cases had discrepancies (could be major or minor).Discrepancy-Case-May16

Next, let us look at the breakup of the discrepancies to identify further which types of components (checks) were the source of these discrepancies. These discrepancies were in education and employment checks, with education discrepancies being the most significant and accounting for 88% of all discrepancies.

Discrepancy-Component-May16

 

Employee Background Verification – Discrepancy Statistics for April 2016

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Here are the high level monthly discrepancy statistics for April 2016.
We observed that 5.05% of the overall number of cases had discrepancies (could be major or minor).

April 2016 Case Discrepancy Stats

Next, let us look at the breakup of the discrepancies to identify further which types of components (checks) were the source of these discrepancies. These discrepancies were in education, address and reference checks, with education discrepancies being the most significant and accounting for 79% of all discrepancies.

April 2016 Component Discrepancy Stats