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Analyse a KYC data set

As a financial institution regulated by the FCA, Revolut has the obligation to verify the identity of all customers who want to open a Revolut account. Each prospective customer has to go through a Know Your Customer (KYC) process by submitting a government-issued photo ID and a facial picture of themselves to our partner, Veritas. Veritas then would perform 2 checks:

● Document check: To verify that the photo ID is valid and authentic;

● Facial Similarity check: To verify that the face in the picture is the same with that on the submitted ID.

The customer will ‘pass’ the KYC process and get aboard if the results of both Document and Facial Similarity checks are ‘clear’. If the result of any check is not ‘clear’, the customer has to submit all the photos again.

The pass rate is defined as the number of customers who pass both the KYC process divided by the number of customers who attempt the process. Each customer has up to 2 attempts.

The pass rate has decreased substantially in the recent period.

Please write a report that outlines the root causes and suggests solutions. Relevant files attached:

Reports of all Facial Similarity checks

Reports of all Document checks

The API documentation of Veritas explaining some terms used in the reports (html file)

You are free to use Excel and please show all your work (including your code if applicable) and assumptions.

Навыки: Excel, Python, SQL, Написание отчетов

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