KYC SQL data analysis project

There are two independent tasks in this challenge: ● Improving KYC ● Catching fraud Your submission should contain: 1. Improving KYC report/presentation of your findings and solutions in pdf / doc / html etc. (up to 5 slides or pages, document can include appendix ) 2. Catching fraud report in pdf / doc / html (up to 2 pages or 3 slides) 3. Supporting materials (calculation files, additional analysis, etc) If the files are too big please upload to an external storage (gdrive, dropbox, etc.). Make sure to allow public access and send the coordinator the link. Deadline: 7 Calendar Days from the date you received the email Task 1: Improving KYC As a financial institution regulated by the FCA, thsi company has the obligation to verify the identity of all customers who want to open an 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 themself 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 as that on the submitted ID. The customer will ‘pass’ the KYC process and get onboarded 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 solutions. Relevant files: • [login to view URL] - Reports of all Facial Similarity checks • [login to view URL] - Reports of all Document checks • [login to view URL] - The API documentation of Veritas explaining some terms used in the reports (you might need to download it to your computer and open it with a browser) The candidate is free to use Excel or any scripting language to parse and analyse the data. Please show all your work (including your code if applicable) and assumptions. Task 2: Catching fraud Financial Crime team has noticed something odd happening over the last few days and have requested additional information to investigate. They asked for you help in analysing the data. You’re not sure what you’re looking for but you’re sure that once you get your hands on that data, you’ll be able to figure it out in no time! In the folder , the data team have provided six files and a message for you. 1. Examine the following SQL query, and explain clearly and succinctly what it means. Will the query work? Explain why or why not. WITH processed_users AS ( SELECT LEFT (u . phone_country , 2 ) AS short_phone_country , u . id FROM users u) SELECT t . user_id , t . merchant_country , Sum (t . amount / fx . rate / Power ( 10 , cd . exponent)) AS amount FROM transactions t JOIN fx_rates fx ON ( fx . ccy = t . currency AND fx . base_ccy = 'EUR' ) JOIN currency_details cd ON cd . currency = t . currency JOIN processed_users pu ON pu . id = t . user_id WHERE t . source = 'GAIA' AND pu . short_phone_country = t . merchant_country GROUP BY t . user_id , t . merchant_country ORDER BY amount DESC ; 2. Now it’s your turn! Write a query to identify users whose first transaction was a successful card payment over $10 USD equivalent 3. Find 5 likely fraudsters (not already found in [login to view URL]!), provide their user_ids, and explain how you found them and why they are likely fraudsters. Feel free to ask us any questions you might have, but before you do, please check out