This is a Data science, sample assignment done by Codersarts Android Experts.
Objective:
Small exercise representing a typical TM task, analyzing customers, finding outliers, and presenting the results to management. The resulting presentation should have a maximum of 4 PowerPoint slides
Recommendations:
Use creativity to approach analytical and business answers to the questions, the software tools used are not relevant, but the logic and final outcome is
Use correct vocabulary and logical categories related to AML discipline to explain results and the rationale of the approach
Answer all the required questions as if you were performing a real assignment from AML stakeholders
Present your results using visual and verbal communication in clear and compelling terms
Keep to the established limits of slides and time
Please include all the scripts used
Data:
Sheet „transactions”: sequence of 160691 fake transactions in relative time order for a month. The format of the table is as follows: customer_id, credit_debit, amount (EUR), CPCC (counterparty country code), product_code
Sheet „country_risk”: list of country codes and associated country risk. The format of the table is as follows: country_code; high_risk_country (Y/N)
Sheet „product_type”: dictionary of products. The format of the table is as follows: product_code, product_type
Complete the following tasks, prepare a short presentation (to management who is interested in AML risks in our customer base) that details the rationale behind your answers
Group the customers based on their transactional behavior (customer segment). Explain what fields you used to select the customer segment and why from an AML perspective.
Identify a minimum of 5 customer IDs connected to transactions that do not fit into the customer's own typical behavior, and/or into the respective customer segments typical behavior. For each of these customers, also explain the reason behind its selection
There are several countries classified as a high-risk country. Given the rule "Total Monthly Incoming transaction amount
from high-risk country > threshold_value",
Recommend an amount threshold to find potential monthly unusual behavior
Identify customer ID's who would trigger the new rule
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