Credit Analysis

Credit Analysis

Consider you are a Data Analyst with a private bank or a loan distribution firm. Your organization receives many applications in a given day. In order to process the applications, you sometimes miss out on accepting applications from people who are able to pay loans in time and end up sanctioning loans to those who later turn out to be defaulters.

You are now provided with two datasets:

1. Current_app: This file gives you information on the existing loan applications. Whether or not clients have payment difficulties
2. Previous_app: This file contains information on the previous loan applications with status details of the previous applications being Approved, Cancelled, Refused or Unused offer.

Data Analyst is really fun! You get to select how to approach the problem with the defined objectives. Here in this analysis, you are required to identify the loan application patterns and recommend the bank/firm on how they can build their loan portfolios and avoid giving loans to defaulters. You have to recommend ways in which the bank/firm can maximize their loan sanction applications to the clients who can repay the installments. This can be really tricky since there could be new clients with no credit history and can take advantage of the bank and turn out to be defaulters in the future.