Credit Risk Analysis

Building a predictive model to assess credit risk and reduce loan default rates.

Scenario interviews help us assess your thought process, creativity, and comfort with ambiguity. At the same time, the scenarios represent real client engagements so they allow you to gain insight into the work we do.

Business situation
Problem statement

A financial institution wants to improve its credit risk analysis process to make more informed lending decisions. The institution has historical data on past loan applications, including details on borrowers, loan attributes, and whether the loans were paid back in full or defaulted. The goal is to build a predictive model that can accurately assess the credit risk of new loan applicants and help reduce the default rate.

Business situation
Problem statement

You are given a dataset containing the below attributes. Answer the questions in the scenario after reviewing the below dataset.

  1. Applicant's Age: Age of the loan applicant.
  2. Applicant's Income: Monthly income of the loan applicant.
  3. Loan Amount: The amount of the loan requested by the applicant.
  4. Loan Term: The term of the loan in months.
  5. Credit Score: The credit score of the applicant.
  6. Employment Years: Number of years employed.
  7. Loan Purpose: The purpose for which the loan is requested (e.g., education, home, car).
  8. Loan Status: The target variable indicating whether the loan was fully paid ("Good") or defaulted ("Bad").