Build a fair loan classifier
Company: Zest
Role: Data Scientist
Category: Machine Learning
Difficulty: hard
Interview Round: Onsite
You are given a dataset of loan applicants. Each row represents one loan application and contains applicant attributes, loan attributes, and a binary outcome label indicating whether the loan was good or bad. A good loan means the borrower repaid according to the business definition; a bad loan means the borrower defaulted, became seriously delinquent, or otherwise failed the repayment criteria.
Design and present a classification model for predicting loan risk. Your presentation should cover:
1. How you would define the modeling objective and target.
2. How you would explore and prepare the data.
3. Which models you would try and why.
4. Which evaluation metrics you would use.
5. How you would choose an approval threshold.
6. How you would address fairness, bias, and regulatory concerns.
7. How you would explain, validate, and monitor the model after deployment.
Quick Answer: This question evaluates competency in supervised classification and credit risk modeling, covering data exploration and preparation, model selection, evaluation metrics, threshold setting, fairness and regulatory considerations, interpretability, validation, and monitoring.