Build a fair loan classifier
Problem Description
You are provided with a dataset of loan applicants. Each row in this dataset represents a single loan application and includes various applicant attributes, loan-specific attributes, and a binary outcome label. This label indicates whether the loan was 'good' or 'bad'. A 'good' loan signifies that the borrower successfully repaid the loan according to the defined business criteria, while a 'bad' loan indicates that the borrower defaulted, became seriously delinquent, or otherwise failed to meet the repayment criteria.
Your task is to design and present a comprehensive classification model intended for predicting loan risk. Your presentation must thoroughly cover the following seven key areas:
- How you would define the modeling objective and target variable.
- How you would approach data exploration and preparation.
- Which specific models you would consider trying and provide justifications for your choices.
- Which evaluation metrics you would utilize to assess model performance.
- How you would determine an appropriate approval threshold for loan applications.
- How you would proactively address concerns related to fairness, bias, and regulatory compliance.
- How you would plan to explain, validate, and continuously monitor the model once it has been deployed into production.
AI interview prep
Core dilemma
Balancing the competing objectives of maximizing predictive accuracy (and thus profitability/risk mitigation) with ensuring fairness, mitigating bias, and adhering to strict regulatory compliance in a high-stakes financial domain.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Build a fair loan classifier
Problem Description
You are provided with a dataset of loan applicants. Each row in this dataset represents a single loan application and includes various applicant attributes, loan-specific attributes, and a binary outcome label. This label indicates whether the loan was 'good' or 'bad'. A 'good' loan signifies that the borrower successfully repaid the loan according to the defined business criteria, while a 'bad' loan indicates that the borrower defaulted, became seriously delinquent, or otherwise failed to meet the repayment criteria.
Your task is to design and present a comprehensive classification model intended for predicting loan risk. Your presentation must thoroughly cover the following seven key areas:
- How you would define the modeling objective and target variable.
- How you would approach data exploration and preparation.
- Which specific models you would consider trying and provide justifications for your choices.
- Which evaluation metrics you would utilize to assess model performance.
- How you would determine an appropriate approval threshold for loan applications.
- How you would proactively address concerns related to fairness, bias, and regulatory compliance.
- How you would plan to explain, validate, and continuously monitor the model once it has been deployed into production.