Loan Approval Models

Requirements Clarification & Assessment

1. Understanding the Problem

   • Objective: Automate the loan approval process to efficiently handle numerous requests and leverage data to predict loan defaults.
   • Business Goal: Minimize loan defaults by accurately predicting customer creditworthiness.
   • Target Variable: Binary outcome indicating loan approval (1 for approval, 0 for rejection).

2. Data Insights

   • Input Features: Customer income, credit score, employment history, debt-to-income ratio, etc.
   • Data Characteristics: Identify if the dataset is balanced or imbalanced regarding loan defaults.

3. Model Requirements

   • Classification Model: Predicts binary outcomes based on customer data.
   • Performance Metrics: Identify the best metrics to evaluate model performance, considering business objectives and data characteristics.

4. Comparison Needs

   • Model Comparison: Evaluate two models for loan approval and default prediction.
   • Timeframe: Consider a suitable period based on loan repayment schedules (e.g., 6-12 months).