Developing a Loan Default Risk Prediction Model

Requirements Clarification & Assessment

Clarifying Questions:

1. Loan Types: What specific types of loans are offered (e.g., home loans, auto loans, personal loans)?
2. Client Base: Who are the primary customers (e.g., first-time buyers, high-net-worth individuals)?
3. Current Default Rate: What is the current rate of non-performing loans?
4. Regulatory Environment: Are there any regulatory constraints or compliance requirements?
5. Data Availability: What is the quality and completeness of the available data?

Assessing Requirements:

• Bank Type: Assume the bank is a retail B2C mortgage bank.
• Data Access: Full access to the bank's database, including customer demographics, loan details, and external macroeconomic conditions.
• Objective: Develop a predictive model to assess the likelihood of loan defaults to minimize risk and inform lending decisions.

Data Considerations:

• Customer Data: Demographics, transaction frequency, trustworthiness scores, salary, job, and properties.
• Loan Data: Loan request dates, amounts, interest rates, purpose categories, acceptance rates, durations, and payment types.
• External Factors: Macroeconomic indicators that may affect loan repayment capabilities.