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📘 Credit Risk Assessment – Use Case Overview

What Is This Use Case About?

This use case helps a bank decide whether to approve or reject a mortgage loan application. It uses a credit risk assessment model to analyze a customer's financial situation, employment, and the property they want to buy.

The goal is to make smart, data-based decisions that protect the bank while giving customers fair access to loans.

🔍 Real-Life Scenario

Imagine you're a loan officer at a bank. A customer wants to borrow $200,000 to buy a house worth $250,000. You need to figure out:

  • Can they afford it?
  • Is the loan safe for the bank?
  • Should the loan be approved, rejected, or approved with conditions?

This prompt walks through how to assess that decision using structured data and policy rules.

🧩 What Information Is Used?

We look at two main types of information:

1. Customer Financial Profile

  • Credit score (How reliable they are with credit)
  • Debt-to-income ratio (How much debt they have vs. income)
  • Loan-to-value ratio (How big the loan is compared to the house value)
  • Employment status (Are they employed full-time, part-time, etc.?)
  • Income and savings
  • Other debts (e.g., car loans, credit cards)

2. House & Market Information

  • Property value
  • Neighborhood safety and school ratings
  • Real estate market trends
  • Offered interest rate

🧠 How Does the Decision Work?

Step 1: Rate Each Criterion

Each factor is rated from A (best) to D (riskier). For example: - A credit score above 700 = A - Debt-to-income ratio between 30–39% = B - Employment is full-time and stable = A

Step 2: Create a Composite Rating

Combine all the individual ratings into one string, like ABACA.

Step 3: Check Bank Policy

Each composite rating has a defined: - Maximum loan amount - Minimum interest rate

Using ABACA, the bank knows how much they can safely lend and the lowest interest rate they can offer.

Step 4: Compare With Customer Request

Check if the customer’s requested loan and the offered interest rate are within allowed limits.

Step 5: Make a Decision

Based on: - Their rating - Their savings and debt - The condition of the market and property

The bank decides to: - ✅ Approve the loan - ⚠️ Approve with conditions (e.g., larger down payment) - ❌ Reject the loan

📝 Example

Customer: - Credit Score: 720 → A - Debt-to-Income: 35% → B - Loan-to-Value: 80% → C - Employment: Full-time → A - Market Trend: Stable → B

Composite Rating: ABACB

Policy Table Lookup:
ABACB allows a max loan of $300,000 and minimum interest rate of 4.25%.

Customer Request:
Loan of $200,000 at 4.00% — both are within limits, so the loan may be approved.

💡 Why This Matters

This use case is important because it: - Helps banks avoid risky loans - Ensures fair treatment of customers - Makes decisions consistent and transparent - Can be automated to save time and reduce errors

👤 Who Is This For?

  • Bank underwriters and analysts
  • AI model developers for financial services
  • Risk and compliance officers
  • Anyone interested in how loan decisions are made using data