First Home Buyer Credit Decision

◷ Reading Time: 5 minutes

This example is about checking the eligibility of a first home buyer loan for a given individual.

The model will be taking certain decisions on regular income/expenses, dole payments, affordability, etc. Based on those factors it will decide whether the customer passes the credit decision assessment.

Running the Sample

1. Open the file First Home Buyer Credit Decision.xml

2. Click on Logic Run Template.

3. Click on one of the given templates.

4. Click Debug.

5. Click Next Step to go step by step.

6. You will see the output which is the calculated health premium under the Parameters window.

Process Steps

These are the process steps.

1. Read the income and expenditure data of the customer.
2. Check for the income consistency/ frequency, minimum income, and dole payments to make the income decision.
3. Calaculate income expense ratio to make the expence decison.
4. Calculate the efficiency ratio to get the credit score.
5. Determine the final outcome of the credit decision.

Project Description

DRD

• First Home Buyer Credit Decision.xml: Define the decision hierarchy to make the final credit decision

Decision Table

• Income Assessment Period.xml: Determine the number of transactions to be read according to the payment frequency

Generic Flow

• Average Regular Weekly Income .xml: Calculate Average Regular Weekly Income

Natural Language

• Final Credit Decision Outcome.xml: Determine the final credit decision outcome

Glossaries and Concepts

• Concept.xml: This Fact Concept defines context of the project
• BusinessTerms.xml: This is the Business Glossary that defines business terms
• BoxedExpressions.xml: This Boxed Expressions document determines expressions and constant values

The Project Design

The main Decision Requirement Diagram (DRD) shows how each decision is made.

The input datasets for this model contain,

• Income
• Expenses
• Affordability

Based on those data, we will be determining certain parameters by calculating the average income, expencese and efficiency ratio etc.

Certain decision nodes consist of expressions directly instead of connecting logic documents. The expressions used here are defined in a Boxed Expression document.

Determine the income assessment period using a decision table.

Final credit decision outcome is determined using Natural Language.

Boxed Expressions and Business Glossary documents are used to define the reusable expressions/ terms.

The Fact Concept is used to define the context.

Download the Project

Use the attachment at the end of the page to download the sample project.