The application of predictive analytics for loan approval and management is an important aspect of decision-making.

1. Loan Approval
Predictive Analytics Use Case

2. A bank or financial company can determine what type of
loan applicants are most likely to default, and which ones
are most likely to pay on time. This technique can be used
to predict whether a particular customer will default, and
when it will happen and to understand why particular
customers default.
Loan Approval
Sample Application
Description

3. Target
Past history and loan status of
a customer
Loan Approval
Sample Application

4. • Demographic info about customers – gender, age
education, marital status, occupation
• Customer bank account details
• Past history and existing loan details
Influencing
Factors
Loan Approval
Sample Application

5. Binary Logistic Regression is the method used for classifying
numeric and/or categorical data into two groups based on
predefined categories.
• Higher classification accuracy (>=70%) means the results
are reliable and accurate.
• Lower classification accuracy (<70%) means the model
needs to be rebuilt using different input parameters.
Algorithm(s)
Loan Approval
Sample Application

6. Model
Visualisation
Loan Approval
Sample Application

7. Model
Visualisation
Loan Approval
Sample Application

8. Model
Visualisation
Loan Approval
Sample Application

9. Model
Summary
Loan Approval
Sample Application

10. Interpretation
Loan Approval
Sample Application

11. Result
• Likelihood/ probability of a default.
• Flag containing ‘likely to default’ and ’unlikely to default’
information with ‘yes’ and ‘no’ values.
Loan Approval
Sample Application

12. Result
Default prediction with probability value can be performed
using APPLY functionality shown below
Loan Approval
Sample Application

13. Result
Loan Approval
Sample Application

14. Loan Approval
Predictive Analytics Use Case
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Smarten – Loan Approval Use Case - 2019