Question

Elsa is a data analytics intern at a financial firm. Her company has obtained their customers bank transaction records recently. Elsa has been asked by her manager to find out all the attributes (variables) that can be delivered from those bank transaction records for creating the machine learning algorithm. The machine learning algorithm will help Elsa's firm to determine whether someone is likely to get a loan approval or not.
List 5 attributes (variables) and explain why they should be considered in the Machine Learning Assignment

Answer 1

Machine learning algorithms

ML algorithms are widely used for calculating whether an applicant qualifies for a loan or not.

Computer engineers create algorithms which can effect lending decisions of lenders.

Many companies use machine learning for making finance decisions.

Given below are five attributes that should be considered by Elsa for creating an ML Algorithms-

1. Saving Part of your Income

Checking the Statements gives bankers an idea about the financial health of the applicant. These statements gives an idea about the Monthly Expenses & Savings of the Applicant. These are necessary to ensure that the applicant is worthy of the loan and can repay the debt in time.

2. Current Financial Status

If there are bounced cheques, this may adversely effect in getting a loan, as this is an indication of the fact that you are not financially well, and will not be able to pay on time. Timely payment of credit cars also matter.

3. Existing Liabilities

Your lender will want to see if you are left with sufficient money to repay them the loan. So your existing liabilities are also analysed for getting an understanding of your financial wellness.

4. Already existing Debt

The payment of your existing debt also plays a role and let the lender know that you will be able to repay their loan or not.

5. History of Credit

Lenders will always wish for borrowers to have good financial status. You need to have a good credit score to get a new loan. Your repayment status on your prior loan contribute a lot.