There is a consumer finance company which specialises in lending various types of loans to urban customers.
Like most other lending companies, lending loans to ‘risky’ applicants is the largest source of financial loss (called credit loss). Credit loss is the amount of money lost by the lender when the borrower refuses to pay or runs away with the money owed.
The main objective here is to reduce the risk of the credit losses.
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As mentioned above, There is a consumer finance company which specialises in lending various types of loans to urban customers.
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Like most other lending companies, lending loans to ‘risky’ applicants is the largest source of financial loss (called credit loss). Credit loss is the amount of money lost by the lender when the borrower refuses to pay or runs away with the money owed.
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The main objective here is to reduce the risk of the credit losses.This can be done by analysing below:
1) If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company. 2) If the applicant is not likely to repay the loan, then if the loan is approved results in loss to company. -
So the main objective here is to analyse the data using EDA(Eploratory Data Analysis) techniques with the dataset.
- Default Rate : There is a total of 14% of default Rate.
- Grade : Most of the default accounts are from B,C and D.
- Sub Grade : Applicants from the sub grade B3,B4,B5 tend to be more as defaulters.
- Term : The longer the Term, the more default rate in terms of loan amount.
- Employee Length : Applicants who are having an eperience above 10 years are contributing more to charged off percent.
- Open Accounts : Open accounts less than 5 and more than 10 show lower default tendency.
- Home ownership : Loan applicants with rented house with rented house are contributing more to defaulters.
- Purpose : debt_consolidation and credit cards contribute highest in Purpose.
- Verification : Members who are verified also appear higher in number.
- DTI: High Debt-to-Income (DTI) ratios are associated with defaults.
- Loan Amount: 72% of the default borrowers have loan amount till 15k.
- Interest Rate: Applicants who are having moderate interest rate tend to be more defaulters.
- Annual Income: Higher annual income correlates to chance of reducing defaulters.
- Loan processing volume: Most of the loans issued in second half of the year. December month is highest month.
- Python - version 3.12.4
- Matplotlib - version 3.8.4
- Numpy - version 1.26.4
- Pandas - version 2.2.2
- Seaborn - version 0.13.2
- This project was inspired by EDA session on Upgrad by S Anand
- UpGrad tutorials on Exploratory Data Analysis (EDA) on the learning platform
Created by @AishwaryaGobburi and @GirishKolhe - feel free to contact us!