Bank Loan Defaulter Case Study

Purpose :

• To get hands on experience with huge datasets using detailed Exploratory Data Analysis.

• To learn preparing presentations based on the analysis done, to present them to the Business.

• This was a group case study (Group members - Satvik Yadav, Antara Chatterji ) ; done as part of the Executive PG Diploma program of UpGrad in collaboration with IIIT Bangalore .

Dataset link :

You can find the dataset here

Business Problem:

This case study aims to give an idea of applying EDA in a real business scenario. This case study is intended to develop a basic understanding of risk analytics in banking and financial services and understand how data is used to minimise the risk of losing money while lending to customers.

This case study aims to identify patterns which indicate if a client has difficulty paying their installments which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc. This will ensure that the consumers capable of repaying the loan are not rejected. Identification of such applicants using EDA is the aim of this case study. In other words, the company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment.

Final Conclusions after detailed analysis:

• While giving loans, Bank can consider Commercial Associate/State Servant/Student NAME_INCOME_TYPE as they mostly pay back the loan.
• Bank can also consider Working NAME_INCOME_TYPE in some purposes like 'Buying a new car' ,'Buying a holiday home/land' as they have very small chance of being a defaulter.
• Though 'Repair' Purpose have higher chance of loan repayment, but they also have highest chance of being a defaulter. So, bank should be more cautious in paying the loan for 'Repair' Purpose.
• Bank can consider giving loan to Housing type - With Parents, as they are likely to give loan payment.
• Bank should get as many customers for loan with purpose 'Buying a home' and income type State Servant, as they are very likely to pay the loan.

Important Points to remember while solving the Assignment :

• The commands are syntactically correct.
• The output of the code is correct in terms of the question and format.
• The data frame has been thoroughly inspected using the taught commands.
• In the case of dataframes, the results contain the same rows and columns as expected
• Regarding plots, making appropriate charts with the mentioned libraries and getting the right trends.Writing clear and concise inferences for the charts wherever asked.
• The code is concise. Wherever appropriate, built-in functions are used instead of making the code longer (if-else statements, for loops,loc/iloc ).
• If new variables are created, the names are descriptive and unambiguous. Following the variable/dataframe names mentioned in the question wherever it is provided.
• Charts are neatly formatted including proper chart sizes, annotations(if required) and labelling.
• Mention the inferences for each of the graphs after thoroughly inspecting them.