❖To represent the Bank Loan Modelling.
❖To predict the likelihood of a liability customer buying personal loans at Tera Bank using Tableau.
- Data cleaning, transformation, and modeling
- Visualization using TABLEAU
- Business insights generation
This case is about a bank (Thera Bank) which has a growing customer base. Majority of these customers are liability customers (depositors) with varying size of deposits. The number of customers who are also borrowers (asset customers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns to better target marketing to increase the success ratio with a minimal budget.
❖Using Tableau, the analysis is made between Age, Family, Income, Education, Mortgage with Personal Loan.
❖Symbol Map is used to express the Zip codes in a meaningful Information so that the it can be easily understood.
❖Personal Loan Vs Age/Education is analyzed using a Bar Graph.
❖Personal Loan Vs Family/Income is analyzed using Text Tables.
Mostly Bar Graph is used to express the factors affecting the Personal Loan. ❖There is a chance of 9.4% customers who don’t have securities and 11.5% of customers having securities can avail for personal loan.
❖There is a chance of 9.3% customers who don’t have credit card and 9.5% of customers who having credit card can avail for personal loan.
❖There is an equal chance of 9.4% customers who don’t use online banking and customers who use online banking can avail for personal loan.
❖ PERSONAL LOAN BY INCOME:
- Customers who have taken personal loan have higher income than those who did not take the loan. So high income customers should be a good target for the bank.
❖ PERSONAL LOAN BY AGE:
- We can observe that age is very close to the normal distribution. We need to investigate further between 38-42 age group customers to see what characters would take a loan.
❖ PERSONAL LOAN BY FAMILY:
- We can clearly see that those customers with family size of 3 who had borrowed loans from the bank is greater than other family size.
❖ PERSONAL LOAN BY EDUCATION:
- Customers with education level of graduate and advanced/professional have higher chances of taking a loan. We should Heavily market to those customers.
❖ PERSONAL LOAN BY FAMILY & INCOME:
- Customers who have a family size of 3 or more and have higher income between 120k to 224k are more likely to take personal loan.
❖ PERSONAL LOAN BY AGE & MORTGAGE:
- Most of the customers do not have any mortgage. But, very few customers whose mortgage is over 500K has higher chances to take personal loan.
❖ PERSONAL LOAN BY EDUCATION & FAMILY:
- Customers has a family of three or more with education level of graduated & advanced have higher chances of taking personal loan.
❖ PERSONAL LOAN Vs MONTHLY CREDIT CARD AVG SPENDING:
- Most of the customer monthly average spending on credit cards is between $0 to $2K.There are few customers whose monthly average spending on credit card is more than $3K has higher chance of getting personal loan.
❖ PERSONAL LOAN Vs MORTGAGE:
- Most of the customers do not have any mortgage. However, customers whose mortgage amount $200K and up has higher chances of getting personal loan.
❖ From the analysis the likelihood of a liability customer buying personal loans is predicted.
❖ This analysis will help the company to spend marketing money on the right customers rather than spending it advertising to customers who have higher chances of not taking personal loan.