Analysis of Bank Customers using Dashboard in Power BI
By: Sabrina Lxn
Tags: Banking, Data Analytics, Power Bi, Data Visualization, Marketing

Key Charts, Analysis & Implications

Marketing analysis

One of a good indicator of credit worthiness is the income so higher salary would be a better borrower so we should target promotion and advertising for the population with the highest average salary, Prague, also noticed on the map at the outer circle of the capital Prague is Bohemia Central, the 2nd highest average salary. Since these 2 areas are very close to each other and highly populated. Unlike other regions where people are scatter around a huge area of land as shown in the map.

The huge gap (yellow arrow) between the no. of inhabitants and the no. of bank’s client in that Prague also indicates that this is a large untapped area of potential customers for the bank. We need to gather more data on the preferences of the people in the city to come out with good marketing.

Loan and Credit Analysis

The borrowing rate is the highest for Baby Boomers at 45%, taking up also most ½ of the loan borrowing. These Baby Boomers are our main target for our loan products, another significant group are the Gen X at 37%. The sum of the age from 20–54 makes up more than 80% of the borrowers. We can find out further what type of purchase they are making for the loan and provide more specialised loan with better interest or free items for them.

From further analysis on target age group, comparing the % of good and bad borrowers in each age group around 10–11% would default. (4.99% / 45.02%). We should look into these bad credit borrowers’ traits and improve our credit review at loan approval.

There isn’t a significant relationship to show that a credit card owner will default on their loan given that there are only 2 card owners that default out of 892 cards issued, less than 1%.

From the amount of bad debts, we can see that the Moravia North and South have the highest amount of defaults and late payment. (darkest red = highest amount).

Another trait of bad borrowers is that they have a lower average account balance that the average account balance of $34,060.66. As shown in these brown cells have highlighted all the borrowers that default and make late payments. We can consider adding the borrowers’ account balance as a criterion in our credit review for loan approval. Also, we should avoid giving out loans to those age 55 and above as we can see that those without loan (see brown cell in the “blank” column) have very low average balance of less than $30,000.

The “blank column” also flag out some potential good credit customers for us, highlighted in green. These are current bank customers are age 20–54 who have a good average balance with the bank. The bank can consider targeting them for direct marketing for its loan and card products.

Also from data we can see that the loan granted are short term loan, from 1 to 5 years. The bank can consider granting longer term loan to customers.

Credit Card analysis

Another income generating area would be the credit card, from the date we could see that the bank has been getting more aggressive in their credit card promotion by the increase of the card issued each year.

Using a waterfall chart, we further analyze the increase of card issuance year on year. Where green shows an increase in the card issue as compared to previous year and red shows a decline. A % change on the year on year can also be seen when we hover over the bar.

Limitations

More data on the borrowers could be collected for a better analysis of their credit. Data such as the customers’ credit history, being their payment history, total debt owned , length of credit history, its credit mix to gain more insights of the traits of bad borrowers and effective credit review for the future loan. We can get data from the credit card spending to also find out what these customers are interested in so that we can give gifts that are attractive to them to entice them to take up our loans and credit card products.

The data provided is from 1993–1998, it is less than 10 years and outdated over 20 years ago. More recent data would be able to give us a more relevant insights on the customers behaviours and demographics as technology have improve and make changes to people jobs and lifestyle.

More data on the population in each region such as the age, gender, job of the population would be able to help us to group them into better customer profile.

Video presentation

References & Images credits

Jacobsen, T. (2019, 03 19). MARKETING ARTFULLY. Retrieved from CUSTOMER DEMOGRAPHICS — AGE RANGES, GENERATIONAL NAMES AND NUMBERS: https://marketingartfully.com/customer-demographics-age-ranges-numbers/

Cover page background

https://eeagrants.org/countries/czech-republic

Credit card icon https://www.iconfinder.com/icons/2224480/card_credit_card_debit_card_master_card_icon

Loan icon https://www.businesstoday.in/top-story/what-will-happen-to-your-loan-after-banks-link-it-to-external-benchmark/story/380795.html

Marketing icon https://www.feedough.com/what-is-marketing-strategy-examples-components-planning/