big east bank

BigEast Case Group 6

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BigEast Bank: Credit Card Approval

Group 6 Project:

Datla, Aditya

Koya, Siva

Merla, Narendra

Pappoppula, Veerendra


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Executive Summary

This report provides an analysis and evaluation on how BigEast Bank’s decision on

approval or denial of a credit card to an existing customer, might affect its overall customer

relations and thereby in turn its profitability.

Business Problem:

Should BigEast consider changing its credit card acceptance criteria?

To find a solution for the Business problem, we have addressed the following issuesin the

report:

What role does the high denial rate of credit card play in customer defection?

Is the average profit between accepted and denied credit cards customers

significantly different?

Product lines/ sources that contribute to profit from customers that are being

declined credit cards?

Is the bank risking potential profits by declining credit cards to profitable

customers?

Analysis:

In this report we have used the data of 5,538 customers, who have applied for a credit

card during the month of January 2001. This data set is obtained by consolidating the data from

the Credit Card Marketing department and data on customer profitability.

The average profitability for the rejected credit card applicant is 31.57.

The average profitability for the accepted credit card applicant is 23.39.

82.2 % of customers (4,556 out of 5,538) were denied a credit card.

The average cost incurred on a credit card approved customer is 9.69 and that of a

denied customer is 10.39.

A major share of the company’s profitability comes from the Over Draft Fee of the

rejected customers.

9.06% of customers (413 out of 4,556), who were declined a credit card have defected

from the bank in the next 8 months.

Results & Findings:

The following are the findings and results obtained by running various statistical tests on

the data

It is observed that the credit card rejected customers seem to be more profitable when

compared to the accepted ones by 8 USD.


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Credit card rejected applicants have paid more Over Draft fee when compared to the

ones whose applications have been accepted, which has been the main contributor for

the profits. The balance of amount present in the credit card rejected customers

significantly low compared to the balances of the accepted customers.

Though 93.6% of customers whose credit card application have been rejected have

defected from the bank. Out of the 4556 declined customers only 413 (9.06) customers

are defected. Hence we can say that relation between high denial rate and customer

defection is weak.

The customers who are charged with high Overdraft fee and high monthly service

charge fees when compared to the other customers are more likely to defect.

Customers who had more number of accounts with the bank and customers who

generated more net revenue for the bank were more likely to stay with the bank.

Recommendations:

The bank should be more transparent about its credit card approval process thereby improving the customer relations.

Make use of available data like customer profitability from all the departments in the credit card approval process.

BigEast should consider running promotions to encourage existing customers to open new accounts.

Should take calculated risks in the approval process.

Encourage existing customers to open new accounts with the bank.

Limitations:

The limitation of this report is the analysis on insufficient data. Extensive data from other

departments would have further helped in analyzing the reasons for customer defections.


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Report

Summary Statistics:

Looking at the data, we have made the following observations on the customers have applied for the

credit card.

Out of all online applications for the credit card, about 82.26% of applications are rejected. The

acceptance of the application depends on customer’s credit worthiness. The major task ahead is to find

whether the rejection has any effect on the customer defection. As far as the profitability is concerned,

the difference between averages of the rejected and accepted customers is 8.18 USD and the

significance of the difference has to be tested using the two-sample t-Test.

Testing the average profitability of the rejected and accepted customers for the

credit card application.

Two sample t-Test:

Null hypothesis (H0): No significant difference in profit between customers who were denied and

approved credit.

Alternative hypothesis (H1): H0 is False.

Assuming 5% level of significance, from the T-test for unequal variances we can see that the two sided

probability value is <.0001 which is less than 0.05 and thus we reject the null hypothesis. Hence there

exists significant profit difference of 8.17USD between customers who were denied and approved

credit.

But there are other variables that might have an effect on the profitability such as additional charges like

service charges, overdraft fess and the overdraft fee on the returned items. Hence the average

profitability difference across the customers would be better explained by applying regression on the

profit with these control variables.


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Multiple Regression:

From the above results it is evident that the overall model looks

significant (P < .0001) and also the other control variables have an

significant effect on the profitability. Also it is observed that the

rejected customers seem to be more profitable when compared to the

accepted ones by a margin of around 8 USD. The difference in the

average can be attributed to either the greater number of rejections

when compared to approvals in the given sample or it might be

because of some other reasons contributed by other variables like fee

and balances.

Effect of Fee and Balances on greater profitablity average for rejected applicants

From the above statistic summary results of average ODFeeRev and Balances for the overall applicants it

is evident that the credit card rejected applicants have paid more ODFee when compared to the ones

whose applications have been accepted. Also the balances of the rejected customers are much lesser

than the balances of the accepted customers. From the t-tests, P value of < 0.0001 in both the cases,

which is less than 5% level of significance, it is apparent that the average difference of Balance and

ODFee is significant between the accepted and rejected customers for the credit card applications.

Looking at the credit card acceptance decision on customer defection

For checking whether the credit card acceptance has any effect on the customer defection, Cross-Tab statistic on Declined and Defected variables is used.


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Cross-tab: From the above contingency table it is evident that out of 441 customers who left the bank, 413 (93.6%)

customer’s credit card application has been rejected. But out of 4556

declined customers only 413 (9.06) customers are defected. Hence to test

the statistical significance for the relation between these two variables chi-

square test has been applied and the ‘p’ value of <0.0001 which is less than

5% level of significance shows that the association between the variables is

significant. Strength of association for these variables is verified from the

Kendall’s tau-b measure of association, the value of which is 0.08 (<0.7),

hence there exists a weak association between the variables Declined and

Defected. The same results can be obtained from the contingency

coefficient values.

Predicting the customer retention

From the above results and understandings, the bank’s ability to retain the present customers should be

tested. By applying the Logical Regression on the defection variable customer retention can be predicted

and from the results the bank should analyze its current business strategies to retain the customers if

the prediction of defection is high.

From the results of Stepwise logistical regression it is evident that the entire model is significant and

from the parameter estimated it is apparent that all the variables are having significant effect on the

customer defection. Other findings from the LR model are as follows:


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From the generalized R-square the variance explained is 8.9 % which is very less and from this we can

state the model is not performing well.

The Misclassification rate is 0.0791 and the Overall % correctly predicted ( 1-0.0791) is 92.09 %.

Managerial significance:

As far as the managerial significance of the model is concerned the overall % correctly predicted is quite

high, sensitivity is 70.06%, and specificity is 59.72%.

From the sensitivity value, around 70% of the time the model is able to predict the customers defecting

correctly, which is decent. And from the odds ratio, it is evident that the variables, Revenue, XSell have a

negative effect on customer defection i.e the customers with high revenue and Xsell are most likely to

stay and the variables Declined, FeeRev and ODFeeRev have an positive effect on the defection i.e the

customers with high ODFeeRev and FeeRev are most likely to defect. Hence for retaining the customers

the bank has to either reduce the Fee for the customers having more number of accounts or having high

balances.

Predicting the profitable customer retenti on

Similar to the prediction for the entire customer list, the overall model is insignificant and from the

generalized R-square only 10.3% of the variance has been explained.

The Misclassification rate is 0.0747 and the Overall % correctly predicted ( 1-0.0747) is 92.53 %.

Managerial significance:

As far as the managerial significance of the model is concerned the overall % correctly predicted is quite

high, sensitivity is 68.9 %, and specificity is 63.6 %.

From the sensitivity value, around 69% of the time the model is able to predict the profitable customers

defecting correctly, which is good. And from the odds ratio, it is evident that the variables, Revenue,

XSell have a negative effect on profitable customer defection i.e the customers with high revenue and

Xsell are most likely to stay and the variables Declined, FeeRev and ODFeeRev have an positive effect on


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the defection i.e the customers with high ODFeeRev and FeeRev are most likely to defect. Hence for

retaining the profitable customers the bank has to either reduce the Fee for the customers having more

number of accounts or having high balances. Hence it is observed that the prediction for profitable

customer defection is almost similar to the overall customer defection prediction and the manager has

to take the similar kind of actions to retain the customers.

What should the Big East bank do to retain the customers?

Though most of the customers who were rejected a credit card did not defect from the bank, we can observe that a 92% of defected customers were rejected a card. Which can be one of the essential contributing factors for the defection. Also the rejected customers are the most profitable customers and a chunk those profits come from Over Draft fee, Since the bank is already taking a risk in allowing those customers to Over Draft it should also consider being a little flexible in its acceptance process. We can also note that the rejected customers have been the more profitable customers. They are also likely to provide more profits to the bank through APR and late payment fee. Hence relaxing the acceptance policy is not only likely to improve customer relationship but also improve profits. The bank should make the information like customer profitability available to the credit card marketing department thereby improving their approval process. The bank should be more transparent about its credit card approval process thereby improving the customer relations. As the number of accounts a customer has with the bank is linearly related to the probability of how likely a customer is to stay with the bank. BigEast should consider running promotions to encourage existing customers to open new accounts.

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