This document analyzes customer data from a bank to predict loan default risk. It contains information on 2464 customers, including credit rating, age, income, credit cards, education, and previous loans. The analysis finds that income level is the best predictor of default, with low-income groups more likely to default. Number of credit cards and age are also important predictors. The analysis achieves an overall correct classification rate of 77.07% for predicting good and bad credit risks. It recommends the bank focus on higher- and middle-income customers, especially those with fewer than 5 credit cards, and be cautious of middle-income customers over 28 with more than 5 cards.

1. SOMDEEP SEN; Business Analyst: Trimax Analytics
(e) somdeepenggmba@gmail.com; (p): 09748229123
LinkedIn: http://linkd.in/1ifqs3x

2. •
Bank maintains database of historic information on customers who have taken loans
•
This includes those, who have repaid as well the ones who defaulted
•
Total Number of observations: 2464
Variable
Type
Credit Rating(Dependent )
Categorical
Age
Continuous
Income
Categorical
Number of Credit Cards
Categorical
Education
Categorical
Loans Taken
Categorical
Data Source: http://bit.ly/1ewAlYR

3. • Analysis of the characteristics of the two groups of customers
• To predict the likelihood that loan applicants will default on payments
• Reduction Of Non Performing Assets (NPA)

4. Age
Income
Number of
Credit Cards
Education
Loans Taken
Age , Income & Number of Credit Cards
Note: Independent variables have been chosen by the package based on statistical significance

7. Customer Segment Break-up(%)
22.44
46.02
31.54
Middle
High
Low
•
Income level has also emerged as the best predictor
•
MIG is the biggest contributor to the customer segment followed by HIG

8. •
The next best predictor after income is number of credit cards
– 56% having >=5 credit cards have defaulted
– 86% having <5 credit cards have not defaulted
•
5 or more credit cards group the includes one more predictor: age
– Over 80% of customers less than equal to 28 years having have a bad credit rating
– Slightly less than half of those over 28 have a bad credit rating

9.  The next best predictor after age is number of credit cards
 88% have not defaulted
 Income level is the only significant predictor
 82% have defaulted

10. Bad
Good
Total
Categories
Predicted
Category
Percent
Percent
LIG
82%
18%
100%
Bad
MIG
42%
58%
100%
Bad
HIG
12%
88%
100%
Good
MIG with 5 or more credit cards
57%
43%
100%
Bad
MIG with less than 5 credit cards
14%
86%
100%
Good
HIG with 5 or more credit cards
18%
82%
100%
Good
HIG with less than 5 credit cards
3%
97%
100%
Good
80%
20%
100%
Bad
43%
57%
100%
Bad
41%
59%
100%
Good
MIG with 5 or more credit cards & 28 years or
more
MIG with less than 5 credit cards & more than28
years
Overall rating

11. Classification
Predicted
Observed
Bad
Good
Percent Correct
Bad
876
144
85.90%
Good
421
1023
70.84%
Overall Percentage
52.64%
47.36%
77.07%
 Almost 86% of the bad credit risks are now correctly classified
 Almost 71% of the good credit scores are now correctly classified
 Overall correct classification : 77.1%

12. •
While providing loans, the bank should look to focus on the HIG & MIG
•
Among the MIG the focus should be on the customer having <5 credit cards
•
Customer belonging to MIG, having >5 credit cards & >=28 years seem to be highly risky
The bank should also be careful in providing credit cards to customers having four
credit cards belonging to MIG as it may hamper other product lines like loans