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A bank maintains a database of historic information on customers who have taken out loans from the bank, including whether or not they repaid the loans or defaulted. Using a tree model, you can analyze the characteristics of the two groups of customers and build models to predict the likelihood that loan applicants will default on their loans.
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SOMDEEP SEN; Business Analyst: Trimax Analytics
(e) [email protected]; (p): 09748229123
LinkedIn: http://linkd.in/1ifqs3x
• 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
• 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)
Note: Independent variables have been chosen by the package based on statistical significance
Age , Income & Number of Credit Cards
AgeIncomeNumber of Credit Cards EducationLoans Taken
• Income level has also emerged as the best predictor
• MIG is the biggest contributor to the customer segment followed by HIG
46.02
31.54
22.44
Customer Segment Break-up(%)
Middle High Low
• 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
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
CategoriesBad Good
TotalPredicted 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
MIG with 5 or more credit cards & 28 years or more
80% 20% 100% Bad
MIG with less than 5 credit cards & more than28 years
43% 57% 100% Bad
Overall rating 41% 59% 100% Good
Classification
ObservedPredicted
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%
• 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