SOMDEEP SEN; Business Analyst: Trimax Analytics

(e) somdeepenggmba@gmail.com; (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