Decision tree
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Decision tree

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A bank maintains a database of historic information on customers who have taken out ...

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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Decision tree Decision tree Presentation Transcript

  • 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)
  • 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
  • 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
  • • 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
  • 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
  • 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%
  • • 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