credit scoring using rattle and r
DESCRIPTION
A simple classification problem based on credit score data which allows us to identify whether a particular loan applicant may be given or denied credit (loan). Using Rattle and R (for some boxplot snippets), we've tried to bring out some interesting insightsTRANSCRIPT
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Credit scoring for loans with Rattle and R
Created and presented by:
Madhumita GhoshAyan Das
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Agenda
• What is a credit score?• The Data• Distributions• Correlation of variables• Creation of Models• Evaluation of Models• Facts and Fictions of Credit Scores
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What is a credit score?
• A number• Connection between the lender and
the borrower• Predicts how likely the borrower is,
to pay off a loan• Higher the score, more the chances
of receiving beneficial terms for loans and credits
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The Data
• 1000 observations
• 20 independent variables
• 1 dependent variable ‘credit’
• 0 – bad credit, 1- good credit
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DistributionsBoxplot – credit duration
• For all credits – median credit duration is 18 months (approx.)
• For bad credits – median duration of credit is about 21 months
• For good credits – median duration of credit is less than that for the bad ones
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Distributions
Boxplot – Age
Younger applicants are more likely to default• Impulsive nature• Not yet settled in life• Less financial
responsibilities towards family
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Distributions
Boxplot – Credit amount
High variance in credit amount for bad credit• Might not be
significant• Very small margin
towards borrowing of the loan amount
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Distributions
Histogram - Age• About 30 years of
age is when the default rate tapers off Ideal age for
being settled in life
• Higher the age (till an extent), lesser the chances of default More responsible Steady job
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Distributions
Histogram – Credit amount• The likely amount
of credit for a low default rate is about 2200 DM Borrower has a
very small margin towards borrowing the loan amount
Too high an amount – may not be able to repay back
Too low an amount – loan may not be necessary
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Correlation of variables
• Fairly high positive correlation between credit amount and credit duration More time
required to pay off large credit amounts
• Negative correlation between installment rate % and credit amount Offer more
discounted rates and easier pay off schemes for larger amounts borrowed
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Creation of Models
• Check “All” models• Custom “Split” and “Bucket” size• “Draw” models for visualization• Execute
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Evaluation of Models
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Evaluation of Models• Validation data
• Most of the models give identical results
• Decision Tree not a good option for Credit Scoring data
• Ignore Neural network evaluation
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Facts & Fictions of credit scores
• Fiction – More money you make, better your credit score will fare
Fact: Income has nothing to do with credit score. It’s not even reported to the credit bureau.
• Fiction – Credit bureaus never make mistakes
Fact: Nearly 8 in 10 credit reports contain a serious error or some sort of mistake, according to a survey by the US Public Interest Research Group.
• Fiction – Practicing a cash-only policy will help your credit score
Fact: Having good credit is a function of having credit available to you and using it responsibly.
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