Credit Risk Modeling in Python
Michael Crabtree
Data Scientist, Ford Motor Company
Payment | Payment Date | Loan Status |
---|---|---|
$100 | Jun 15 | Non-Default |
$100 | Jul 15 | Non-Default |
$0 | Aug 15 | Default |
Formula for expected loss:
expected_loss = PD * EAD * LGD
Two Primary types of data used:
Application | Behavioral |
---|---|
Interest Rate | Employment Length |
Grade | Historical Default |
Amount | Income |
Column | Column |
---|---|
Income | Loan grade |
Age | Loan amount |
Home ownership | Interest rate |
Employment length | Loan status |
Loan intent | Historical default |
Percent Income | Credit history length |
pd.crosstab(cr_loan['person_home_ownership'], cr_loan['loan_status'],
values=cr_loan['loan_int_rate'], aggfunc='mean').round(2)
plt.scatter(cr_loan['person_income'], cr_loan['loan_int_rate'],c='blue', alpha=0.5)
plt.xlabel("Personal Income")
plt.ylabel("Loan Interest Rate")
plt.show()
Credit Risk Modeling in Python