Loan Delinquency Prediction:
- Loan default prediction is one of the most critical and crucial problem faced by financial institutions and organizations as it has a noteworthy effect on the profitability of these institutions. In recent years, there is a tremendous increase in the volume of non – performing loans which results in a jeopardizing effect on the growth of these institutions.
- Therefore, to maintain a healthy portfolio, the banks put stringent monitoring and evaluation measures in place to ensure timely repayment of loans by borrowers. Despite these measures, a major proportion of loans become delinquent. Delinquency occurs when a borrower misses a payment against his/her loan.
- Objective is to identify the delinquency status of loans for the next month given the delinquency status for the previous 12 months
train.csv:
train.csv contains train.csv. train.csv contains the training data with details on loan as described in the last section.
Variable- Descriptionloan_id- Unique loan IDsource- Loan origination channelfinancial_institution- Name of the bankinterest_rate- Loan interest rateunpaid_principal_bal- Loan unpaid principal balanceloan_term-Loan term (in days)origination_date-Loan origination date (YYYY-MM-DD)first_payment_date- First instalment payment dateloan_to_value- Loan to value rationumber_of_borrowers- Number of borrowersdebt_to_income_ratio- Debt-to-income ratioborrower_credit_score- Borrower credit scoreloan_purpose- Loan purposeinsurance_percent-Loan Amount percent covered by insuranceco-borrower_credit_score- Co-borrower credit scoreinsurance_type-0- Premium paid by borrower,1- Premium paid by Lenderm1 to m12- Month-wise loan performance (deliquency in months)m13target, loan deliquency status (0 = non deliquent, 1 = deliquent)
test.csv
test.csv contains test.csv which has details of all loans for which the participants are to submit the delinquency status - 0/1 (not probability)
Submissions are evaluated on F1-Score between the predicted class and the observed target.