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.
Given the information like mortgage details, borrowers related details and payment details, our objective is to identify the delinquency status of loans for the next month given the delinquency status for the previous 12 months (in number of months)
train.zip train.zip contains train.csv. train.csv contains the training data with details on loan as described in the last section
- loan_id Unique loan ID
- source Loan origination channel
- financial_institution Name of the bank
- interest_rate Loan interest rate
- unpaid_principal_bal Loan unpaid principal balance
- loan_term Loan term (in days)
- origination_date Loan origination date (YYYY-MM-DD)
- first_payment_date First instalment payment date
- loan_to_value Loan to value ratio
- number_of_borrowers Number of borrowers
- debt_to_income_ratio Debt-to-income ratio
- borrower_credit_score Borrower credit score
- loan_purpose Loan purpose
- insurance_percent Loan Amount percent covered by insurance
- co-borrower_credit_score Co-borrower credit score
- insurance_type 0 - Premium paid by borrower, 1 - Premium paid by Lender
- m1 to m12 Month-wise loan performance (deliquency in months)
- m13 target, loan deliquency status (0 = non deliquent, 1 = deliquent)
test.zip contains test.csv which has details of all loans for which the participants are to submit the delinquency status - 0/1 (not probability)
sample_submission.zip contains the submission format for the predictions against the test set. A single csv needs to be submitted as a solution.
Submissions are evaluated on F1-Score between the predicted class and the observed target.