Analytics_Vidhya_India_ML_Hiring_Hackathon2019

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.

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)

Data Description

train.zip train.zip contains train.csv. train.csv contains the training data with details on loan as described in the last section

Data Dictionary

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

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

sample_submission.zip contains the submission format for the predictions against the test set. A single csv needs to be submitted as a solution.

Evaluation Metric

Submissions are evaluated on F1-Score between the predicted class and the observed target.

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