Analytics Vidhya - India ML Hiring Hackathon 2019

Problem Statement: 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 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)

Response: loan deliquency status (0 = non deliquent, 1 = deliquent)

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

Language: Python

IDE: Spyder

F1 score: 0.32