LoanTap Data

Context:

LoanTap is an online platform committed to delivering customized loan products to millennials. They innovate in an otherwise dull loan segment, to deliver instant, flexible loans on consumer friendly terms to salaried professionals and businessmen.

The data science team at LoanTap is building an underwriting layer to determine the creditworthiness of MSMEs as well as individuals.

LoanTap deploys formal credit to salaried individuals and businesses 4 main financial instruments:

1. Personal Loan
2. EMI Free Loan
3. Personal Overdraft
4. Advance Salary Loan
   This case study will focus on the underwriting process behind Personal Loan only

Problem Statement:

Given a set of attributes for an Individual, determine if a credit line should be extended to them. If so, what should the repayment terms be in business recommendations?

Data dictionary:

• loan_amnt : The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.
• term : The number of payments on the loan. Values are in months and can be either 36 or 60.
• int_rate : Interest Rate on the loan
• installment : The monthly payment owed by the borrower if the loan originates.
• grade : LoanTap assigned loan grade
• sub_grade : LoanTap assigned loan subgrade
• emp_title :The job title supplied by the Borrower when applying for the loan.*
• emp_length : Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years.
• home_ownership : The home ownership status provided by the borrower during registration or obtained from the credit report.
• annual_inc : The self-reported annual income provided by the borrower during registration.
• verification_status : Indicates if income was verified by LoanTap, not verified, or if the income source was verified
• issue_d : The month which the loan was funded
• loan_status : Current status of the loan - Target Variable
• purpose : A category provided by the borrower for the loan request.
• title : The loan title provided by the borrower
• dti : A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LoanTap loan, divided by the borrower’s self-reported monthly income.
• earliest_cr_line :The month the borrower's earliest reported credit line was opened
• open_acc : The number of open credit lines in the borrower's credit file.
• pub_rec : Number of derogatory public records
• revol_bal : Total credit revolving balance
• revol_util : Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.
• total_acc : The total number of credit lines currently in the borrower's credit file
• initial_list_status : The initial listing status of the loan. Possible values are – W, F
• application_type : Indicates whether the loan is an individual application or a joint application with two co-borrowers
• mort_acc : Number of mortgage accounts.
• pub_rec_bankruptcies : Number of public record bankruptcies
• Address: Address of the individual

Try to use concepts like:

• Exploratory Data Analysis
• Feature Engineering
• Logistic Regression
• Precision Vs Recall Tradeoff

Questionnaire (Answers should present in the text editor along with insights):

• What percentage of customers have fully paid their Loan Amount?
• Comment about the correlation between Loan Amount and Installment features.
• The majority of people have home ownership as _______.
• People with grades ‘A’ are more likely to fully pay their loan. (T/F)
• Name the top 2 afforded job titles.
• Thinking from a bank's perspective, which metric should our primary focus be on..
• ROC AUC
• Precision
• Recall
• F1 Score
• How does the gap in precision and recall affect the bank?
• Which were the features that heavily affected the outcome?
• Will the results be affected by geographical location? (Yes/No)

Evaluation Criteria (100 points)

• Define Problem Statement and perform Exploratory Data Analysis (10 points)
• Definition of problem (as per given problem statement with additional views)
• Observations on shape of data, data types of all the attributes, conversion of categorical attributes to 'category' (If required), missing value detection, statistical summary.
• Univariate Analysis (distribution plots of all the continuous variable(s) barplots/countplots of all the categorical variables)
• Bivariate Analysis (Relationships between important variable)
  Illustrate the insights based on EDA
• Comments on range of attributes, outliers of various attributes
• Comments on the distribution of the variables and relationship between them
• Comments for each univariate and bivariate plots
• Data Preprocessing (20 Points)
• Duplicate value check
• Missing value treatment
• Outlier treatment
• Feature engineering
• Data preparation for modeling
• Model building (10 Points)
• Build the Logistic Regression model and comment on the model statistics
• Display model coefficients with column names
• Results Evaluation (50 Points)
• ROC AUC Curve & comments (10 Points)
• Precision Recall Curve & comments (10 Points)
• Classification Report (Confusion Matrix etc) (10 Points)

Tradeoff Questions:

• How can we make sure that our model can detect real defaulters and there are less false positives? This is important as we can lose out on an opportunity to finance more individuals and earn interest on it. (10 Points)
• Since NPA (non-performing asset) is a real problem in this industry, it’s important we play safe and shouldn’t disburse loans to anyone. (10 Points)
• Actionable Insights & Recommendations (10 Points)