Lending Club Case Study

Table of Contents

• Problem Statement
• Objectives
• Dataset Analysis
• Approach
• Case Study Analysis
• Conclusions
• Technologies Used
• Acknowledgements
• Glossary
• Team

Problem Statement

Lending Club is a consumer finance marketplace for personal loans that matches borrowers who are seeking a loan with investors looking to lend money and make a return.

It specialises in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant's profile.

Like most other lending companies, lending loans to ‘risky’ applicants is the largest source of financial loss (called credit loss). The credit loss is the amount of money lost by the lender when the borrower refuses to pay or runs away with the money owed.

In other words, borrowers who default cause the largest amount of loss to the lenders. In this case, the customers labelled as 'charged-off' are the 'defaulters'.

The core objective of the excercise is to help the company minimise the credit loss. There are two potential sources of credit loss are:

• Applicant likely to repay the loan, such an applicant will bring in profit to the company with interest rates.** Rejecting such applicants will result in loss of business**.
• Applicant not likely to repay the loan, i.e. and will potentially default, then approving the loan may lead to a financial loss* for the company

Objectives

The goal is to identify these risky loan applicants, then such loans can be reduced thereby cutting down the amount of credit loss. Identification of such applicants using EDA using the given dataset, is the aim of this case study.

If one is able to identify these risky loan applicants, then such loans can be reduced thereby cutting down the amount of credit loss. Identification of such applicants using EDA is the aim of this case study.

In other words, the company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment.

DataSet Analysis

The data given below contains the information about past loan applicants and whether they ‘defaulted’ or not. The aim is to identify patterns which indicate if a person is likely to default, which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc.

• The dataset reflects loans post approval, thus does not represent any information on the rejection criteria process
  • Overall objective will be to observe key leading indicaters (driver variables) in the dataset, which contribute to defaulters
  • Use the analysis as a the foundation of the hypothesis
• The overall loan process is represented by three steps
  • Potential borrower requests for loan amount (loan_amnt)
  • The approver approves/rejects an amount based on past history/risk (funded_amnt)
  • The final amount offered as loan by the investor (funded_amnt_inv)

Analysis based on Domain Understanding

Leading Attribute (loan_status)

• Loan Status - Key Leading Attribute (loan_status). The column has three distinct values
  • Fully-Paid - The customer has successfuly paid the loan
  • Charged-Off - The customer is "Charged-Off" ir has "Defaulted"
  • Current - These customers, the loan is currently in progress and cannot contribute to conclusive evidence if the customer will default of pay in future
    • For the given case study, "Current" status rows will be ignored

Decision Matrix (loan_status)

• Loan Accepted - Three Scenarios
  • Fully Paid - Applicant has fully paid the loan (the principal and the interest rate)
  • Current - Applicant is in the process of paying the instalments, i.e. the tenure of the loan is not yet completed. These candidates are not labelled as 'defaulted'.
  • Charged-off - Applicant has not paid the instalments in due time for a long period of time, i.e. he/she has defaulted on the loan
• Loan Rejected - The company had rejected the loan (because the candidate does not meet their requirements etc.). Since the loan was rejected, there is no transactional history of those applicants with the company and so this data is not available with the company (and thus in this dataset)

Important Columns

The given columns are leading attributes, or predictors. These attributes are available at the time of the loan application and strongly helps in prediction of loan pass or rejection. Key attributes Some of these columns may get dropped due to empty data in the dataset

• Customer Demographics
  • Annual Income (annual_inc) - Annual income of the customer. Generally higher the income, more chances of loan pass
  • Home Ownership (home_ownership) - Wether the customer owns a home or stays rented. Owning a home adds a collateral which increases the chances of loan pass.
  • Employment Length (emp_length) - Employment tenure of a customer (this is overall tenure). Higher the tenure, more financial stablity, thus higher chances of loan pass
  • Debt to Income (dti) - The percentage of the salary which goes towards paying loan. Lower DTI, higher the chances of a loan pass.
  • State (addr_state) - Location of the customer. Can be used to create a generic demographic analysis. There could be higher delinquency or defaulters demographicaly.
• Loan Attributes
  • Loan Ammount (loan_amt)
  • Grade (grade)
  • Term (term)
  • Loan Date (issue_date)
  • Purpose of Loan (purpose)
  • Verification Status (verification_status)
  • Interest Rate (int_rate)
  • Installment (installment)
  • Public Records (public_rec) - Derogatory Public Records. The value adds to the risk to the loan. Higher the value, lower the success rate.
  • Public Records Bankruptcy (public_rec_bankruptcy) - Number of bankruptcy records publocally available for the customer. Higher the value, lower is the success rate.

Ignored Columns

• The following types of columns will be ignored in the analysis. This is a generic categorization of the columns which will be ignored in our approach and not the full list.
  • Customer Behaviour Columns - Columns which describes customer behaviour will not contribute to the analysis. The current analysis is at the time of loan application but the customer behaviour variables generate post the approval of loan applications. Thus these attributes wil not be considered towards the loan approval/rejection process.
  • Granular Data - Columns which describe next level of details which may not be required for the analysis. For example grade may be relevant for creating business outcomes and visualizations, sub grade is be very granular and will not be used in the analysis

Data Set Analysis based on understanding of EDA

Rows Analysis

• Summary Rows: No summary rows were there in the dataset
• Header & Footer Rows - No header or footer rows in the dataset
• Extra Rows - No column number, indicators etc. found in the dataset
• Rows where the loan_status = CURRENT will be dropped as CURRENT loans are in progress and will not contribute in the decision making of pass or fail of the loan. The rows are dropped before the column analysis as it also cleans up unecessary column related to CURRENT early and columns with NA values can be cleaned in one go
• Find duplicate rows in the dataset and drop if there are

Columns Analysis of the Dataset

Drop Columns

• There are multiple columns with NA values only. The columns will be dropped.
  • This is evaluated after dropping rows with loan_status = Current
  • (next_pymnt_d, mths_since_last_major_derog, annual_inc_joint, dti_joint, verification_status_joint, tot_coll_amt, tot_cur_bal, open_acc_6m, open_il_6m, open_il_12m, open_il_24m, mths_since_rcnt_il, total_bal_il, il_util, open_rv_12m, open_rv_24m, max_bal_bc, all_util, total_rev_hi_lim, inq_fi, total_cu_tl, inq_last_12m, acc_open_past_24mths, avg_cur_bal, bc_open_to_buy, bc_util, mo_sin_old_il_acct, mo_sin_old_rev_tl_op, mo_sin_rcnt_rev_tl_op, mo_sin_rcnt_tl, mort_acc, mths_since_recent_bc, mths_since_recent_bc_dlq, mths_since_recent_inq, mths_since_recent_revol_delinq, num_accts_ever_120_pd, num_actv_bc_tl, num_actv_rev_tl, num_bc_sats, num_bc_tl, num_il_tl, num_op_rev_tl, num_rev_accts, num_rev_tl_bal_gt_0, num_sats, num_tl_120dpd_2m, num_tl_30dpd, num_tl_90g_dpd_24m, num_tl_op_past_12m, pct_tl_nvr_dlq, percent_bc_gt_75, tot_hi_cred_lim, total_bal_ex_mort, total_bc_limit, total_il_high_credit_limit)
• There are multiple columns where the values are only zero, the columns will be dropped
• There are columns where the values are constant. They dont contribute to the analysis, columns will be dropped
• There are columns where the value is constant but the other values are NA. The column will be considered as constant. columns will be dropped
• There are columns where more than 65% of data is empty (mths_since_last_delinq, mths_since_last_record) - columns will be dropped
• Drop columns (id, member_id) as they are index variables and have unique values and dont contribute to the analysis
• Drop columns (emp_title, desc, title) as they are discriptive and text (nouns) and dont contribute to analysis
• Drop redundant columns (url). On closer analysis url is a static path with the loan id appended as query. It's a redundant column to (id) column
• Drop customer behaviour columns which represent data post the approval of loan
  • They contribute to the behaviour of the customer. Behaviour of the customer is recorded post approval of loan and not available at the time of loan approval. Thus these variables will not be considered in analysis and thus dropped
  • (delinq_2yrs, earliest_cr_line, inq_last_6mths, open_acc, pub_rec, revol_bal, revol_util, total_acc, out_prncp, out_prncp_inv, total_pymnt, total_pymnt_inv, total_rec_prncp, total_rec_int, total_rec_late_fee, recoveries, collection_recovery_fee, last_pymnt_d, last_pymnt_amnt, last_credit_pull_d, application_type)

Convert Column Format

• (loan_amnt, funded_amnt, funded_amnt_inv) columns are Object and will be converted to float
• (int_rate, installment, dti) columns are Object and will be converted to float
• strip "month" text from term column and convert to integer
• Percentage columns (int_rate) are object. Strip "%" characters and convert column to float
• issue_d column converted to datetime format

Standardise Values

• All currency columns are rounded off to 2 decimal places as currency are limited to cents/paise etc only.

Convert Column Values

• loan_status column converted to boolean Charged Off = False and Fully Paid = True. This converts the column into ordinal values
• emp_length converted to integer with following logic. Note < 1 year is converted to zero and 10+ converted to 10.
  • < 1 year: 0,
  • 2 years: 2,
  • 3 years: 3,
  • 7 years: 7,
  • 4 years: 4,
  • 5 years: 5,
  • 1 year: 1,
  • 6 years: 6,
  • 8 years: 8,
  • 9 years: 9,
  • 10+ years: 10

Added new columns

• verification_status_n added. Considering domain knowledge of lending = Verified > Source Verified > Not Verified. verification_status_n correspond to {Verified: 3, Source Verified: 2. Not Verified: 1} for better analysis
• issue_y is year extracted from issue_d
• issue_m is month extracted from issue_d

Ignored Rows and Columns because of missing data

• Columns with high percentage of missing values will be dropped (65% above for this case study)
• Columns with less percentage of missing value will be imputed
• Rows with high percentage of missing values will be removed (65% above for this case study)

Approach

• Step 0 - Data Cleaning & Manipulation Checklist
• Step 1 - Dropping Rows - where loan_status = "Current"
• Step 2 - Dropping Columns based on EDA and Domain Knowledge
• Step 3 - Convert the data types
• Step 4 - Identify columns with blank values which need to be imputed
• Step 5 - Analysis of the dataset post cleanup
• Step 6 - Outlier Treatment
• Step 7 - Analysis - Univariate, Bivariate and Derived Metrics Analysis
• Step 8 - Conclusions Inferences and Recommendations

Case Study Analysis

Conclusions

Technologies Used

• Python - Version 3.9.12
• numpy - Version 1.21.5
• pandas - Version 1.4.2
• matplotlib - Version 3.5.1
• seaborn - Version 0.11.2
• Jupyter Notebook - Version 3.3.2
• JupyterLab - Version 6.4.11
• Anaconda - Version 2.1.4

Acknowledgements and References

• The project references insights and inferences from live presentation given by Aditya Bhattacharya
• The project reference course materieals from upGrads curriculm
• What is Exploratory Data Analysis? by Prasad Patil
• https://stackoverflow.com/questions/56611698/pandas-how-to-read-csv-file-from-google-drive-public

Glossary

• EDA - Exploratory Data Analytics

Team

• Manoj Kumar Shukla
• Siddhartha Lahiri