NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship ๐Ÿ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lesson
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular ๐Ÿ”ฅ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New ๐ŸŽ‰ Generative AI for Finance New ๐ŸŽ‰ Generative AI for Marketing New ๐ŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular ๐Ÿ”ฅ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular ๐Ÿ”ฅ Data Science R: Machine Learning Designing and Implementing Production MLOps New ๐ŸŽ‰ Natural Language Processing for Production (NLP) New ๐ŸŽ‰
Find Inspiration
Get Course Recommendation Must Try ๐Ÿ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lessons
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > R Shiny > LendingClub Analysis: Insights from Issued Loans

LendingClub Analysis: Insights from Issued Loans

Tyler Kotnour
Posted on Feb 17, 2020
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

LendingClub Shiny App

Introduction

This dashboard was created by using a random sample of 500,000 records from the LendingClub data on issued loans. The original data set contained more than  2 million records. In order to create the Shiny app, the data needed to be sampled to meet the size requirements. Throughout this post, I will refer to the data as the sample set. The data consisted of loans issued from 2007 to 2018. Again, the data consisted of only loans that were issued to customers and not loans that were declined.  

Briefly, I will discuss total funding by State. Next, I will discuss why people utilize LendingClubโ€™s loan services. After that, the grades of issued loans will be examined. More specifically, the types of loan grades by purpose and the frequency of loan grades over time will be discussed. To conclude, funded loans and payment totals will be explored. This is meant to serve as an analysis of why people utilize LendingClubโ€™s loan service as well as an initial look at the outcomes of loans.

 

What is LendingClub?

LendingClub is a leading peer-to-peer lending service. The LendingClub (2020) company was founded in 2007. The company helps individuals acquire loans from other investors. LendingClub screens individuals for loan approval. Following screening, investors can analyze various metrics suited to their investment needs and risk tolerance. Once an individual is approved and investors choose to fund a loan, LendingClub aids the transaction process and services the loans. Investors and LendingClub earn money from the fees or interest. Meanwhile, borrowers can use the loans as they need.

 

Applicants & Loan Purpose

Loans from LendingClub were issued in nearly every State except for Iowa. California had the most frequent number of loans, followed by Texas and New York. Total funded amounts by state reflect these frequencies. California had the highest total amount of loans funded and the highest total payments received.

LendingClub acquires information from borrowers, such as to why they are utilizing their services. The most frequented purpose for a borrower to use LendingClub was for โ€œdebt consolidationโ€, โ€œcredit cardsโ€, or โ€œhome improvement.โ€ This trend was closely maintained over time as well. However, the โ€œotherโ€ category closely followed home improvement loans.

Applicant income varied greatly, however, the median income was $67,000. The Consumer Financial Protection Bureau (2019) noted that the debt-to-income ratio is a measure of an individualโ€™s monthly debts divided by their gross income. The debt-to-income ratio is one measurement lenders use to asses an individualโ€™s capacity to pay the loan in full. The average debt-to-income ratio was 18.54%. Additionally, most individuals either had a mortgage (245,366) or rented a home (198,493).

 

Issued Loan Grades

Issued loan grades were determined by LendingClub. Grades ranged from โ€œAโ€ to โ€œGโ€. โ€œBโ€ loans had the highest proportion of issued loans at 29.4%. โ€œCโ€ loans had the second highest at 28.7% and โ€œAโ€ loans were the third highest at 19.2%.  This trend was observed over time, however, starting in early 2017, โ€œAโ€ grade loans started increasing. โ€œAโ€ grade loans even surpassed all other issued loan grades October 2018.

Next, I examined the proportion of grades within each loan purpose. The highest proportion of โ€œAโ€ grade loans was observed in the โ€œcredit cardsโ€ (27.4%) purpose and for cars (25.4%). The highest proportion of โ€œBโ€ grade loans were observed in the same purposes. The proportion of โ€œBโ€ grade loans for โ€œcredit cardsโ€ was 34.1% and 30.8% for cars. The highest proportion of โ€œCโ€ grade loans were observed for โ€œeducationalโ€ (33.7%) or for โ€œmovingโ€ (32.3%) purposes.

 

Loan Statuses

Approximately, 46% of loans were โ€œfully paidโ€ at the observation point. Additionally, 40% of individuals were โ€œcurrentโ€ on their loan and 11.6% had their loans โ€œcharged off.โ€ Kagan (2019) noted that a debt being charged off does not wipe the debt from the consumer and the โ€œcharge offโ€ status can have serious implications for an individualโ€™s credit. Typically, a charge-off occurs after 180 days without a payment. The lender determines the funds to be uncollectible, however, the individual is still responsible for the loan until it is paid or settled. Defaults and charge-offs are important for investors because they may be at risk of losing a portion of their investment with these loan statuses.

Below, the status of loans with respect to the issued grade and loan purpose are presented. We can see that โ€œAโ€ grade loans, compared to โ€œBโ€ or โ€œCโ€ loans, were less likely to be charged off. โ€œBโ€ grade loans had a higher likelihood of default, however, โ€œBโ€ loans were the most likely to be fully paid. โ€œCโ€ grade loans had the highest likelihood of being late in both categories. 

โ€œDebt consolidationโ€ had the highest likelihood of occurring in each loan status. Thus, while debt consolidation loans were more likely to be fully paid, they were also more likely to be โ€œcharged offโ€ or in โ€œdefault.โ€ In the sample, only four purposes had defaults.

 

Loan Funding and Repayments

Overall, $7,524,284,350 in loans were requested and $7,521,835,375 were funded. At the time of observation $5,915,792,962 had been received in total payments (including interest and principal). Below is the amounts of loans requested per month compared to the total payments received per month.

It is important to provided context when discussing loan funding and repayments. As noted above, 46% of the sample had fully paid their loan, 40% of individuals were โ€œcurrentโ€ on their loan, and approximately 12% had been โ€œcharged off.โ€ To surpass the negative balance, there needs to be an approximate change in 21.35% of the total loans paid. Loan terms for LendingClub could be 36 months or 60 months. Due to the term lengths of loans and the observation point, 60 month loans from 2015 may not have been fully accounted for. Similarly, 36 month loans from 2017 may not have been fully accounted for. That being said, I estimated the potential expected profit for investors and LendingClub.

To calculate the estimated potential profit, I had to estimate the number of months into the term an individual was at. I did this by subtracting the last payment date from the date the loan was issued. I rounded to the nearest whole month. Then the months remaining could be derived by subtracting the number of payments from the term length. Utilizing the number of payments left in a loan multiplied by the monthly installment gave the estimated balance remaining. Finally, to estimate the total potential profit, I added the already paid amounts to the estimated remaining balance and subtracted the funded amount. The result was $3,907,621,984. This assumes that all remaining balances were paid without charge off.

However, this calculation could be more conservative. The percentage of loans that were not โ€œfully paidโ€ or โ€œcurrentโ€ was about 13%. Again, to be conservative I rounded to 14%. I subtracted 14% from the estimated amount remaining and recalculated the estimated profit to be $3,135,708,931. That works out to be a 41% profit given these conditions. Furthermore, a 20% decrease in the remaining total would result in a 37% profit.

Conclusion

Overwhelmingly, the most frequent purpose for individuals to use LendingClub was for โ€œdebt consolidation.โ€ The most common issued grades issued were โ€œA,โ€ โ€œB,โ€ or โ€œCโ€ grade loan. While the proportion of grades within each loan purpose varied โ€œBโ€ or โ€œCโ€ loans held the highest proportion of grades across most loan purposes. With respect to loan statuses, most loans were either paid off or โ€œcurrent.โ€ The โ€œdebt consolidationโ€ purpose held the highest proportion of purposes across all loan statuses, including โ€œcharged off,โ€ โ€œdefault,โ€ โ€œcurrent,โ€ and โ€œfully paid.โ€ Finally, I discussed the funding amounts and total payments and estimated various potential profits.

While the LendingClub was founded slightly before an economic recession, it has positioned itself to provide loans to millions of individuals across the country. With loans funded exceeding billions of dollars, consumers have a unique way to acquire personal loans while investors have found a unique way to diversify their portfolio.  

 

 

References

Consumer Financial Protection Bureau. (2020). What is a debt-to-income ratio? Why is the 43% debt-to-income ratio important? Retrieved from https://www.consumerfinance.gov/ask-cfpb/what-is-a-debt-to-income-ratio-why-is-the-43-debt-to-income-ratio-important-en-1791/

Kagan, J. (2019). Charge-off. Retrieved from https://www.investopedia.com/terms/c/chargeoff.asp

LendingClub. (2020). How it works. Retrieved from https://www.lendingclub.com/public/how-peer-lending-works.action 

Kaggle. (2020). Lending Club loan data. Retrieved from https://www.kaggle.com/wendykan/lending-club-loan-data

 

 

 

About Author

Tyler Kotnour

Tyler Kotnour graduated with his Master of Public Administration degree in 2018. Upon graduation, Mr. Kotnour worked in consulting conducting research and program evaluation. His primary role involved analyzing public health data for governments and non-profits. A major...
View all posts by Tyler Kotnour >

Leave a Comment

No comments found.

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day ChatGPT citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay football gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income industry Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    ยฉ 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application