Project 1: Analysis of Lending Club's data
Contributed by Jean-Francois Darre. Jean took NYC Data Science Academy 12 week full time Data Science Bootcamp program between Sept 23 to Dec 18, 2015. The post was based on his first class project(due at 2nd week of the program).
Check out the full report here! You will find all the details of the code behind the analysis and the visualisations.
Lending club:
For this project, we wish to present and explore the data provided by Lending Club. Lending Club (LC) is a peer-to-peer online lending platform. It is the world’s largest marketplace connecting borrowers and investors, where consumers and small business owners lower the cost of their credit and enjoy a better experience than traditional bank lending, and investors earn attractive risk-adjusted returns.
How it works:
- Customers interested in a loan complete a simple application at LendingClub.com
- LC leverage online data and technology to quickly assess risk, determine a credit rating and assign appropriate interest rates.
- Qualified applicants receive offers in just minutes and can evaluate loan options with no impact to their credit score
- Investors ranging from individuals to institutions select loans in which to invest and can earn monthly returns
The entire process is online, using technology to lower the cost of credit and pass the savings back in the form of lower rates for borrowers and solid returns for investors. Here is the link to more details about LC.
Goal of the project:
We will present and explore the data provided by LC. This data was made available to us after the creation of an investor account. The data consists in 4 files updated every quarter on the same day as the quarterly results of the company are released. They contain information on almost all the loans issued by LC. The only loans missing from these files are the few loans where LC was not authorized to release publicly the details of the transactions.
The information available for each loan consists of all the details of the loans at the time of their issuance as well as more information relative to the latest status of loan such as how much principal has been paid so far, how much interest, if the loan was fully paid or defaulted, or if the borrower is late on payments etc.
First look at the data, exploratory research:
Second look at the data: Exploration of the relationship between FICO and LC scores:
FICO scores have been implemented to assess the credit worthiness of potential borrowers. A model that Lending Club could have used could have been to simply rely on the foundation of FICO’s scores. The majority of the mortgage industry relies on FICO scores to issue mortgages of 100s of thousands.
Hence our interest in looking into LC’s grade system and more specifically if they have an obvious linear relationship with FICO scores. At first, it seems obvious that the LC grades and FICO scores are very correlated:
But the second graph already seems to imply otherwise. Indeed 20 of the 35 possible LC sub grades have average FICO scores within 10pts!
Running the full distribution of FICO ratings per LC grade confirm our previous point. Grades E, F, G and arguably D and even C have very close distributions and medians:
It is very interesting to see how these densities have evolved over the years. We can see how Lending Club’s earlier model used to rely on FICO score heavily and over the course of the years, LC refined their credit models using the data that they started accumulating. It is particularly obvious when looking at the grade A. Its density changed from being condensed over a range FICO scores of 730 to 820 to a density that covers the entire FICO range with a mean that is lower than the minimum score they used to require to get an A rating:
Third look at the data: Exploration and discovery of the predictive strength of each feature:
In this section we will take each features given at the time of the origination of the debt and attempt to extract their relation to default rates. For this part of the analysis we will use the data set LCmatured that, we recall, contains only the loans that have matured or if defaulted, would have matured.
Home ownership:
Home ownership: Does home ownership have any relationship with LC grades, FICO scores and Charge Off rates?
Purpose:
Purpose: We want to see if there is any relation between LC Grades, FICO scores, Charge Off rates and purpose. There is a strong correlation between charge off rates and if the purpose is either educational or small business. These loans tend to be a lot riskier. The fico score does not reflect this while the LC score seems to partially capture that risk:
Revolvoving balance and employement length:
Revolving Balance, along with Employment length are actually the features with the least obvious link to default rates:
## revol_bal_bucket charged net_EL avg_fico avg_grade
## (fctr) (dbl) (dbl) (dbl) (chr)
## 1 0-1,333 12.93 8.19 734 B4
## 2 1,333-3,170.2 11.93 7.50 723 B4
## 3 3,170.2-5,080 12.59 7.66 717 B4
## 4 5,080-6,974.4 12.94 7.77 712 B5
## 5 6,974.4-9,100 13.62 7.93 711 B5
## 6 9,100-11,592.2 12.27 7.48 710 B5
## 7 11,592.2-14,693.2 13.04 7.59 710 B5
## 8 14,693.2-19,306 12.38 7.53 712 B5
## 9 19,306-28,052.4 12.06 7.25 712 C1
## 10 28,052.4-+inf 13.09 8.26 717 C1
## emp_length charged net_EL avg_fico avg_grade
## (chr) (dbl) (dbl) (dbl) (chr)
## 1 n/a 19.11 11.86 723 B3
## 2 < 1 year 12.73 8.05 715 B5
## 3 1 year 12.50 7.84 713 B5
## 4 2 years 12.25 7.65 713 B5
## 5 3 years 12.14 7.27 714 B5
## 6 4 years 12.35 7.43 713 B5
## 7 5 years 11.91 7.03 716 B5
## 8 6 years 12.56 7.88 715 B5
## 9 7 years 13.54 8.20 714 B5
## 10 8 years 12.64 7.85 717 B5
## 11 9 years 11.68 6.86 718 B5
## 12 10+ years 12.74 7.49 719 B5Number of delinquencies over the past 2 years prior the application for a loan:
Number of delinquencies during the past 2 years: This feature is somewhat linked to charge off rates but a vast majority of the borrowers actually have 0 delinquencies, which is not helpful distinguishing between them:
## delinq_bucket charged net_EL avg_fico avg_grade
## (chr) (dbl) (dbl) (dbl) (chr)
## 1 0 12.45 7.56 718 B4
## 2 1 14.41 8.90 696 C3
## 3 2+ 15.85 9.64 686 C4Number of opened and total number of accounts:
Total number of accounts and Open accounts are not the most impactful features. Fully paying borrower tend to have slightly more accounts but too many accounts may be bad too. This is interesting because FICO considers that the more accounts. This is also confirmed in the numbers below:
## (fctr) (dbl) (dbl) (dbl) (chr)
## 1 0-9 15.85 9.99 710 C2
## 2 9-12 13.32 8.11 711 B5
## 3 12-15 13.50 8.17 713 B5
## 4 15-17 12.85 7.42 714 B5
## 5 17-20 11.55 6.89 715 B4
## 6 20-23 11.05 6.67 718 B4
## 7 23-26 11.92 7.32 718 B4
## 8 26-30 12.56 7.48 719 B4
## 9 30-37 11.12 6.75 720 B4
## 10 37-+inf 12.41 7.80 721 B5## open_acc_bucket charged net_EL avg_fico avg_grade
## (fctr) (dbl) (dbl) (dbl) (chr)
## 1 0-4 15.09 9.61 718 C2
## 2 4-6 12.10 7.43 718 B4
## 3 6-7 12.20 7.47 717 B4
## 4 7-8 12.91 7.71 716 B4
## 5 8-9 12.36 7.35 715 B4
## 6 9-10 12.78 7.67 715 B4
## 7 10-11 11.41 6.74 715 B5
## 8 11-13 12.52 7.63 714 B5
## 9 13-15 13.06 7.95 715 B5
## 10 15-+inf 12.53 7.54 713 C1DTI (Debt To Income ratio):
The DTI ratio: the lower the better, a DTI of 5 means your debts payment excluding mortgage, are only 5% of your gross income. DTI vs LC Grade, although having the expected trend, does not seem to show an strong dependency. DTI vs FICO score show a strong relationship for scores ranging from 700-850 but no correlation for scores < 700. But overall DTI has some impact on charge off probabilities.
## dti_bucket charged net_EL avg_fico avg_grade
## (fctr) (dbl) (dbl) (dbl) (chr)
## 1 0-4.32 11.07 6.90 736 B4
## 2 4.32-7.24 10.47 6.12 723 B4
## 3 7.24-9.56 11.52 6.83 720 B4
## 4 9.56-11.684 11.73 7.35 716 B5
## 5 11.684-13.69 12.52 7.79 714 B5
## 6 13.69-15.68 12.64 7.66 712 B5
## 7 15.68-17.8 13.72 8.27 710 B5
## 8 17.8-20.1 13.68 8.18 709 B5
## 9 20.1-22.67 14.70 8.94 707 C1
## 10 22.67-+inf 14.79 9.14 711 B5 
Public records:
Public Records: This feature is definitely correlated to charged off rates, but its value is very low. It is zero in most cases. Basically if a borrower has 0 public records is a strong indicator that he has greater chances to pay off his debts and conversely, if the number of public records is greater than 0, the borrower has a substantially greater chance of default.
## rec_bucket charged net_EL avg_fico avg_grade
## (chr) (dbl) (dbl) (dbl) (chr)
## 1 0 12.34 7.52 717 B5
## 2 1+ 19.85 11.71 695 C3Age of Credit history:
The age of credit history has a significant impact on default rates: Fully paying borrowers tend to have slightly older credit history, which is to be expected. But the main take on Credit history is on borrower that have relatively short credit history: We can see that the bottom 10% have credit histories of less than 6 years and have a significantly higher default rates. One should also be careful when looking into the age of the credit history since it correlates with the age of the borrower and hence with his income and other features. This will need to be investigated further later.
## credit_ym_bucket charged net_EL avg_fico avg_grade
## (fctr) (dbl) (dbl) (dbl) (chr)
## 1 0-5.9 16.42 10.41 700 C3
## 2 5.9-7.8 12.39 7.71 707 B5
## 3 7.8-9.7 12.79 7.59 710 B5
## 4 9.7-11 13.11 8.15 713 B5
## 5 11-12.2 12.85 7.77 715 B5
## 6 12.2-13.8 12.57 7.59 717 B5
## 7 13.8-15.7 12.71 7.52 719 B5
## 8 15.7-18.2 11.54 6.75 723 B4
## 9 18.2-22.7 11.34 7.14 725 B4
## 10 22.7-+inf 10.99 6.46 730 B3Revolving utilization:
As expected, higher revolving utilization mean higher risk of default. The top 10% has default rates that are almost twice as low as the bottom 10%.
## revol_bucket charged net_EL avg_fico avg_grade
## (fctr) (dbl) (dbl) (dbl) (chr)
## 1 0-9.8 9.44 5.97 757 B2
## 2 9.8-22.3 8.32 5.07 743 B1
## 3 22.3-33.2 10.32 6.07 730 B3
## 4 33.2-43.2 11.42 6.70 719 B4
## 5 43.2-52.5 11.93 7.36 713 B5
## 6 52.5-61.3 13.80 8.46 708 B5
## 7 61.3-69.5 13.95 8.24 704 B5
## 8 69.5-78.4 13.75 8.30 697 C2
## 9 78.4-87.9 15.75 9.57 694 C2
## 10 87.9-+inf 18.22 11.45 693 C5Annual income:
This is a very strong feature. People in the top 20% quantile have half as much charged off loans compared to the bottom 20%. It is worth noticing though, that even the top 20% still get a 9.5% chance off defaulting on their loan which is still very high. So annual income is not a silver bullet either.
## annual_inc_bucket charged net_EL avg_fico avg_grade
## (fctr) (dbl) (dbl) (dbl) (chr)
## 1 0-28,995.6 17.40 11.14 712 B5
## 2 28,995.6-36,000 16.70 10.00 712 B5
## 3 36,000-43,000 14.31 8.68 713 B5
## 4 43,000-50,000 13.00 7.88 713 B5
## 5 50,000-57,000 12.47 7.42 714 B5
## 6 57,000-65,000 12.37 7.47 716 B5
## 7 65,000-75,000 11.32 6.88 717 B5
## 8 75,000-89,100 10.33 6.25 719 B5
## 9 89,100-114,000 8.95 5.47 720 B5
## 10 114,000-+inf 9.44 5.64 723 C1Inquiries in the last 6months:
This feature is supposed to represent how desperate the borrower is for credit. We can see that the correlation between this feature and charged off rates is very strong. This is the feature that has the highest impact on default rates. The EL of people with 0 Inquiries is more than 4 times smaller than the EL of the 7+ bucket!
## inq_bucket charged net_EL avg_fico avg_grade
## (chr) (dbl) (dbl) (dbl) (chr)
## 1 0 10.22 5.91 719 B4
## 2 1-2 13.29 8.17 714 C1
## 3 3-4 19.05 12.28 711 C1
## 4 5-6 26.57 18.13 710 C5
## 5 7+ 35.40 24.68 702 D5Geography:
Finally, here is a heat map of the USA. We see that Nevada is not doing so great…
Conclusion
Lending Club’s data is a great source of information on personal credit. Additionally this data set is bound to grow exponentially over the next years. We tried to build a report to both present Lending Club and build the foundations to more in depth analyses.
To continue this analysis we need to be careful with the following:
- There is a geographical selection bias: as we saw in the previous maps most accounts are very concentrated geographically.
- By nature of the platform, we are selecting only applicants who are somewhat tech-savvy.
- We need to be careful with the implications of certain features when trying to extract correlation. As stated earlier, features like number of accounts or age of credit history are correlated to the age of the borrower. Could their relationship to default rates be linked to their age and indirectly to their income?
- We have to be careful with adverse selection, a mistake that the financial industry as know for a long time. Let’s keep in mind that the safe way to do financial services is to keep the segmentation down to a minimum and let the mathematics of risk pooling look after us.
The next steps of this analysis could be some of the following:
- Building a credit model and see if we can predict reliably defaults.
- Building a model to arb LC’s own model.
- Trying to replicate LC’s models using machine learning techniques using the Lending Club’s grades as our outputs.
Check out the full report here! You will find all the details of the code behind the analysis and the visualisations.
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Jean-Francois Darre
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