Any interest from this saavy group? For example str(data) gives us the structure of the dataset, summary(variable) gives us the summary statistics of variables. This may include affiliate relationships, and receiving access to products and/or services, or compensation, for the purposes of product and service reviews and giveaways. Thus any outlier was able to drive the line up. Unlike credit score obtained from a credit reporting agency is based on a much broader population, Prosper Score is based on a more precise picture from a smaller lending platform subset. The Exploratory Data Analysis is a good way to know the data using vivid In the next part, I will explore the major features may be related to Prosper Score, which have the probability to make Prosper Rating be more discriminating in evaluating quality of loans. I first removed the the top 1% data from the original and result distribution looks like a gamma distribution. I created a new variable ‘check_default’ to group the status into two catagories: 0-‘completed’, 1-‘chargedoff’ or ‘defaulted’. If the borrower cancels it, it’s ‘withdrawn’. Eligibility for personal loans up to $40,000 depends on the information provided by the applicant in the application form. In this data set, I found the variables related to this kind of scores were CreditScoreRangeLower and CreditScoreRangeUpper. So in the next plot, I removed the top 1% data from the original and got a more reasonable plot. According to above definitions by Bank of America, the distribution of BorrowerAPR and BorrowerRate should be similar with slight difference since the APR is always higher than the interest rate. Hence, I search what’s the major elements of Prosper Score. The bar chart above shows that the proportion of high risk loans have decreased after 2009 from about 37% to 30%. This feature is proved to be efficient to predict whether there will be a defaulted loan or not. The points are clustered at the bottom left corner, when a borrower has relatively low credit amount (<25,000) the probabilities of getting low and high interest rates are similar. Another feature can be used the predict the interest rata is “AvailableBankcardCredit” . Let’s compare the relationship between each level of Credit Grade and Prosper Rating from HR to AA(high to low risk) and Loan Status. Sign up to receive blog updates, actionable tips, and more right to your email inbox, right now! The lending platform spreads the risk across many investors, and it make the measuring way to be very different. Prosper conduct stricter loan auditing after 2009.
Therefore I made the second plot with weekly means. After reading through the descriptions and definitions, I chose ‘BorrowerRate’ as my target variable since this number matters a lot to a potential borrower.
The lower percentage of High Risk loan, the better the Rating is. Unsubscribe at any time.
It's bad enough that new lenders are driving down the % below sustainable limits. So these information can not be included in our model. If so, why did you do this? Let’s check out Prosper page to see their risk rating metric from an investor’s perspective. So I included another variable (listing catagory) here to see if we can find any interesting trend for individual listings. Two factors that has “significant”” impact on the interest rates are are “ProsperRating..Alpha.” and “ProsperScore”. Join our newsletter and receive actionable money-saving ideas, blog updates, and more, right to your inbox, right now! The score ranges from 1-10, with 10 being the best, or lowest risk score.
We hate spam as much as you do! Maybe I'm just hitting a run of bad fortune and having my money tied up for a week or so while Prosper.com tries to close the loop with the borrower after the auction has completed, fully funded. From a scatter matrix plot, I filtered out some variables like the “CurrentCreditLines”, “EmploymentStatus”, “InquiriesLast6Months”, and “DelinquenciesLast7Years”.
This Prosper Dataset was provided by Udacity as a part of Data Analyst Nanodegee(last updated on 03/11/2014) which can be download here. Prosper Score consider the borrower behaviors that is unique among platform population. Occupation is the occupation selected by the Borrower at the time they created the listing. Compared to Credit Score Average, the variance of Credit Score Average in each Prosper Rating is kind of larger than Prosper Score’s. So I created a new dummy variable ‘check_default’ using ‘0’ to represent a completed loan and ‘1’ to represent a default or charged off loan.
Did they ever intend to borrow the money through Prosper.com?
The reason is, on P2P lending platform, we can not get a lot of information about borrowers, the only reference sources are metrics provided by P2P lending platform. In risk management one important job is to build a predictive model to predict whether the loan will be default or not. Because we know the lending platform offers borrower a additional platform when he can’t borrow from a bank which measure credit score in more strict way. The ability of Prosper Rating performed better on assessing the high risk loans compared to Credit Grade applied before 2009. and interesting visualizations. (No junk, ever.). This project is part of the Udacity Data Analyst Nano Degree Program. This score is represented as Prosper Rating and Prosper score. Note: For more detail exploration result, see my report on Rpubs and codes in GitHub! I want to check how do Prosper Rating and Credit Grade assess both of the bad and good loans. However, since Prosper combined Prosper Score as well, it makes Prosper Rating present much better measuring ability and appear a much better discriminating between completed and high risk loans. The meanings of each number can be found on the Documentation. My impression, without having really dug into it, is that it's catching more bogus loan requests. The new loan performance shows that Prosper’s loan default rate has been significantly reduced.
Although these platforms provide credit score and basic information of borrowers to ensure lending tradings are in a safety environment, there are still thousands of people under risk of losing money. The plots also illustrate similar distributions. Unlike an I also created a Datetime variable ‘ListingDateTime’ in format of ‘yyyy-mm-dd’ to indicate the loan listing date. Even Prosper — a leading financial peer-to-peer lending platform company, still suffered from credit risk issues before.
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