August 15th, 2012 
jasonejacks Here at P2P Analytics.com, our goal is to make your job as a P2P lending investor as simple as possible. That’s why over the next few weeks, the subscription service will be upgraded to include a login area that contains the following features:
1. An easier, more graphical representation of the notes suggested by the algorithm (with links to the Lending Club platform included)
2. Graphical, charted representation of the week by week statistics on average rankings.
3. A real time default rate chart which shows a review of the P2P Analytics.com selection strategy based on all of the previously selected notes.
4. The money flow index.
If anyone has any suggestions for how the P2P Analytics.com service can be improved, please let us know.
Posted in 
Hi,
I’m considering using your service. Currently I’ve been using Smart Peer Lending’s loan recommendations. Without giving anything proprietary away, is your analysis similar? http://smartpeerlending.com/posts/improving-returns-with-default-prediction
Larry Ventura
LC Investor
The analysis performed on In Funding Notes at P2P Analytics.com is similar to this platform’s method. I see a few key differences and points of comparison between this service and P2P Analytics:
#1. Returns: Smart Peer Lending has provided no data regarding the success of their algorithm. The data for P2P Analytics can be found here: http://www.p2panalytics.com/july-20-2012-final-algorithm-results-and-comparison/
#2. I see no evidence that this algorithm has a different set of variables for each grade. The Smart Peer lending algorithm seems to treat all grades the same at the expense of accuracy. One can say, for example, “Mortgage holders default less than Renters”, but the amount by which this factor is considered must be different for each grade, as shown in this post: http://www.p2panalytics.com/july-9th-2012-a-quick-lesson-in-being-specific/
#3 Algorithm “overswamping”. It seems that the Smart Peer Lending algorithm takes all of these factors into account: http://smartpeerlending.com/statistics/lending-club-statistics . This can be a bad thing because: Is enough statistical data present to build assumptions on some of these factors? For example: I see that according these charts, a good amount of people from Indiana seem to be deadbeats (with an estimated ROI of -7.28%),even though there have only been 19 loans made to Indiana. My point is that, it seems that the algorithm may include too many statistically unsupportable assumptions.
#4. Customer service. Since P2P Analytics is a paid service and is currently maintained, your service comes first. For example, over the next few days a new customer login area will be implemented to make this service more valuable to its users.
If you have any more questions, feel free to send an email to contact {at} p2p analytics . com (without all the spaces)