Prosper Score

A custom risk score was built using historical Prosper data to assess the risk of Prosper borrower listings. The output to Prosper users is a Prosper score which ranges from 1 to 10, with 10 being the best, or lowest risk, score. The worst, or highest risk, score, is a 1. The Prosper score was built specifically on the Prosper population, so it incorporates behavior that is unique and inherent to this population. In contrast, the credit score obtained from a credit reporting agency is based on a much broader population, of which Prosper borrowers are just a small subset. As such, the credit reporting agency score should, and does, rank order risk on the Prosper population, but is not as discriminating as a custom score. Prosper uses both the custom score and the credit reporting agency score together to assess the borrower's level of risk and determine estimated loss rates, which is more powerful than using just one score. The loss estimates are based on the historical performance of Prosper loans to borrowers with similar characteristics. They are not a guarantee and actual performance may differ from expected performance.

Scorecard Development

The Prosper score estimates the probability of a loan going "bad," where "bad" is the probability of going 61+ days past due. Loans booked from April, 2007 through October, 2008 were used to build the discrete additive scorecard, with the performance measured for the following fifteen months. The scorecard was verified and results validated on an independent validation sample of loans booked during the same time period, with the performance measured for the following fifteen months.

All potential variables available at the time of listing, including those from the identification authorization process, the credit report, and listing details provided by the borrower were analyzed for potential inclusion in the final scorecard. For example, variables such as authorization score (used during identity verification), income, debt-to-income ratio, total revolving balance and delinquencies were reviewed. Transformations to refine the variables were performed during the development process. Variables were dropped or kept in the final scorecard based on their contribution and stability over time. Many scorecard iterations were completed and analyzed in order to determine the final scorecard.

Key variables in the scorecard are:

• Number of trades
• Number of delinquent accounts
• Number of inquiries
• Number of recently opened trades
• Amount of available credit on bankcards
• Bankcard utilization