social network

Do Your Friends Determine If You Are Creditworthy?

wiseclerk 4 Comments

A new US patent filed claims that may be a good idea! The patent application “System and method for assessing credit risk in an on-line lending environment” describes a risk assessment method, where the first level links on a social network would be checked for a borrower. It aims to derive insights from looking at the age and “activity” of these first level contacts.

Does What Your Friends Say About You Determine If You Are Creditworthy?

In a step further the method suggests to “invite linked users to provide a personal endorsement of the borrowing party; sending an endorsement invitation to identified users; and receiving endorsements from the identified users, the endorsement providing a rating of the user trustworthiness based on a numerical scale; determining and aggregate endorsement score from received endorsement which is included in the assessment score”

Assessment score to be used as one of several criteria

The patent filed by Canadian company Neobanx Technologies, Inc was previously already filed in Canada. Inventors Ronald N. Ingram, Dylan Littlewood and Aston Lau describe the whole process with assessment score and endorsement score being only 2 of multiple elements that are used to assess the risk.

The potential problems with using data from social networks for the purpose of risk assessment in p2p lending were already described in detail in the article: “For Debate: Can Data from Social Networks be Used to Reduce Risks in P2P Lending“.  I still think that social network data could be used to some degree as additional data for lenders – but not to the degree this patent seems to imply.

It would be most interesting to see this implemented and monitor how it works out.

What is your opinion, dear reader?

For Debate: Can Data from Social Networks be Used to Reduce Risks in P2P Lending?

wiseclerk 7 Comments

P2P Lending is mostly anonymous and loans are unsecured. To make the risks of lending to a stranger acceptable for lenders, p2p lending services had to provide models for the lenders to judge the dimension of the risk of not getting paid back.

The initial estimation of the risk-level could not come from the platform itself as it had no track record and could not build a model that “calculated” the level of risk involved for the lender. The consistent consequence was that nearly all p2p lenders relied on established third party providers for credit history data and credit scores. Prosper for example showed Experian data on default levels to be expected depending on credit grade.

Over the time it became obvious that the actual default levels at Prosper were much higher than the expected default levels based on Experian data. We don’t actually need to argue here what led to this (be it financial development of the economy, be it that p2p lending attracted bad risks, be it a poor validation process), but the result was that since defaults were much higher than expected, lender ROIs were much lower than expected at the time of the investment.

And this is not Prosper specific. Several other p2p lending services show clear signs that default levels will (or have) surpassed the initially published percentages of defaults to be expected based on external data.

Boober failed due to default levels, on Smava levels are higher than the Schufa percentages fore-casted, same is likely for Auxmoney defaults which will be higher then Schufa and Arvato Infoscore data suggested. The one exception from the rule is Zopa UK, which successfully manages to keep defaults low, as CEO Giles Andrews rightly points out.

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