Usage of Pre-Made Text-Modules and Peer-Groups for Mitigating Information Asymmetry in Social Lending: Evidence on Funding Success from German Platform Smava

Introduction

In the wake of Web 2.0, several platforms for connecting potential borrowers and lenders have emerged. The subprime crisis has endangered the stability of the global financial markets (King & Maier, 2009) and has led to a growing distrust of customers in banks (Jones, 2009). This development supported social lending platforms in becoming an attractive way for issuing unsecured loans without a bank as a financial intermediary.

In social lending, identities of market participants are only known to the platform operator; transactions are conducted between fictitious, user-chosen “screen names” (Berger & Gleisner, 2009). These anonymous actions pose a challenge also present in traditional banking: The overcoming of information asymmetry between borrowers and lenders.

The situation that leads to information asymmetry in financial relationships can be described as follows: Prior to the lending decision, the entity that posts the request for capital (the borrower) is better informed about his financial characteristics and his willingness to pay back the desired loan than any lender could ever be. In contrast to traditional banking (Leland & Pyle, 1977) where the financial intermediary faces the default risk, the situation in social lending is even more urgent: The decision of funding a loan is transferred into the “crowd” where many individual lenders provide the requested capital and consequently face default risk (Steelman, 2006; Ashta & Assadi, 2009). Additionally, individual lenders do not have the opportunity to rely on typical functions of banks (e.g. term, lot size and risk transformation) (Diamond & Dybvig, 1983). Consequently, it is desirable from a lender’s point of view to gain as much information about the borrower as possible in order to make a qualified lending decision.

Platform operators support lenders in making their investment decisions: They conduct a check on the borrower’s creditworthiness by drawing on a credit information agency and demanding proof of monthly earnings and spending from the borrower, with which they can calculate the available income for the payment of principal and interest similar to a traditional bank (Hartmann-Wendels et al., 2007). However, the operator-provided information on the borrower’s quality may not be sufficient to meet individual lender’s perceptions of risk (Berk & De Marzo, 2007).

Lenders may want further information – either from the borrower himself or from other market participants – in order to assess the borrower. To achieve this, borrowers can use operator-provided pre-made credit project-descriptions or add a self-composed description concerning the purpose of the credit request accompanied by further personal information. Furthermore, they can join peer groups (Berger & Gleisner, 2009; Lin et al., 2009a) in order to get additional support for funding of their credit requests. When the request attracts sufficient investments, it is transformed into a loan and the funded sum is transferred to the borrower.

Since platform operators charge fees for each successful funded credit application, they have an interest in providing features that may assist in reducing information asymmetry thus helping borrowers in getting their loans funded. With respect to these aspects, we address the following research question:

Different aspects of social lending have recently received attention in research (e.g. Hulme & Wright, 2006; Ashta & Assadi, 2009; Berger & Gleisner, 2009; Lin et al., 2009a; Chen et al., 2009; Böhme & Pötzsch, 2010). We contribute to this knowledge base about social lending by analyzing data from the German social lending platform Smava.