Privacy in Online Social Lending

Online social lending is the Web 2.0’s response to classical bank loans. Borrowers publish credit applications on websites which match them with private investors. We point to a conflict between economic interests and privacy goals in online social lending, empirically analyze the effect of data disclosure on credit conditions, and outline directions towards efficient yet privacy-friendly alternative credit markets. “There is no practice more dangerous than that of borrowing money,” said George Washington in 1797. For sure he has not accounted for the privacy risks of 21 century online social lending, the topic of this paper. Online social lending, also known as peer-to-peer lending, has grown rapidly after the launch of the first commercial platform, UK-based Zopa.com, in 2005. Drawing on concepts of (offline) micro-finance, the idea of social lending is to provide a marketplace for unsecured personal loans: an online platform lets borrowers advertise credit projects to individual lenders, who decide in which project they invest. Credit risk is shared in project-specific pools of lenders; each member funds a small share of the financed amount. As compensation for taking risk, interest is paid to the lenders, whereas platform operators typically charge fixed (i. e., riskfree) fees. The exact market mechanism differs between platforms and has very recently been subject to research in mechanism design (Chen, Ghosh, and Lambert 2009). Independent of the specific mechanism, matching borrowers’ demand with lenders’ supply online sidesteps the traditional role of banks as intermediaries in credit markets. Obviously, this technology-driven paradigm shift in organizing credit markets has a string of economic and social consequences. This paper covers only a small part, namely the role of personal data and the impact of social lending on borrowers’ informational privacy. Borrowers’ privacy is affected since credit applications entail personal data being irrevocably disclosed on the Internet. We will briefly revisit the role of information in credit markets from a privacy protection point of view and contrast it with empirical results from data collected on Smava.de, the largest German social lending platform. To the best of our knowledge, this is the first attempt to study social lending from a privacy angle. AAAI Spring Symposium on Intelligent Information Privacy Management, Stanford University, Palo Alto, CA, March 2010 Background and Research Question Adequate and coherent privacy regulation requires deep understanding of how the distribution of personal data (i. e., knowledge about individuals’ attributes) in a society affects social welfare. It is unlikely that in the near future, a single unified model will be ripe enough to guide policy makers. Therefore it is advisable to break the large problem into smaller, more tractable sector-specific ones. Credit markets form a particularly relevant sector for at least three reasons: first, they are core to modern economies’ allocation of capital; second, credit markets are driven by information and thus exhibit a clear link to questions of privacy regulation; and third, advances in technology are about to change the shape of credit markets substantially, as witnessed by the uptake of online social lending over the past couple of years. A welcome side-aspect is the availability of empirical data. Personal Information in Credit Markets Aside from traditional functions of financial intermediaries in credit markets, such as size, risk, and maturity transformation, modern economic theory recognizes that information is crucial to prevent market failure (Stiglitz 1981). At the same time, useful information almost always consists of personal data of borrowers. While privacy activists were already concerned about the use of such information by hopefully trustworthy institutions, such as banks and credit bureaus (Jentzsch 2007), the problem exacerbates when creditrelevant personal data is disclosed to every potential lender. For current platforms, this means all Internet users. Borrowers’ personal data may influence lenders’ credit decisions by several mechanisms: • Most importantly, information asymmetries (Akerlof 1970) preclude lenders from distinguishing between good and bad risks. Detailed information on borrowers and their envisaged projects helps to assess the likelihood of timely debt service and to adjust credit conditions accordingly. This improves the overall allocation of capital. • Knowing personal details of creditors, including their identity, facilitates contract monitoring and legal recourse in case of default. The mere possibility to do so can prevent moral hazard over the payback period. • This feedback channel exists also indirectly through joint liability and social sanctions (Besley and Coate 1995). Debt service is encouraged by pressure from the borrower’s close social network (e. g., family, community) whose reputation is at risk in the case of default. These mechanisms require the disclosure of personal data of the borrower’s peers. A borrower’s decision to seek credit thus affects the privacy of others, too. • One has to consider behavioral effects as well: trust between market participants can be established by communicating personal attributes as social cues (Kolm 2000). • Borrowers may use persuasion techniques to convince potential lenders of their credit projects with rational (consistency, plausibility) or emotional arguments (helping tendency, altruism, goodness). Such arguments often tell a lot about the borrower and thus contain personal data. This list is most likely incomplete, but good enough to point out that online social lending exhibits an outright conflict between economic goals and privacy protection goals. This calls for research in at least two directions: first, to study how people deal with the conflict, and second, can we find technical, organizational, or legal provisions to alleviate it? Related Work on Online Social Lending Online social lending has recently attracted interest of scholars in economics and social sciences. Unless otherwise stated, all prior art is based on data of Prosper.com, the major online social lending platform serving US residents. Ravina (2007) as well as Pope and Syndor (2008) look at discrimination in credits decisions made on social lending platforms. They report effects of race, age, gender, and weight on credit conditions, though not always statistically significant. Credit conditions were operationalized by (inverse) interest rate, or the probability of (full) funding success. The predictors were either extracted from categorial data of the project description, or manually assigned by evaluating text or pictures. A study by Herzenstein et al. (2008) comes one step closer to privacy aspects. The authors measured the level of detail of the project description on a 3-step ordinal scale and found it to be a major influencing factor for funding success after controlling for fundamental financial parameters, such as amount, starting interest rate in an auction market, and the (endogenous) duration of the listing. In terms of predictive power, the researchers distinguished between demographic factors (e. g., gender), financial factors (e. g., credit score), and effort measures (e. g., length of project description). The first category was found to have only very little effect on funding success, whereas the latter two categories were found to be decisive. Another string of research has focused on social network theory and tries to identify decision pattern and peerinfluence within the social network of registered users of a social lending platform. An early study found that endorsement by group leaders—typically lenders—has the most positive effect on both funding success and total number of bids (Ryan, Reuk, and Wang 2007). This indicates that in social lending, a personal recommendation can be more important than ‘hard’ facts, such as credit scores. This finding was confirmed by Berger and Gleisner, who analyzed the role of group leaders as new “financial intermediaries” (Berger and Gleisner 2009). Freedman and Jin (2008) as well as HerreroLopez (2009) studied whether the social network between borrowers can contribute to reducing information asymmetries and thus is helpful to make good investments. They report empirical evidence that a borrower’s affiliation with a reputed group increases the chance of full funding and results in lower interest rates. When looking at the realized default rates, however, the picture becomes more complicated: according to a study by Everett (2008), mere group membership tends to increase default rates. Only when distinguishing between groups with supposedly strong and weak interpersonal relations (alumni of one school vs mere profession), a positive outcome (i. e., lower default rate) is observable for groups with strong interpersonal relations. The author interprets this as evidence for social sanctions.