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Tell me who your friends are and I will tell you who you are. This ancient social philosophy is at the heart of a new financial technology system—social credit. In recent years, loosely regulated marketplace lenders have increasingly developed methods to rank individuals, including those traditionally considered unscored or credit-less. Specifically, some lenders build their score-generating algorithms around behavioral data gleaned from social media and social networking information, including the quantity and quality of social media presence, the identity and features of the applicant's contacts, the applicant's online social ties and interactions, the applicant's contacts' financial standing, the applicant's personality attributes as extracted from her online footprints, and more. This Article studies the potential consequences of social credit systems predicated on a simple transaction: authorized use of highly personal information in return for better interest rates. Following a detailed description of emerging social credit systems, the Article analyzes the inclination of rational and irrational customers to be socially active online and/or disclose all their online social-related information for financial ranking purposes. This examination includes, inter alia, consumers' preferences as well as mistakes, gamesmanship, and consumers' self-doxing or lack thereof. The Article then moves to discuss policy challenges triggered by social-based financial ranking that may become the new creditworthiness baseline criteria. It focuses on (i) direct privacy harms to loan seekers, and derivative privacy harm to loan seekers' online contacts or followers, (ii) online social segregation potentially mirrored by offline social polarization, and (iii) due process violations derived from algorithmic decision-making and unsupervised machine learning. The Article concludes by making a significant normative contribution, introducing a limited "right to be unnetworked," to accommodate the welcomed aspects of social credit systems while mitigating many of their undesired consequences.