For most Americans, access to credit is an essential requirement for upward mobility and financial success. A favorable credit rating is necessary to purchase a home or car, to start a new business, to seek higher education, or to pursue other important goals. For many consumers, strong credit is also necessary to gain access to employment, rental housing, and essential services such as insurance. At present, however, individuals have very little control over how they are scored and have even less ability to contest inaccurate, biased, or unfair assessments of their credit. Traditional, automated creditscoring tools raise longstanding concerns of accuracy and unfairness. The recent advent of new “big-data” credit-scoring products heightens these concerns.The credit-scoring industry has experienced a recent explosion of start-ups that take an “all data is credit data” approach, combining conventional credit information with thousands of data points mined from consumers’ offline and online activities. Big-data scoring tools may now base credit decisions on where people shop, the purchases they make, their online social media networks, and various other factors that are not intuitively related to creditworthiness. While the details of many of these products remain closely guarded trade secrets, the proponents of big-data credit scoring argue that these tools can reach millions of underserved consumers by using complex algorithms to detect patterns and signals within a vast sea of information. While alternative credit scoring may ultimately benefit some consumers, it also poses significant risks. Credit-scoring tools that integrate thousands of data points, most of which are collected without consumer knowledge, create serious problems of transparency. Consumers have limited ability to identify and contest unfair credit decisions, and little chance to understand what steps they should take to improve their credit. Recent studies have also questioned the accuracy of the data used by these tools, in some cases identifying serious flaws that have a substantial bearing on lending decisions. Big-data tools may also risk creating a system of “creditworthiness by association” in which consumers’ familial, religious, social, and other affiliations determine their eligibility for an affordable loan. These tools may furthermore obscure discriminatory and subjective lending policies behind a single “objective” score. Such discriminatory scoring may not be intentional; instead, sophisticated algorithms may combine facially neutral data points and treat them as proxies for immutable characteristics such as race or gender, thereby circumventing existing non-discrimination laws and systematically denying credit access to certain groups. Finally, big-data tools may allow online payday lenders to target the most vulnerable consumers and lure them into debt traps.Existing laws are insufficient to respond to the challenges posed by credit scoring in the era of big-data. While federal law prohibits certain forms of discrimination in lending and ensures that consumers have limited rights to review and correct errors in their credit reports, these laws do not go far enough to make sure that credit-scoring systems are accurate, transparent, and unbiased. Existing laws also do little to prevent the use of predatory scoring techniques that may be geared to target vulnerable consumers with usurious loans.This article, which has been developed as part of a collaborative effort between lawyers and data scientists, explores the problems posed by big-data credit-scoring tools and analyzes the gaps in existing laws. It also sets out a framework for comprehensive legislative change, proposing concrete solutions that would promote innovation while holding developers and users of credit-scoring tools to high standards of accuracy, transparency, fairness, and non-discrimination.