Credit scoring in the era of big data

TABLE OF CONTENTS TABLE OF CONTENTS I. INTRODUCTION II. TRADITIONAL CREDIT-ASSESSMENT TOOLS III. ALGORITHMS, MACHINE LEARNING, AND THE ALTERNATIVE CREDIT-SCORING MARKET A. Introduction to basic terminology and concepts B. How traditional credit-modeling tools compare to alternative, tools C. Using machine learning to build a big-data credit-scoring model--how it works and potential problems IV. THE INADEQUACIES IN THE EXISTING LEGAL FRAMEWORK FOR CREDIT SCORING A. The Fair Credit Reporting Act (FCRA) B. The Equal Credit Opportunity Act (ECOA) V. THE CHALLENGES OF ALTERNATIVE CREDIT-SCORING AND A LEGISLATIVE FRAMEWORK FOR CHANGE A. Existing transparency rules are inadequate B. The burden of ensuring accuracy should not fall to the consumer C. Better tools are needed to detect and prevent discrimination by proxy D. Credit-assessment tools should not be used to target vulnerable consumers VI. CONCLUSION VII. ANNEXES I. INTRODUCTION One day in late 2008, Atlanta businessman Kevin Johnson returned home from his vacation to find an unpleasant surprise waiting in his mailbox. It was a letter from his credit card company, American Express, informing him that his credit limit had been lowered from $10,800 to a mere $3,800. (1) While Kevin was shocked that American Express would make such a drastic change to his limit, he was even more surprised by the company's reasoning. By any measure, Kevin had been an ideal customer. Kevin, who is black, was running a successful Atlanta public relations firm, was a homeowner, and had always paid his bills on time, rarely carrying a balance on his card. (2) Kevin's father, who had worked in the credit industry, had taught him the importance of responsible spending and, because of his father's lessons, [Kevin had] scrupulously maintained his credit since college. (3) Yet his stellar track record and efforts to maintain scrupulous credit seemed to matter little, if at all, to American The company had deemed him a risk simply because, as the letter put it, [o]ther customers who ha[d] used their card at establishments where [Kevin] recently shopped have a poor repayment history with American Express. (4) When Kevin sought an explanation, the company was unwilling to share any information on which of businesses--many of them major retailers--contributed to American Express's decision to slash Kevin's limit by more than 65 percent. (5) Kevin Johnson was an early victim of a new form of credit assessment that some experts have labeled behavioral analysis or behavioral scoring, (6) but which might also be described as creditworthiness by association. Rather than being judged on their individual merits and actions, consumers may find that access to credit depends on a lender's opaque predictions about a consumer's friends, neighbors, and people with similar interests, income levels, and backgrounds. This data-centric approach to credit is reminiscent of the racially discriminatory and now illegal practice of redlining, by which lenders classified applicants on the basis their zip codes, and not their individual capacities to borrow responsibly. (7) Since 2008, lenders have only intensified their use of big-data profiling techniques. With increased use of smartphones, social media, and electronic means of payment, every consumer leaves behind a digital trail of that companies--including lenders and credit scorers--are eagerly scooping up and analyzing as a means to better predict consumer behavior. (8) The credit-scoring industry has experienced a recent explosion of start-ups that take an all is credit data approach that combines conventional credit information with thousands of points mined from consumers' offline and online activities. (9) Many companies also use complex algorithms to detect patterns and signals within a vast sea of information about consumers' daily lives. …