Credit Scoring in the Era of Big Data

By Hurley, Mikella; Adebayo, Julius | Yale Journal of Law & Technology, Fall 2016 | Go to article overview

Credit Scoring in the Era of Big Data


Hurley, Mikella, Adebayo, Julius, Yale Journal of Law & Technology


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, "big-data" 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 Express. 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 data 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 data is credit data" approach that combines conventional credit information with thousands of data 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. …

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