Assessing Creditworthiness in the Age of Big Data

The purpose of this article is twofold: first, we show how algorithms have become increasingly central to financial credit scoring; second, we draw on this to further develop the anthropological study of algorithmic governance. As such, we describe the literature on credit scoring and then discuss ethnographic examples from two regulatory and commercial contexts: the US and Denmark. From these empirical cases, we carve out main developments of algorithmic governance in credit scoring and elucidate social and cultural logics behind algorithmic governance tools. Our analytical framework builds on critical algorithm studies and anthropological studies where money and payment infrastructures are viewed as embedded in their specific cultural contexts (Bloch and Parry 1989; Maurer 2015). The comparative analysis shows how algorithmic credit scoring takes different forms hence raising different issues in the two cases. Danish banks seem to have developed a system of intensive, yet hidden credit scoring based on surveillance and harvesting of behavioural data, which, however, due to GDPR takes place in restricted silos. Credit scores are hidden to customers, and therefore there has been virtually no public debate regarding the algorithmic models behind scores. In the US, fewer legal restrictions on data trading combined with both widespread and visible credit scoring has led to the development of a credit data market and widespread use of credit scoring by ‘affiliation’ on the one hand, but also to increasing public and political critique on scoring models on the other