‘All data is credit data’: Constituting the unbanked

Abstract

Global financial and data capitalism has constituted new forms of knowledge, novel inscriptions which make that knowledge tangible and new ways of visualizing sources of value and profit. This paper examines a cluster of new practices designed to make visible – and extract value from – those without formal credit scores in contemporary financial markets. Many ‘financial inclusion’ projects now attempt to score the ‘credit invisible’ by drawing on a range of alternative data – non-financial payment streams, academic records, behavioural signals gleaned from online or social media footprints and results generated via digitized psychometric testing – and by assessing that data in relation to models of risk assessment based on the analysis of big data. I argue in this paper that these experiments in alternative credit scoring constitute the unbanked as an important, and dubious, category of knowledge and intervention. I also argue that attempts to score the unbanked offer a revealing glimpse of many of the social and political limitations associated with projects of ‘inclusion’. Although often imagined as forms of pristine incorporation, inclusion projects often constitute troubling new kinds of social sorting and segmentation.

Varieties of consumer credit data regimes: A regulatory governance approach

“…The collection, analysis, and sale of credit data poses harms to individuals' autonomy and regiments their behavior. Those risks are even greater when big data technologies are used to score the "credit invisible" people who have little or no scorable credit history (Aitken, 2017;Citron & Pasquale, 2014;Hurley & Adebayo, 2016). This has implications for the formation of financial subjects; driven by a desire to be more creditworthy (i.e., improve their credit scores), people are required to learn the credit score game, to follow rules that adhere to a different logic than their everyday lives, and to conform to financial behavior that they often disidentify with (Kear, 2017).…”

“…With the ever-falling price of digitalization, innovative information technologies make it increasingly easy to collect big data and analyze it through automated algorithms and machine learning. Consequently, consumer financial information has become an instrument of social and economic engineering that makes individuals identifiable and available as "financial" assets (Aitken, 2017;Ferretti, 2017;Fourcade & Healy, 2017;Rona-Tas, 2016;Trumbull, 2010;Trumbull, 2014;Zuboff, 2015). Consumer data lie at the forefront of the surveillance economy, and their regulatory governance are increasingly important for both consumer welfare and the promotion and protection of liberal society.…”

“…Also, extensive processing of personal data comes at the expense of individuals' privacy interests; it may raise issues over data protection rights and create greater risks of misuse of personal information and identity theft (Cate, 2002;Jentzsch, 2007). Those risks are even greater when big data technologies are used to score the "credit invisible" people who have little or no scorable credit history (Aitken, 2017;Citron & Pasquale, 2014;Hurley & Adebayo, 2016). Furthermore, mistakes and inaccuracy of the data collected could prevent individuals from accessing services or products, thereby tremendously impacting individuals' lives, especially considering their weak bargaining position as compared to the powerful corporations holding their information (Ferretti, 2017;O'Neil, 2016;Zarsky, 2016).…”

Tracing mutations of neoliberal development governance: ‘Fintech’, failure and the politics of marketization

“…Cignifi has developed a proprietary algorithm that uses on a behavioural model drawing on data on calls and texts received per day, along with patterns of web and social network usage, to assess the creditworthiness of mobile users who can then be selectively targeted for financial products. This is licensed out to telecommunications operators and financial service providers (see Aitken 2017).…”

“…Aitken (2017) offers a closely cognate reading of emergent 'alternative' forms of credit data, arguing that they actively constitute the 'unbanked' as subjects legible to global financial markets.…”