Risk, Data, Alignment: Making Credit Scoring Work in Kenya

TL;DR

The paper investigates the sociotechnical and institutional work behind credit scoring in Kenya, showing how new data sources, actors, and regulatory shifts shape risk assessment in digital lending. It argues that risk and uncertainty are deeply entangled and that alignment operates as a two-way translation between models and the contexts they measure. Through nine months of ethnography in Nairobi, the study documents how alternative data, explainability pressures, and political considerations reshape who gets credit and under what conditions. The findings reveal performative effects of data practices, raise concerns about privacy and surveillance, and offer policy-oriented questions for governing algorithmic credit in developing contexts.

Abstract

Credit scoring is an increasingly central and contested domain of data and AI governance, frequently framed as a neutral and objective method of assessing risk across diverse economic and political contexts. Based on a nine-month ethnography of credit scoring practices in Nairobi, Kenya, we examined the sociotechnical and institutional work of data science in digital lending. While established regional telcos and banks are leveraging proprietary data to develop digital loan products, algorithmic credit scoring is being transformed by new actors, techniques, and shifting regulations. Our findings show how practitioners construct alternative data using technical and legal workarounds, formulate risk through multiple interpretations, and negotiate model performance via technical and political means. We argue that algorithmic credit scoring is accomplished through the ongoing work of alignment that stabilizes risk under conditions of persistent uncertainty, taking epistemic, modeling, and contextual forms. Extending work on alignment in HCI, we show how it operates as a two-way translation, where models are made "safe for worlds" while those worlds are reshaped to be "safe for models."