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

TL;DR

This paper explores how credit scoring in Kenya is shaped by sociotechnical practices, legal workarounds, and ongoing alignment efforts amid regulatory shifts and diverse data sources, revealing complex risk management in digital lending.

Contribution

It provides an ethnographic analysis of credit scoring practices in Kenya, highlighting how practitioners create alternative data, interpret risk, and negotiate model performance through technical and political means.

Findings

Practitioners construct alternative data using technical and legal workarounds.

Risk is formulated through multiple interpretations and negotiations.

Model performance is stabilized via ongoing alignment efforts.

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."