Big-data credit scoring: risk management in Chinese social credit programmes

10/29/19 • 70 min

In this talk, Ruowen Xu examines the organisation process by which Big Data credit scoring models are produced, investigating the analytical work of data scientists who continuously maintain and improve their models to keep the results predictive. Big Data algorithmic technology is having a profound impact on our social, organisational, and public life and it permits large tech companies to perform analytics for consumer credit-risk assessments and to determine credit risk.
Based on ethnographic fieldwork in a credit score modelling team of a large tech company, Ruowen's research studies the development of an emerging Big Data algorithmic credit-scoring technology alongside the government’s programme for building a social credit system in China. Her findings show that data scientist work is a continuous, repetitive, and a pre-prescribed process of developing and updating models that are complemented with machine learning-generated results, and that the way that data scientist work is organised has a direct impact on the produced model. This research expands the perimeter of how we look at algorithms and, broadly, other data-driven computing devices by looking at the organisational setting through which they are produced.
For more information, please visit: https://warwick.ac.uk/fac/soc/pais/currentstudents/phd/resources/wcf/upcomingevents/ruowen-xu/

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WCF in discussion with Daniela Gabor

A decade after the 2008 financial crisis, new political economic imaginaries have emerged to make sense of our financialised world. Critical macro-finance is one of the most important of these trends. It has shed light on the infrastructure of contemporary global finance, the links between shadow banking, money markets and monetary policy, and the evolving governance architecture established in the wreckage of the crash.
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