Algorithms in Household Finance

This working group explores the growing role of algorithms and artificial intelligence in household finance, an area of economics that studies how people save, borrow, invest, and use financial products to manage their well-being.

Advances in machine learning and automated decision-making are transforming how financial services are designed, priced, and delivered. These changes raise important questions about efficiency, fairness, consumer welfare, and regulation. Our aim is to bring together researchers and practitioners interested in understanding these developments and their implications.

Format #

We have started as a reading group, meeting every two weeks to discuss recent academic papers and key research directions in the field.

We have started by focussing on credit scoring and we are discussing the following papers at our next few meetings:

• Björkegren, Daniel, Joshua E. Blumenstock, and Samsun Knight. “Manipulation-robust prediction.” (2024).
• Hurlin, Christophe, Christophe Pérignon, and Sébastien Saurin. “The fairness of credit scoring models.” Management Science (2024).
• Jansen, Mark, Hieu Quang Nguyen, and Amin Shams. “Rise of the machines: The impact of automated underwriting.” Management Science 71, no. 2 (2025): 955-975.
• Shema, Alain. “Effective credit scoring using limited mobile phone data.” In Proceedings of the tenth international conference on information and communication technologies and development, pp. 1-11. 2019.
• Bouyamourn, Adam, and Alexander Williams Tolbert. “Escaping the Subprime Trap in Algorithmic Lending.” (2025).

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Working Group Members #