Algorithms in Household Finance

Read a summary of the Algorithms in Household Finance Working Group’s discussions during the second half of 2025 on Medium.

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.

We started in September 2025 and are preparing to begin our second year. During our first year, our discussions have ranged from credit scoring and automated lending to financial abuse, disaster relief, and algorithmic rental pricing.

Format #

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

Papers Discussed #

First Half of 2026 #

• Liu, Yidi, Xin Li, and Zhiqiang Zheng. “Smart natural disaster relief: Assisting victims with artificial intelligence in lending.” Information Systems Research 35, no. 2 (2024): 489–504.
• Calder-Wang, Sophie, and Gi Heung Kim. “Algorithmic pricing in multifamily rentals: Efficiency gains or price coordination?” Working Paper (2024).
• Adams, Abi, Kristiina Huttunen, Emily Nix, and Ning Zhang. “The dynamics of abusive relationships.” The Quarterly Journal of Economics 139, no. 4 (2024): 2135–2180.
• Bellini, Rosanna. “Paying the price: When intimate partners use technology for financial harm.” In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pp. 1–17. 2023.
• Avenhaus, Rudolf, Bernhard von Stengel, and Shmuel Zamir. “Inspection Games.” February 5, 2001.

Second Half of 2025 #

We began by focusing on credit scoring, with the following papers:

• 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).

Working Group Organizer #

Working Group Members #

Past Working Group Members #