---
title: "A Market Clearing Solution for Social Lending"
authors:
  - "Ning Chen"
  - "Arpita Ghosh"
doi: "10.5591/978-1-57735-516-8/IJCAI11-037"
url: "https://ijcai.org/proceedings/2011/37"
pdf: "https://ijcai.org/proceedings/2011/0037.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "152-156"
track: ""
keywords:
---

# A Market Clearing Solution for Social Lending

Ning Chen, Arpita Ghosh

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 152-156. DOI: [10.5591/978-1-57735-516-8/IJCAI11-037](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-037)

## Abstract

The social lending market, with over a billion dollars in loans, is a two-sided matching market where borrowers specify demands and lenders specify total budgets and their desired interest rates from each acceptable borrower. Because different borrowers correspond to different risk-return profiles, lenders have preferences over acceptable borrowers; a borrower prefers lenders in order of the interest rates they offer to her. We investigate the question of what is a computationally feasible, 'good', allocation to clear this market. We design a strongly polynomial time algorithm for computing a Pareto-efficient stable outcome in a two-sided many-to-many matching market within differences, and use this to compute an allocation for the social lending market that satisfies the properties of stability — a standard notion of fairness in two-sided matching markets — and Pareto efficiency; and additionally addresses envy-freeness amongst similar borrowers and risk diversification for lenders.