# Crowd, Lending, Machine, and Bias

arXiv: https://arxiv.org/abs/2008.04068 · HTML: https://mlsanity.com/paper/2008.04068/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 13 of 13)

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- [The AI Revolution: Opportunities and Challenges for the Finance Sector](https://mlsanity.com/paper/2308.16538/) (2023)
- [Do Prevalent Bias Metrics Capture Allocational Harms from LLMs?](https://mlsanity.com/paper/2408.01285/) (2024)
- [Foundation Models for Credit Risk Prediction: A Game Changer?](https://mlsanity.com/paper/2605.18147/) (2026)
- [Uncovering the Source of Machine Bias](https://mlsanity.com/paper/2201.03092/) (2022)
- [Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation](https://mlsanity.com/paper/2305.05827/) (2023)
- [Toward a Fairness-Aware Scoring System for Algorithmic Decision-Making](https://mlsanity.com/paper/2109.10053/) (2021)
- [Ex-Ante Assessment of Discrimination in Dataset](https://mlsanity.com/paper/2208.07918/) (2022)
- [Echoes of Biases: How Stigmatizing Language Affects AI Performance](https://mlsanity.com/paper/2305.10201/) (2023)
- [DispaRisk: Auditing Fairness Through Usable Information](https://mlsanity.com/paper/2405.12372/) (2024)
- [A Bayesian Model for Multi-stage Censoring](https://mlsanity.com/paper/2511.11684/) (2025)
- [LLMs for Explainable Business Decision-Making: A Reinforcement Learning Fine-Tuning Approach](https://mlsanity.com/paper/2601.04208/) (2025)
- [Counterfactual Analysis via Large Language Models](https://mlsanity.com/paper/2608.05367/) (2026)

## Abstract (arXiv)

Big data and machine learning (ML) algorithms are key drivers of many fintech innovations. While it may be obvious that replacing humans with machine would increase efficiency, it is not clear whether and where machines can make better decisions than humans. We answer this question in the context of crowd lending, where decisions are traditionally made by a crowd of investors. Using data from Prosper.com, we show that a reasonably sophisticated ML algorithm predicts listing default probability more accurately than crowd investors. The dominance of the machine over the crowd is more pronounced for highly risky listings. We then use the machine to make investment decisions, and find that the machine benefits not only the lenders but also the borrowers. When machine prediction is used to select loans, it leads to a higher rate of return for investors and more funding opportunities for borrowers with few alternative funding options. We also find suggestive evidence that the machine is biased in gender and race even when it does not use gender and race information as input. We propose a general and effective "debasing" method that can be applied to any prediction focused ML applications, and demonstrate its use in our context. We show that the debiased ML algorithm, which suffers from lower prediction accuracy, still leads to better investment decisions compared with the crowd. These results indicate that ML can help crowd lending platforms better fulfill the promise of providing access to financial resources to otherwise underserved individuals and ensure fairness in the allocation of these resources.