Crowd, Lending, Machine, and Bias

• URL: http://arxiv.org/abs/2008.04068v1
• Date: Mon, 20 Jul 2020 01:26:00 GMT
• Title: Crowd, Lending, Machine, and Bias
• Authors: Runshan Fu, Yan Huang and Param Vir Singh
• Abstract summary: We show that a reasonably sophisticated machine learning algorithm predicts listing default probability more accurately than crowd investors. When machine prediction is used to select loans, it leads to a higher rate of return for investors and more funding opportunities for borrowers. We propose a general and effective "debasing" method that can be applied to any prediction focused ML applications.
• Score: 10.440847890315293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• The Gendered Algorithm: Navigating Financial Inclusion & Equity in AI-facilitated Access to Credit [0.0]
  A growing trend in financial technology (fintech) is the use of mobile phone data and machine learning (ML) to provide credit scores. This paper draws on interview data with leaders, investors, and data scientists at companies developing ML-based alternative lending apps. It examines how the underlying logics, design choices, and management decisions by developers embed or challenge gender biases.
  arXiv  Detail & Related papers  (2025-04-09T22:28:21Z)
• Bank Loan Prediction Using Machine Learning Techniques [0.0]
  We have worked on a dataset of 148,670 instances and 37 attributes using machine learning methods. The best-performing algorithm was AdaBoosting, which achieved an incredible accuracy of 99.99%.
  arXiv  Detail & Related papers  (2024-10-11T15:01:47Z)
• American Option Pricing using Self-Attention GRU and Shapley Value Interpretation [0.0]
  We propose a machine learning method for forecasting the prices of SPY (ETF) option based on gated recurrent unit (GRU) and self-attention mechanism. We built four different machine learning models, including multilayer perceptron (MLP), long short-term memory (LSTM), self-attention LSTM, and self-attention GRU.
  arXiv  Detail & Related papers  (2023-10-19T06:05:46Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Superhuman Fairness [2.610470075814367]
  We re-cast fair machine learning as an imitation learning task by introducing superhuman fairness. We demonstrate the benefits of this approach given suboptimal decisions.
  arXiv  Detail & Related papers  (2023-01-31T05:23:53Z)
• Quantum computational finance: martingale asset pricing for incomplete markets [69.73491758935712]
  We show that a variety of quantum techniques can be applied to the pricing problem in finance. We discuss three different methods that are distinct from previous works.
  arXiv  Detail & Related papers  (2022-09-19T09:22:01Z)
• Uncovering the Source of Machine Bias [9.75150920742607]
  We find two types of biases in gender, preference-based bias and belief-based bias, are present in human evaluators' decisions. We quantify the effect of gender bias on loan granting outcomes and the welfare of the company and the borrowers. We find that machine learning algorithms can mitigate both the preference-based bias and the belief-based bias.
  arXiv  Detail & Related papers  (2022-01-09T21:05:24Z)
• Understanding the Usability Challenges of Machine Learning In High-Stakes Decision Making [67.72855777115772]
  Machine learning (ML) is being applied to a diverse and ever-growing set of domains. In many cases, domain experts -- who often have no expertise in ML or data science -- are asked to use ML predictions to make high-stakes decisions. We investigate the ML usability challenges present in the domain of child welfare screening through a series of collaborations with child welfare screeners.
  arXiv  Detail & Related papers  (2021-03-02T22:50:45Z)
• When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making [68.19284302320146]
  We carry out user studies to assess how people with differing levels of expertise respond to different types of predictive uncertainty. We found that showing posterior predictive distributions led to smaller disagreements with the ML model's predictions. This suggests that posterior predictive distributions can potentially serve as useful decision aids which should be used with caution and take into account the type of distribution and the expertise of the human.
  arXiv  Detail & Related papers  (2020-11-12T02:23:53Z)
• An Information-Theoretic Approach to Personalized Explainable Machine Learning [92.53970625312665]
  We propose a simple probabilistic model for the predictions and user knowledge. We quantify the effect of an explanation by the conditional mutual information between the explanation and prediction.
  arXiv  Detail & Related papers  (2020-03-01T13:06:29Z)
• A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores [85.12096045419686]
  We study the adoption of an algorithmic tool used to assist child maltreatment hotline screening decisions. We first show that humans do alter their behavior when the tool is deployed. We show that humans are less likely to adhere to the machine's recommendation when the score displayed is an incorrect estimate of risk.
  arXiv  Detail & Related papers  (2020-02-19T07:27:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.