Do FinTech algorithms reduce gender inequality in banks loans? A quantitative study from the USA

IF 1.4 4区经济学 Q3 ECONOMICS

Journal of Applied Economics Pub Date : 2024-08-18 DOI:10.1080/15140326.2024.2324247

Ziheng Song, Shafiq Ur Rehman, Chun PingNg, Yuan Zhou, Patick Washington, Ricardo Verschueren

引用次数: 0

Abstract

The potential of FinTech algorithms to decrease gender bias in credit decisions is limited by the impartiality of the data used to train them. If the data is partial or biased, the algorithmic deci...

查看原文本刊更多论文

金融科技算法能否减少银行贷款中的性别不平等？美国的一项定量研究

金融科技算法在减少信贷决策中的性别偏见方面的潜力受限于用于训练算法的数据的公正性。如果数据不完整或有偏差，那么算法的决策就会受到影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Applied Economics ECONOMICS-

CiteScore

3.00

自引率

0.00%

发文量

审稿时长

40 weeks

期刊介绍： The Journal of Applied Economics publishes papers which make a significant and original contribution to applied issues in micro and macroeconomics. The primary criteria for selecting papers are quality and importance for the field. Papers based on a meaningful and well-motivated research problem that make a concrete contribution to empirical economics or applied theory, in any of its fields, are especially encouraged. The wide variety of topics that are covered in the Journal of Applied Economics include: -Industrial Organization -International Economics -Labour Economics -Finance -Money and Banking -Growth -Public Finance -Political Economy -Law and Economics -Environmental Economics