Research in the UK has repeatedly found evidence of spotlighting. Bias based on the way a person speaks can affect people in the classroom, the office, and beyond.
Our research on dialects and peer-to-peer lending in China shows that such language differences can also affect access to finance. Specifically, borrowers who speak minority dialects receive smaller loans and higher default rates due to dialect biases, communication barriers, or dialect discrimination.
Financial technology (abbreviated as fintech) is a technology and innovation that aims to compete with traditional financial institutions. The growing industry is an alternative to banks and personal loans that allow people to borrow directly from each other or borrow from each other. These internet-based companies have low transaction fees and make up for the shortcomings of traditional small loan banks.
Inequality in traditional financial services is well documented, and there is an ongoing debate about diversity in the industry. However, it focuses on more overt forms of bias, such as racial or ethnic bias, while our research shows that bias can be broader and more subtle in the form of dialect discrimination.
People who speak different dialects often have different cultural customs, and sometimes they seem to be from different countries. Thus, if there are multiple dialects in an area, this may create communication barriers and eventually affect borrowers with different dialects.
China is one of the largest fintech lending markets, with nearly 2,500 platforms supporting peer-to-peer lending. There are 17 major dialects and 105 sub dialects in 300 Chinese cities. In different provinces, pronunciation and grammar have developed so much that the types of spoken language are often incomprehensible to each other.
My colleagues Zhongfei Chen, Ming Jin and Yuwei Li 210841 and I reviewed a credit history from one of the top online lending platforms in China between 2013 and 2018. We use this data to examine the relationship between dialect diversity (the number of dialects spoken in a city) and borrower behavior.
We found that borrowers in areas with minority dialects are more likely to receive small loans when using a fintech platform. They also have higher default rates than borrowers in cities with less dialect diversity. This is compared to other people with the same income level and other characteristics such as gender, age, education and credit rating.
Financial literacy. Lost in translation?
On the platform we tested, the borrower applied for a loan and the company reviewed the application before determining the final loan amount and interest rate. The lending platform operates as a third party and does not want borrowers to default. Therefore, they treat loan applicants with care.
For multiple transactions between users, the authorization process takes place online. But for certain types of loans (for example, where the platform acts as a guarantor), borrowers have to look for them themselves. The reviewers collect data on gender, marital status, occupation, education, age, and income. We believe that in this review dialect bias and cultural discrimination could be detrimental to borrowers who speak minority dialects.
The relationship between financial access and dialect diversity may be due to differences in levels of financial knowledge and thus communication barriers between users of different dialects. In fact, there is evidence linking language to financial literacy. Low levels of financial literacy in China (about 28% of adults, compared to 67% in the UK) can make it difficult to communicate about debt and finances between speakers of different languages.
The results of our study are not entirely surprising. Linguistic and dialectic discrimination is a long-standing problem in China. Previous research has shown that ethnic minorities often face economic losses compared to other groups.
But our findings show how fintech platforms create mechanisms through which discrimination against minority dialects can occur. This is especially important in a country with high income inequality like China. There may be language differences for those who need it in the flow of credit.
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Section : How FinTech Can Discriminate Among Speakers of Minority Dialects. New Directory from China (2022, Oct 17) Accessed Oct 17, 2022 from https://phys.org/news/2022-10-financial-technology-discriminate-people-minority.html
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