Does fintech discriminate against minority-speaking people?

Athanasios Andrikopoulos, University of Hull

In the UK, studies have repeatedly found evidence of accentism. Prejudice based on someone’s way of speaking can affect people in the classroom, the office, and beyond.

Our research into dialects and peer-to-peer lending in China found that this type of linguistic discrimination can also affect someone’s access to finance. Specifically, that accent bias, communication barriers or dialect discrimination can lead to borrowers who speak minority dialects getting smaller loans and having higher default rates.

Financial technology (fintech for short) is the technology and innovation that aims to compete with traditional financial institutions. The emerging industry is an alternative to banks and personal loans, allowing people to lend or borrow money directly from each other. Based online, these companies have lower transaction costs and complement the shortcomings of traditional banking for small loans.

Discrimination is well documented in traditional financial services and there have been ongoing discussions about diversity in the sector. However, these have focused on more obvious forms of prejudice, such as ethnic or racial bias, while our study shows that this can be broader and more subtle, in the form of dialect discrimination.

People who use different dialects often have distinct cultural habits, sometimes almost as if they are from different countries. So if there are multiple dialects in an area, this can lead to communication barriers and ultimately affect borrowers with different dialects.

China is one of the largest fintech credit markets, with around 2,500 platforms supporting peer-to-peer lending. There are 17 major dialects and 105 sub-dialects spoken in the 300 cities of China. The pronunciation and grammar in different provinces have developed to such an extent that variations of spoken language are often mutually unintelligible.

My colleagues Zhongfei Chen, Ming Jin, Youwei Li and I examined 210,841 historical loan records from one of China’s first online lending platforms, covering the period from 2013-2018. We used these data to explore the links between dialect diversity (the number of dialects spoken in a city) and borrower behaviour.

We found that borrowers from areas where several minority dialects are used received smaller loans when using the fintech platform we studied. They also had higher defaults, compared to borrowers from cities with less dialect diversity. This was the case when comparing people with similar income levels and other characteristics such as gender, age, education and credit rating.

Financial literacy – lost in translation?

In the platform we examined, borrowers apply for a loan and the company assesses the application before deciding the final loan amount and interest rate. The lending platform plays the role of a third party and does not want borrowers to default. As a result, they treat loan applicants with caution.

With some transactions between users, the approval process takes place online. But borrowers of certain types of loans (such as those where the platform acts as guarantor) must also pass a personal assessment. Reviewers collect data on gender, marital status, work, education, age and income. It is in these reviews, we believe, that accent bias and cultural discrimination can work against borrowers who speak minority dialects.

The link between access to finance and dialect diversity may be due to different levels of financial competence and, consequently, communication barriers among different dialect users. There is actual evidence linking language to financial literacy. Low levels of financial literacy in China (about 28% of adults, compared to the UK’s 67%), can make communication about loans and finances more difficult between different language users.

The results of our research are not entirely surprising. Language and dialect discrimination are longstanding problems in China. And previous studies have shown that ethnic minorities often experience economic disadvantages compared to other groups.

But our findings show how fintech platforms create a mechanism through which discrimination against minority dialects can occur. This is particularly important in a country with such great income inequality as China. Language differences can get in the way of loans flowing to those who need them most.

Athanasios Andrikopoulos, senior lecturer (lecturer) in finance, University of Hull

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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