Spelling Errors and Non-Standard Language in Peer-to-Peer Loan Applications and the Borrower’s Probability of Default
Spelling Errors and Non-Standard Language in Peer-to-Peer Loan Applications and the Borrower’s Probability of Default
复制标题
P2P贷款申请中的拼写错误和非标准语言以及借款人的违约概率
DOI：
10.2139/ssrn.3609834
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Jatinder Singh
中科院分区：
文献类型：
--
作者：
M. S. Lee;Jatinder Singh
当P2P贷款人评估每个贷款申请的潜在风险时，他们可能依赖于定性信息的主观判断。学者们发现，贷款批准率与借款人的性格特征、社会资本和外表有关。然而，借款人的语言和违约概率之间的联系尚未得到考虑。在本文中，我们表明，有统计上显着的语言差异，在自由文本贷款俱乐部贷款的描述之间的违约和全额支付。通过对非标准语言和拼写错误的新工程特征，使用自然语言处理技术和运行多元逻辑回归分析，我们发现，在控制借款人的收入和贷款金额时，俚语，缩写和拼写错误的使用都与较高的违约可能性相关。然而，无论是正字法还是语音错误，以及错误的严重性都不会影响默认的概率。最后，我们讨论了潜在的歧视性偏见的伦理影响，考虑到拼写错误和残疾状况（如阅读障碍），民族血统（即英语语言熟悉度）和个性特征（粗心）之间的关联，为未来P2P贷款中的偏见工作奠定了基础，以及其他涉及以申请人为导向的风险评估的情况。
As peer-to-peer (P2P) lenders evaluate the potential risk of each loan application, they may rely on subjective judgement given qualitative information. Academics have found loan approval rates to be associated with the borrower's personality traits, social capital, and appearances. However, the association between a borrower's language and probability of default has yet be considered. In this paper, we show that there are statistically significant linguistic differences in the free-text Lending Club loan descriptions between those that default and those that are fully paid. By newly engineering features on non-standard language and spelling errors, using natural language processing techniques and running multivariate logistic regression analyses, we find that the usage of slang words, short-hand abbreviations, and spelling errors are all associated with a higher likelihood of default when controlling for the borrower's income and loan amount. However, whether the errors were orthographic or phonological and the egregiousness of the error do not affect the probability of default. Finally, we discuss the ethical implications of potential discriminatory bias given the association between poor spelling and disability status (e.g. dyslexia), national origin (i.e. English language familiarity), and personality traits (carelessness), laying the foundation for future work on bias in P2P lending, and other scenarios involving applicant-oriented risk assessments.