When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications

Abstract

The authors present empirical evidence that borrowers, consciously or not, leave traces of their intentions, circumstances, and personality traits in the text they write when applying for a loan. This textual information has a substantial and significant ability to predict whether borrowers will pay back the loan over and beyond the financial and demographic variables commonly used in models predicting default. The authors use text-mining and machine-learning tools to automatically process and analyze the raw text in over 120 thousand loan requests from Prosper.com, an online crowdfunding platform. Including the textual information in the loan significantly helps predict loan default and can have substantial financial implications. The authors find that loan requests written by defaulting borrowers are more likely to include words related to their family, mentions of god, the borrower’s financial and general hardship, pleading lenders for help, and short-term focused words. The authors further observe that defaulting loan requests are written in a manner consistent with the writing style of extroverts and liars.

Keywords: loan default, text mining, consumer finance, machine learning