Words Matter: The Role of Readability, Tone, and Deception Cues in Online Credit Markets

Abstract

Using debt crowdfunding data, we investigate whether borrowers’ writing style is associated with an online lender and borrower behaviors, whether the information contained in linguistic style can mitigate information asymmetry in peer-to-peer markets, and whether online investors correctly interpret the economic value of written texts. Peer-to-peer lenders bid more aggressively, are more likely to fund, and charge lower rates to online borrowers whose writing is more readable, more positive, and contains fewer deception cues. Moreover, such borrowers are less likely to default. Online investors, however, fail to fully account for the information contained in borrowers’ writing.

The role of web browsing in credit risk prediction

“…the mean difference between pairs of measurements is zero), at the 0.05 and 0.01 level of significance for model A1 and A2 regarding ROC, sensitivity and misclassification costs for the three scenarios (i.e. 15,20,25). We can conclude that model A2 and A1 show a statistically significant performance difference on the validation and test data sets.…”

“…A number of papers have assessed the characteristics of text used to describe the borrower and loan purpose by P2P applicants as predictors of PD. For example, Gao et al [15] considered the readability, tone and deceptive cues of text used. Netzer et al [16] considered two word combinations and word classes.…”

“…borrower's application characteristics, credit history, and transactional features) and is exploring "alternative" information. This includes psychometrics [4][5][6][7][8][9][10], social network features [11][12][13][14], texts characteristics [15][16][17][18], mobile phone features [19] and phone usage data [20][21][22][23][24] to predict probability of default (PD).…”

“…Although, this developing literature (see additional details in Section 2) has demonstrated that "alternative" data offers valuable information to predict loan default, research has focused on applying this type of data mainly in peer to peer (P2P) lending platforms [12][13][14][15][16][17][18]21,24,25], credit cards [11,20,22] and traditional bank loans [4,5,[7][8][9][10]. Very few papers have dealt with credit risk modelling in the online retail credit sector [19,24].…”

Consumer lending efficiency: commercial banks versus a fintech lender

“…Various studies focus on different types of alternative data, including information on friendship and social networks, online footprints, and text-based analysis. For example, see Iyer et al (2016), Hildebrandt et al (2017), Lin et al (2013), Gao et al (2018), Dorfleitner et al (2016), and Berg et al (2020. In addition to using alternative data and AI/ML to better understand a more wholistic picture of a person's financial condition, fintech lending could also allow risk pricing to be potentially more accurate.…”