The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
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 above 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 120,000 loan requests from Prosper, an online crowdfunding platform. Including in the predictive model 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 styles of extroverts and liars.
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“text-mining and machine learning can be used to automatically process and analyze loan requests to slice borrowers into good customers (those will pay back the loan) and bad customers (those will not) (Netzer et al. 2019)”
From A strategic framework for artificial intelligence in marketing (2021) · cites this paper as Netzer et al. 2019
In the citing paper
“Existing studies have shown how data mining can be used to uncover patterns that are difficult for human marketers to see.”
From A strategic framework for artificial intelligence in marketing (2021) · cites this paper as Netzer et al. 2019
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“The premise that the text may be indicative of deeper traits is predicated on the idea that there is a systematic relationship between the words people use and their personality traits (Hirsh and Peterson 2009), identities (McAdams 2001), and emotional states (Tausczik and Pennebaker 2010).”
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Metadata
- Authors
- Netzer, O., Lemaire, A., & Herzenstein, M.
- Affiliations
- Columbia University (US)
- University of Delaware (US)
- Corresponding author
- Netzer, Oded
- ORCID
- Netzer, Oded
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 2019-08-15
- Volume
- 56
- Issue
- 6
- Pages
- 960-980
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W3015337523
- Cited by
- 238
- References
- 76
- Citations by year
- 2026: 17 · 2025: 43 · 2024: 39 · 2023: 40 · 2022: 32 · 2021: 41 · 2020: 17 · 2019: 6
- FWCI
- 61.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Marketing Research (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Social Sciences › Business, Management and Accounting › Management Information Systems › FinTech, Crowdfunding, Digital Finance
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- loan default prediction, extraversion, dishonesty, financial hardship, crowdfunding, PROSPER, text mining, writing style
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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