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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In the citing paper
“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
Citations
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The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods
Tausczik, Y. R., & Pennebaker, J. W. (2010) · Journal of Language and Social Psychology · doi:10.1177/0261927x09351676 · Cited by 5,774
Boys will be Boys: Gender, Overconfidence, and Common Stock Investment
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Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
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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).”
Background
Research works printed but not linked: 7
- Anderson Jon, Burks Stephen, DeYoung Colin, Rustichini Aldo (2011), “Toward the Integration of Personality Theory and Decision Theory in the Explanation of Economic Behavior,” presented at the IZA Workshop: Cognitive and Non-Cognitive Skills, Bonn, Germany (January 25–27), https://pdfs.semanticscholar.org/db58/6683a98173dba870d5e8fc93920fa867a1d2.pdf.
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Cited by
Sorted by citations
A strategic framework for artificial intelligence in marketing
Huang, M.-H., & Rust, R. T. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00749-9 · Cited by 2,059
“…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), automated text analysis and correspondence analysis can be used for psychographic consumer segmentation in the art market (Pitt…”
MethodOn the Rise of FinTechs: Credit Scoring Using Digital Footprints
Berg, T., Burg, V., Gombović, A., & Puri, M. (2020) · The Review of Financial Studies · doi:10.1093/rfs/hhz099 · Cited by 919
Artificial intelligence in marketing: Systematic review and future research direction
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2020.100002 · Cited by 905
Uniting the Tribes: Using Text for Marketing Insight
Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., & Schweidel, D. A. (2020) · Journal of Marketing · doi:10.1177/0022242919873106 · Cited by 742
Self-citationBrave New World? On AI and the Management of Customer Relationships
Libai, B., Bart, Y., Gensler, S., Hofacker, C. F., Kaplan, A., Kötterheinrich, K., & Kroll, E. B. (2020) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2020.04.002 · Cited by 358
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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