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

Netzer, Oded; Lemaire, Alain; Herzenstein, Michal

doi:10.1177/0022243719852959

Cited by 238Financial Distress and Bankruptcy PredictionFinTech, Crowdfunding, Digital FinanceComputational and Text Analysis Methods

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

Netzer, Oded, Lemaire, Alain, Herzenstein, Michal·2019·Journal of Marketing Research

DOI 10.1177/0022243719852959

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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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Crossref · 10.1177/0022243719852959

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Text-mining and machine learning can automatically process and analyze loan requests to slice borrowers into good customers and bad customers.
“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
Data mining can uncover patterns that are difficult for human marketers to see.
“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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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
DOI: 10.1177/0022243719852959
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 · 2018: 2 · 2016: 1
FWCI: 61.21
Type: journal article
Language: en
Open access: closed
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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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When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications

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