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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
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“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
Cites
(2012) · Cited by 8,009
Econometric policy evaluation: A critique
Lucas, R. E. (1976) · Carnegie-Rochester Conference Series on Public Policy · doi:10.1016/s0167-2231(76)80003-6 · Cited by 6,110
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
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
Barber, B. M., & Odean, T. (2001) · The Quarterly Journal of Economics · doi:10.1162/003355301556400 · Cited by 5,276
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
Unrealistic optimism about future life events
Weinstein, N. D. (1980) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.39.5.806 · Cited by 4,999
What Makes Online Content Viral?
Berger, J., & Milkman, K. L. (2012) · Journal of Marketing Research · doi:10.1509/jmr.10.0353 · Cited by 3,178
Private traits and attributes are predictable from digital records of human behavior
Kosinski, M., Stillwell, D., & Graepel, T. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1218772110 · Cited by 2,829
The Psychology of Life Stories
McAdams, D. P. (2001) · Review of General Psychology · doi:10.1037/1089-2680.5.2.100 · Cited by 2,808 · cited 1×
“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).”
BackgroundPredicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment
Tumasjan, A., Sprenger, T. O., Sandner, P., & Welpe, I. M. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14009 · Cited by 2,647
DePaulo, B. M., Lindsay, J. J., Malone, B. E., Muhlenbruck, L., Charlton, K., & Cooper, H. (2003) · Psychological Bulletin · doi:10.1037/0033-2909.129.1.74 · Cited by 2,479
Financial Literacy, Financial Education, and Downstream Financial Behaviors
Fernandes, D., Lynch, J. G., & Netemeyer, R. G. (2014) · Management Science · doi:10.1287/mnsc.2013.1849 · Cited by 2,152
Linguistic styles: language use as an individual difference
Pennebaker, J. W., & King, L. A. (1999) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.77.6.1296 · Cited by 1,961
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073791 · Cited by 1,700
Some Consequences of Having Too Little
Shah, A. K., Mullainathan, S., & Shafir, E. (2012) · Science · doi:10.1126/science.1222426 · Cited by 1,567
LDAvis: A method for visualizing and interpreting topics
Sievert, C., & Shirley, K. E. (2014) · doi:10.3115/v1/w14-3110 · Cited by 1,512
Lying Words: Predicting Deception from Linguistic Styles
Newman, M. L., Pennebaker, J. W., Berry, D. S., & Richards, J. M. (2003) · Personality and Social Psychology Bulletin · doi:10.1177/0146167203029005010 · Cited by 1,489
Distance and Private Information in Lending
Agarwal, S. D., & Hauswald, R. B. H. (2010) · Review of Financial Studies · doi:10.1093/rfs/hhq001 · Cited by 1,370
Online Learning for Latent Dirichlet Allocation
Hoffman, M. D., Blei, D. M., & Bach, F. (2010) · Cited by 1,365
Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook
Lee, D., Hosanagar, K., & Nair, H. S. (2018) · Management Science · doi:10.1287/mnsc.2017.2902 · Cited by 1,069
Farrell, J. P., & Rabin, M. (1996) · The Journal of Economic Perspectives · doi:10.1257/jep.10.3.103 · Cited by 1,057
Using linguistic cues for the automatic recognition of personality in conversation and text
Mairesse, F., Walker, M. A., Mehl, M. R., & Moore, R. K. (2007) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2349 · Cited by 1,046
Linguistic predictors of adaptive bereavement
Pennebaker, J. W., Mayne, T. J., & Francis, M. E. (1997) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.72.4.863 · Cited by 970
Martens, M. L., Jennings, P. D., & Jennings, P. D. (2007) · Academy of Management Journal · doi:10.5465/amj.2007.27169488 · Cited by 889
Netemeyer, R. G., Warmath, D., Fernandes, D., & Lynch, J. G. (2018) · Journal of Consumer Research · doi:10.1093/jcr/ucx109 · Cited by 842
Pennebaker, J. W. (2011) · New Scientist · doi:10.1016/s0262-4079(11)62167-2 · Cited by 780
Mehl, M. R., Gosling, S. D., & Pennebaker, J. W. (2006) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.90.5.862 · Cited by 764
Mine Your Own Business: Market-Structure Surveillance Through Text Mining
Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012) · Marketing Science · doi:10.1287/mksc.1120.0713 · Cited by 763
Self-citationPersonality and domain‐specific risk taking
Nicholson, N., Soane, E., Fenton‐O'Creevy, M., & Willman, P. (2005) · Journal of Risk Research · doi:10.1080/1366987032000123856 · Cited by 761
Mayer, C., Pence, K., & Sherlund, S. M. (2009) · Journal of Economic Perspectives · doi:10.1257/jep.23.1.27 · Cited by 720
Scaling to very very large corpora for natural language disambiguation
Banko, M., & Brill, E. (2001) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1073012.1073017 · Cited by 698
Racial Segregation and the American Foreclosure Crisis
Rugh, J. S., & Massey, D. S. (2010) · American Sociological Review · doi:10.1177/0003122410380868 · Cited by 686
Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers
Yarkoni, T. (2010) · Journal of Research in Personality · doi:10.1016/j.jrp.2010.04.001 · Cited by 637
Automated Text Analysis for Consumer Research
Humphreys, A., & Wang, R. J.-H. (2018) · Journal of Consumer Research · doi:10.1093/jcr/ucx104 · Cited by 636
What's in a Picture?: Evidence of Discrimination from Prosper.com
Pope, D. G., & Sydnor, J. (2010) · The Journal of Human Resources · doi:10.3368/jhr.46.1.53 · Cited by 635
On Lying and Being Lied To: A Linguistic Analysis of Deception in Computer-Mediated Communication
Hancock, J. T., Curry, L. E., Goorha, S., & Woodworth, M. (2007) · Discourse Processes · doi:10.1080/01638530701739181 · Cited by 549
Words of wisdom: Language use over the life span
Pennebaker, J. W., & Stone, L. D. (2003) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.85.2.291 · Cited by 518
Patterns of Natural Language Use: Disclosure, Personality, and Social Integration
Pennebaker, J. W., & Graybeal, A. (2001) · Current Directions in Psychological Science · doi:10.1111/1467-8721.00123 · Cited by 501
Personality Factors, Money Attitudes, Financial Knowledge, and Credit‐Card Debt in College Students1
Norvilitis, J. M., Merwin, M. M., Osberg, T. M., Roehling, P. V., Young, P., & Kamas, M. M. (2006) · Journal of Applied Social Psychology · doi:10.1111/j.0021-9029.2006.00065.x · Cited by 484
Herzenstein, M., Sonenshein, S. B., & Dholakia, U. M. (2011) · Journal of Marketing Research · doi:10.1509/jmkr.48.spl.s138 · Cited by 395
Self-citationTowards a General Rule for Identifying Deceptive Opinion Spam
Li, J., Ott, M., Cardie, C., & Hovy, E. H. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-1147 · Cited by 383
Oppenheimer, D. M. (2005) · Applied Cognitive Psychology · doi:10.1002/acp.1178 · Cited by 368
What Lies Beneath: The Linguistic Traces of Deception in Online Dating Profiles
Toma, C. L., & Hancock, J. T. (2012) · Journal of Communication · doi:10.1111/j.1460-2466.2011.01619.x · Cited by 335
Personality and language use in self-narratives
Hirsh, J. B., & Peterson, J. B. (2009) · Journal of Research in Personality · doi:10.1016/j.jrp.2009.01.006 · Cited by 312
Estimating the prevalence of deception in online review communities
Ott, M., Cardie, C., & Hancock, J. T. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187836.2187864 · Cited by 304
Adverse Incentives in Crowdfunding
Hildebrand, T., Puri, M., & Rocholl, J. (2016) · Management Science · doi:10.1287/mnsc.2015.2339 · Cited by 302
Looking Good and Lying to Do It: Deception as an Impression Management Strategy in Job Interviews
Weiss, B., & Feldman, R. S. (2006) · Journal of Applied Social Psychology · doi:10.1111/j.0021-9029.2006.00055.x · Cited by 249
Lynch, J. G., Netemeyer, R. G., Spiller, S. A., & Zammit, A. (2009) · Journal of Consumer Research · doi:10.1086/649907 · Cited by 249
Bond, G. D., & Lee, A. Y. (2005) · Applied Cognitive Psychology · doi:10.1002/acp.1087 · Cited by 195
The role of personality in household saving and borrowing behaviour
Nyhus, E. K., & Webley, P. (2001) · European Journal of Personality · doi:10.1002/per.422 · Cited by 186
Using Big Data as a window into consumers’ psychology
Matz, S., & Netzer, O. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.05.009 · Cited by 182
Self-citationPayday Loans and Credit Cards: New Liquidity and Credit Scoring Puzzles?
Agarwal, S. D., Skiba, P. M., & Tobacman, J. (2009) · American Economic Review · doi:10.1257/aer.99.2.412 · Cited by 176
When to Begin? Regulatory Focus and Initiating Goal Pursuit
Freitas, A. L., Liberman, N., Salovey, P., & Higgins, E. T. (2002) · Personality and Social Psychology Bulletin · doi:10.1177/0146167202281011 · Cited by 170
The role of personality, age, and gender in tweeting about mental illness
Preoțiuc-Pietro, D., Eichstaedt, J. C., Park, G., Sap, M., Smith, L. K., Tobolsky, V. A., Schwartz, H. A., & Ungar, L. (2015) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.3115/v1/w15-1203 · Cited by 169
Credit Scoring with Social Network Data
Wei, Y. “., Yıldırım, P., den Bulte, C. V., & Dellarocas, C. N. (2014) · Marketing Science · doi:10.1287/mksc.2015.0949 · Cited by 165
Assessing the Lucas Critique in Monetary Policy Models
Rudebusch, G. D. (2005) · Journal of money credit and banking · doi:10.1353/mcb.2005.0024 · Cited by 127
Narrative framing of consumer sentiment in online restaurant reviews
Jurafsky, D., Chahuneau, V., Routledge, B., & Smith, N. A. (2014) · First Monday · doi:10.5210/fm.v19i4.4944 · Cited by 125
Feldman, G., Lian, H., Kosiński, M., & Stillwell, D. J. (2017) · Social Psychological and Personality Science · doi:10.1177/1948550616681055 · Cited by 119
Rustichini, A., DeYoung, C. G., Anderson, J. E., & Burks, S. V. (2016) · Journal of Behavioral and Experimental Economics · doi:10.1016/j.socec.2016.04.019 · Cited by 116
Pasupathi, M. (2007) · Memory · doi:10.1080/09658210701256456 · Cited by 112
Why Did So Many Subprime Borrowers Default During the Crisis: Loose Credit or Plummeting Prices?Preprint
Palmer, C. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2665762 · Cited by 102
A New Paradigm for Practical Application of Behavioral Finance
Pompian, M. M., & Longo, J. M. (2004) · The Journal of Wealth Management · doi:10.3905/jwm.2004.434561 · Cited by 100
Knowing When to Spend: Unintended Financial Consequences of Earmarking to Encourage Savings
Sussman, A. B., & O’Brien, R. (2015) · Journal of Marketing Research · doi:10.1509/jmr.14.0455 · Cited by 91
The Ant and the Grasshopper: Understanding Personal Saving Orientation of Consumers
Dholakia, U. M., Tam, L., Yoon, S., & Wong, N. (2016) · Journal of Consumer Research · doi:10.1093/jcr/ucw004 · Cited by 91
Anticipating Divine Protection? Reminders of God Can Increase Nonmoral Risk Taking
Kupor, D. M., Laurin, K., & Levav, J. (2015) · Psychological Science · doi:10.1177/0956797614563108 · Cited by 82
How accounts shape lending decisions through fostering perceived trustworthiness
Sonenshein, S. B., Herzenstein, M., & Dholakia, U. M. (2010) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2010.11.009 · Cited by 79
Self-citationArya, S., Eckel, C. C., & Colin, W. (2011) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2011.05.005 · Cited by 78
Credit Scoring: Statistical Issues and Evidence from Credit‐Bureau Files
Avery, R. B., Bostic, R. W., Calem, P. S., & Canner, G. B. (2000) · Real Estate Economics · doi:10.1111/1540-6229.00811 · Cited by 71
Marketing Models and the Lucas Critique
van Heerde, H. J., Dekimpe, M. G., & Putsis, W. P. (2005) · Journal of Marketing Research · doi:10.1509/jmkr.42.1.15.56888 · Cited by 69
A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics
Whalen, S., & Pandey, G. S. (2013) · Proceedings · doi:10.1109/icdm.2013.21 · Cited by 66
Balabanis, G. I. (2002) · Journal of Consumer Behaviour · doi:10.1002/cb.86 · Cited by 66
Discrimination in Lending Markets
Harkness, S. K. (2016) · Social Psychology Quarterly · doi:10.1177/0190272515623459 · Cited by 49
Credit Scoring and Loan Default
Sengupta, R., & Bhardwaj, G. (2015) · International Review of Finance · doi:10.1111/irfi.12048 · Cited by 26
FROM MEASURE TO CONSTRUCT: AN INVESTIGATION OF THE NOMOLOGICAL NETWORK OF CREDIT SCORES
Bernerth, J. B., Taylor, S. G., & Walker, H. J. (2011) · Academy of Management Proceedings · doi:10.5465/ambpp.2011.65869384 · Cited by 1
Tibshirani (1997) · doi:10.1002/(sici)1097-0258(19970228)16:4
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.
- Bergstra James, 2012, Journal of Machine Learning Research, 13, 281
- Brown Meta, 2015, Federal Reserve Bank
- Gross Jacob P.K., 2009, Journal of Student Financial Aid, 39, 19
- McLaughlin G. Harry, 1969, Journal of Reading, 12, 639
- 10.1353/jhr.2011.0025 · DOI
- Ventrella Jeffrey, 2011, Virtual Body Language
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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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