Author
Alain Lemaire
Bio: Alain Lemaire is an academic researcher from Columbia University. The author has contributed to research in topics: Loan & Default. The author has an hindex of 5, co-authored 5 publications receiving 156 citations.
Topics: Loan, Default, Cross-collateralization, Bridge loan, Non-conforming loan
Papers
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TL;DR: In this paper, the authors present empirical evidence that borrowers leave traces of their intentions, circumstances, and personality traits in the text they write when applying for a loan when they apply for a credit card.
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. T...
120 citations
TL;DR: In this article, a quantitative approach for describing entertainment products, in a way that allows for improving the predictive performance of consumer choice models for these products, has been proposed to improve the prediction performance of these models.
Abstract: The authors propose a quantitative approach for describing entertainment products, in a way that allows for improving the predictive performance of consumer choice models for these products. Their ...
79 citations
TL;DR: This research applied an ensemble machine-learning technique (random-forest modeling) to a data set combining two million spending records from bank accounts with survey responses from the account holders, and found that the predictive accuracies were relatively stable across socioeconomic groups and over time.
Abstract: The automatic assessment of psychological traits from digital footprints allows researchers to study psychological traits at unprecedented scale and in settings of high ecological validity. In this...
49 citations
Journal Article•
TL;DR: In this paper, the authors present empirical evidence that borrowers leave traces of their intentions, circumstances, and personality traits in the text they write when applying for a loan when they apply for a credit card.
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. T...
27 citations
TL;DR: The authors used text mining and machine learning tools to automatically process and analyze the raw text in over 18,000 loan requests from Prosper.com, an online crowdfunding platform, and found that loan requests written by defaulting borrowers are more likely to include words related to their family, mentions of god, short-term focused words, the borrower's financial and general hardship, and pleading lenders for help.
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 over 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 18,000 loan requests from Prosper.com, an online crowdfunding platform. The authors find that loan requests written by defaulting borrowers are more likely to include words related to their family, mentions of god, short-term focused words, the borrower’s financial and general hardship, and pleading lenders for help. The authors further observe that defaulting loan requests are often written in a manner consistent with the writing style of extroverts and liars.
6 citations
Cited by
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TL;DR: The authors found that words are part of almost every marketplace interaction, including online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data.
Abstract: Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. But...
321 citations
TL;DR: A three-stage framework for strategic marketing planning, incorporating multiple artificial intelligence (AI) benefits: mechanical AI for automating repetitive marketing functions and activities, thinking AI for processing data to arrive at decisions, and feeling AI for analyzing interactions and human emotions is developed.
Abstract: The authors develop a three-stage framework for strategic marketing planning, incorporating multiple artificial intelligence (AI) benefits: mechanical AI for automating repetitive marketing functions and activities, thinking AI for processing data to arrive at decisions, and feeling AI for analyzing interactions and human emotions. This framework lays out the ways that AI can be used for marketing research, strategy (segmentation, targeting, and positioning, STP), and actions. At the marketing research stage, mechanical AI can be used for data collection, thinking AI for market analysis, and feeling AI for customer understanding. At the marketing strategy (STP) stage, mechanical AI can be used for segmentation (segment recognition), thinking AI for targeting (segment recommendation), and feeling AI for positioning (segment resonance). At the marketing action stage, mechanical AI can be used for standardization, thinking AI for personalization, and feeling AI for relationalization. We apply this framework to various areas of marketing, organized by marketing 4Ps/4Cs, to illustrate the strategic use of AI.
254 citations
TL;DR: Across all tasks the authors study, either random forest or naive Bayes (NB) performs best in terms of correctly uncovering human intuition, and the results suggest that marketing research can benefit from considering these alternatives.
221 citations
Posted Content•
TL;DR: In this article, the information content of the digital footprint was analyzed for predicting consumer default, and it was shown that even simple, easily accessible variables from the digital footprints equal or exceed the information contents of credit bureau scores.
Abstract: We analyze the information content of the digital footprint – information that people leave online simply by accessing or registering on a website – for predicting consumer default. Using more than 250,000 observations, we show that even simple, easily accessible variables from the digital footprint equal or exceed the information content of credit bureau scores. Furthermore, the discriminatory power for unscorable customers is very similar to that of scorable customers. Our results have potentially wide implications for financial intermediaries’ business models, for access to credit for the unbanked, and for the behavior of consumers, firms, and regulators in the digital sphere.
194 citations
TL;DR: It is argued that complex psychological phenomena are most likely determined by a multitude of causes and that any individual cause is likely to have only a small effect, which can have substantial consequences, especially when considered at scale and over time.
Abstract: We draw on genetics research to argue that complex psychological phenomena are most likely determined by a multitude of causes and that any individual cause is likely to have only a small effect. Building on this, we highlight the dangers of a publication culture that continues to demand large effects. First, it rewards inflated effects that are unlikely to be real and encourages practices likely to yield such effects. Second, it overlooks the small effects that are most likely to be real, hindering attempts to identify and understand the actual determinants of complex psychological phenomena. We then explain the theoretical and practical relevance of small effects, which can have substantial consequences, especially when considered at scale and over time. Finally, we suggest ways in which scholars can harness these insights to advance research and practices in psychology (i.e., leveraging the power of big data, machine learning, and crowdsourcing science; promoting rigorous preregistration, including prespecifying the smallest effect size of interest; contextualizing effects; changing cultural norms to reward accurate and meaningful effects rather than exaggerated and unreliable effects). Only once small effects are accepted as the norm, rather than the exception, can a reliable and reproducible cumulative psychological science be built.
157 citations