Can Giving Straightforward, Personally Relevant Information Improve Financial Decision-Making?

Abstract This paper investigates the impact of providing targeted financial information in the form of a short narrative at the point of decision-making (e.g., buying a house) on household financial choices. We hypothesize that this approach could lead to more prudent decisions and long-term financial well-being for households. Our approach has several novel features. Unlike broad financial literacy programmes, which can be costly and of limited value, this study targets a concrete financial decision (home buying) with relevant information customized to the decision. The narrative considers the individual’s hypothetical financial situation to make it easier to act on by increasing the relevance of the information. We provide the information just before the decision, hoping to maximize its effectiveness at bridging the gap between financial literacy and behaviour. Our findings are based on respondents randomly assigned to take a version of our survey that either gives loan terms with financial information (Enhanced version) or without (Basic condition). Results indicate that the Enhanced condition is strongly positively related to sound financial decisions. Risk attitudes, financial literacy, financial behaviours and previous experience with owning a home and taking out a mortgage all have significant relationships with decisions made. Among socio-economic variables, while income and college education are significant when no education is provided, these variables become nonsignificant with additional targeted information. Our results appear to support the idea that this targeted approach has the potential to improve households’ financial well-being and provide parity with regard to decision-making across income and education levels. Banks typically do not provide a broader financial perspective on how households might make this critical decision about taking out a mortgage, which would keep their long-term financial well-being in mind. Moreover, while the law might require a real estate attorney to represent the homebuyer at the closing, there is no requirement for the homebuyer to have financial representation. Given the broader macroeconomic impact of household financial decisions that link them to crises and the stability of the financial sector, banks could proactively offer tailored financial information to borrowers, contributing to economic stability and aligning the interests of both borrower households and the banking sector. We suggest that AI could play a supportive role in the scalable and cost-effective implementation of the information remedy we propose and present some experiments with ChatGPT 4o.

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Publication Details

Journal
Journal of Consumer Policy
Published
2025-08-04
DOI
https://doi.org/10.1007/s10603-025-09596-z
Primary Topic
Financial Literacy, Pension, Retirement Analysis
Type
article
Field-Weighted Citation Impact
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article

Can Giving Straightforward, Personally Relevant Information Improve Financial Decision-Making?

Purba Mukerji, Ann Sloan Devlin
Journal of Consumer Policy
Financial Literacy, Pension, Retirement Analysis
article

Can Giving Straightforward, Personally Relevant Information Improve Financial Decision-Making?

Purba Mukerji, Ann Sloan Devlin
article en

Abstract

Abstract This paper investigates the impact of providing targeted financial information in the form of a short narrative at the point of decision-making (e.g., buying a house) on household financial choices. We hypothesize that this approach could lead to more prudent decisions and long-term financial well-being for households. Our approach has several novel features. Unlike broad financial literacy programmes, which can be costly and of limited value, this study targets a concrete financial decision (home buying) with relevant information customized to the decision. The narrative considers the individual’s hypothetical financial situation to make it easier to act on by increasing the relevance of the information. We provide the information just before the decision, hoping to maximize its effectiveness at bridging the gap between financial literacy and behaviour. Our findings are based on respondents randomly assigned to take a version of our survey that either gives loan terms with financial information (Enhanced version) or without (Basic condition). Results indicate that the Enhanced condition is strongly positively related to sound financial decisions. Risk attitudes, financial literacy, financial behaviours and previous experience with owning a home and taking out a mortgage all have significant relationships with decisions made. Among socio-economic variables, while income and college education are significant when no education is provided, these variables become nonsignificant with additional targeted information. Our results appear to support the idea that this targeted approach has the potential to improve households’ financial well-being and provide parity with regard to decision-making across income and education levels. Banks typically do not provide a broader financial perspective on how households might make this critical decision about taking out a mortgage, which would keep their long-term financial well-being in mind. Moreover, while the law might require a real estate attorney to represent the homebuyer at the closing, there is no requirement for the homebuyer to have financial representation. Given the broader macroeconomic impact of household financial decisions that link them to crises and the stability of the financial sector, banks could proactively offer tailored financial information to borrowers, contributing to economic stability and aligning the interests of both borrower households and the banking sector. We suggest that AI could play a supportive role in the scalable and cost-effective implementation of the information remedy we propose and present some experiments with ChatGPT 4o.

Journal of Consumer PolicyVol. 48(4)
Connecticut College (US)
Openalex Percentile: Top 71%
Financial Literacy, Pension, Retirement Analysis
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