Artificial intelligence and personal finance

Consumer Finance AI Standard

AI (artificial intelligence) is making consequential decisions about consumers’ financial lives, including who gets access to credit, which claims get paid, what financial products consumers are shown, and how users are advised to manage their accounts, among many others. It’s doing this at scale, largely out of sight, and with almost no accountability when it

Fi Money rolls out AI feature for users' queries on personal finance via ChatGPT, Gemini- Moneycontrol.com

Until now, users relied on generic prompts and manual inputs to use AI tools for financial advice, Fi has replaced this with a structured, consent-driven way to share real financial data, the neo-banking firm's co-founder Sumit Gwalani has said

Generative AI and Finance

Since ChatGPT's release in 2022, demand for artificial intelligence (AI)–related skills in finance has grown rapidly, as generative AI drives significant technological changes in both the financial research field and the broader economy. We show that financial occupations are highly exposed to the productivity effects of generative AI, review the literature on the impact of ChatGPT on firm value, and provide directions for future research investigating the impact of this major technology shock. Generative AI also holds great potential as a tool for finance researchers and practitioners: We review and describe innovations in research methods linked to improvements in AI tools, along with their applications. We offer a practical introduction to available tools and advice for researchers in academia and industry interested in using these tools.

The end of theory? AI and ignorance in financial markets

AI’s growing role in finance challenges traditional expectations of transparency and theoretical understanding. While machine learning (ML) models enhance financial decision-making, they remain largely agnostic to established financial theories, producing knowledge and ignorance in ways that differ from traditional models like VaR, DCF, and Black-Scholes. This essay explores the decoupling of AI models from theoretical financial knowledge and the resulting forms of ignorance. Using 22 semi-structured interviews, we investigate how ML models generate epistemic uncertainties. We focus on causal ignorance: AI systems, including those supported by XAI, fail to provide genuine causal explanations. Because understanding causation is inherently theoretical, AI-driven finance remains theory-agnostic and marked by theoretical ignorance. We explore how this ignorance differs from that of traditional models and what it implies for the role of theory in finance. Finally, we present three possible scenarios for the future of theory in finance and outline directions for further research.

5: Behavioural biases in personal finance

Behavioural economics merges psychology and economics to explore systematic deviations in financial decision-making from traditional economic models. This chapter examines key biases such as mental accounting, present bias, planning fallacy, and misunderstanding of risk, which influence spending, saving, investing, and insuring decisions. Demonstrating the interplay of cognitive biases and heuristics highlights why individuals make suboptimal choices despite financial literacy and resource capacity. Strategies like goal-setting, commitment devices, and education interventions are evaluated, focusing on their limitations and potential for addressing these biases. The chapter concludes by emphasising the need for systemic changes, such as policy-level interventions and financial regulation, to complement behavioural interventions and address structural barriers to better financial decisions. Future research directions are suggested, including tailoring interventions, exploring technology's role, and integrating systemic solutions to support sustainable financial well-being.

The ABCs of Financial Education: Experimental Evidence on Attitudes, Behavior, and Cognitive Biases

This paper uses a large-scale field experiment in India to study attitudinal, behavioral, and cognitive constraints that can stymie the link between financial education and financial outcomes. The study complements financial education with (i) financial incentives on a financial literacy test to affect participant motivation, (ii) financial goal setting to provide a psychological nudge, and (iii) personalized financial counseling to enhance the intensity of treatment. The analysis finds no impact of financial incentives on learning but significant effects of both goal setting and counseling on real financial outcomes. These results identify important complements to financial education that can bridge the gap between financial knowledge and behavior change. Data and the online appendix are available at https://doi.org/10.1287/mnsc.2017.2819 . This paper was accepted by Amit Seru, finance.

The Financial Knowledge Scale: An Application of Item Response Theory to the Assessment of Financial Literacy

Despite increasing interest in and funding for financial literacy and financial education programs in the private and public sectors, the field of financial literacy still has a major obstacle to overcome: the lack of a widely disseminated measure of financial literacy, developed through rigorous psychometric analyses. In this article, we develop such a measure, focusing specifically on financial knowledge . Using item response theory (IRT), we analyze items from three national surveys, resulting in a psychometrically sound 20‐item financial knowledge scale. By using IRT, the current analysis uses individuals' answers to inform which questions to include in the scale in the first place, rather than simply confirming relationships between these answers and other financially relevant outcomes post hoc. Widespread use of this index and the continued use of modern psychometric techniques would allow for the comparison of financial knowledge, measured consistently and reliably, across studies, populations, and programs.

Financial Literacy, Financial Education, and Downstream Financial Behaviors

Policy makers have embraced financial education as a necessary antidote to the increasing complexity of consumers' financial decisions over the last generation. We conduct a meta-analysis of the relationship of financial literacy and of financial education to financial behaviors in 168 papers covering 201 prior studies. We find that interventions to improve financial literacy explain only 0.1% of the variance in financial behaviors studied, with weaker effects in low-income samples. Like other education, financial education decays over time; even large interventions with many hours of instruction have negligible effects on behavior 20 months or more from the time of intervention. Correlational studies that measure financial literacy find stronger associations with financial behaviors. We conduct three empirical studies, and we find that the partial effects of financial literacy diminish dramatically when one controls for psychological traits that have been omitted in prior research or when one uses an instrument for financial literacy to control for omitted variables. Financial education as studied to date has serious limitations that have been masked by the apparently larger effects in correlational studies. We envisage a reduced role for financial education that is not elaborated or acted upon soon afterward. We suggest a real but narrower role for “just-in-time” financial education tied to specific behaviors it intends to help. We conclude with a discussion of the characteristics of behaviors that might affect the policy maker's mix of financial education, choice architecture, and regulation as tools to help consumer financial behavior. This paper was accepted by Uri Gneezy, behavioral economics.

FinTech payment services adoption among women: the impact of personality, financial services knowledge and artificial intelligence

Purpose. This study aims to explore the adoption of FinTech payment services among women. It assesses the phenomenon through the impact of personality traits and financial services knowledge and investigates the moderated-mediation role of the artificial intelligence.Design/methodology/approach. Adopting the technology acceptance model, this study implied a quantitative survey to collect data from the women (N = 220). The relationship among the variables were assessed through Hayes’ PROCESS moderation-mediation model, with a robustness check through structural equation modeling (SEM) that substantiates the study’s findings.Findings. The results showed that the women with high agreeableness and openness are inclined to use FinTech payment services. Moreover, high-level banking services and FinTech usage knowledge positively correlated to the usage of FinTech payment services. In addition, artificial intelligence positively influences the use of FinTech payment services among women.Practical implications. This study offers policymakers a roadmap to make FinTech payment services more accessible among women through financial services knowledge and the use of artificial intelligence. This will advance the progress toward United Nations’ Sustainable Development Goals on gender equality and innovation.Social implications. This study reinforces the prospect of women’s empowerment through FinTech, paving way to explore the prospective mechanism for a complete shift to digitalization of economy to promote economic transparency.Originality/value. To the best of the authors’ knowledge, this is amongst the first studies that investigates the role of big-five personality traits and artificial intelligence in enhancing the usage of FinTech payment services among women. The innovative nature of this study promotes financial inclusion among the female population and opens up new research avenues to promote FinTech adoption.

Household Finance

Household financial decisions are complex, interdependent, and heterogeneous, and central to the functioning of the financial system. We present an overview of the rapidly expanding literature on household finance (with some important exceptions) and suggest directions for future research. We begin with the theory and empirics of asset market participation and asset allocation over the life cycle. We then discuss household choices in insurance markets, trading behavior, decisions on retirement saving, and financial choices by retirees. We survey research on liabilities, including mortgage choice, refinancing, and default, and household behavior in unsecured credit markets, including credit cards and payday lending. We then connect the household to its social environment, including peer effects, cultural and hereditary factors, intra-household financial decision-making, financial literacy, cognition, and educational interventions. We also discuss literature on the provision and consumption of financial advice.

Consumer Reports unveils 'Consumer Finance AI Standard' a first-of-its-kind framework defining what consumers are owed from AI-powered financial products

Washington, DC – Consumer Reports today published the Consumer Finance AI Standard, a first-of-its-kind framework defining the rights, protections, and design practices consumers are owed…

Artificial Intelligence for Economic Development Conference: Roundup of 27 presentations

Is artificial intelligence the future for economic development? Earlier this month, a group of World Bank staff, academic researchers, and technology company representatives convened at a conference in San Francisco to discuss new advances in artificial intelligence. One of the takeaways for Bank staff was how AI technologies might be ...

Built on Shared Knowledge: What the World Wants from AI Wealth

AI labs and policymakers are focusing on AI dividends to address economic insecurity. We asked 1,041 people across 64 countries what they actually want from AI wealth.

Using AI and Behavioral Finance to Cope with Limited Attention and Reduce Overdraft Fees

<p><span>We test how effective a human-algorithm interaction is at stopping users from overdrawing their bank accounts. We use a randomized field experiment and

Chatbots in consumer finance | Consumer Financial Protection Bureau

Many financial institutions are using advanced technologies to deploy customer service chatbots. Poorly designed chatbots can lead to customer frustration, reduced trust, and even violations under the law.