Why trust in AI matters more than financial skills in Fintech use

Trust in artificial intelligence, rather than financial or digital literacy, is the strongest factor shaping consumer confidence in fintech platforms, according to new research published in the Journal of Risk and Financial Management.

The study, Trust in Financial Technology: The Role of Financial Literacy, Digital Financial Literacy, Technological Literacy, and Trust in Artificial Intelligence, examines how different forms of literacy interact with trust to shape consumer confidence in fintech platforms, revealing a disconnect that could carry significant implications for financial risk, regulation, and consumer protection.

Why financial knowledge does not translate into AI confidence

For decades, financial literacy has been treated as a cornerstone of consumer protection policy. The assumption is straightforward: individuals who understand interest rates, investment risk, and financial products should be better equipped to make informed decisions and avoid harm. As financial services migrate online, this framework has expanded to include digital financial literacy and broader technological competence.

The study finds that these forms of literacy remain closely linked to one another. Participants with higher financial literacy also tend to demonstrate stronger digital financial skills and greater comfort with technology. However, when it comes to trusting fintech platforms that rely on artificial intelligence, these competencies lose their predictive power.

According to the analysis, none of the three literacy measures show a statistically significant relationship with trust in fintech services. Instead, trust in artificial intelligence itself emerges as the strongest and most consistent predictor of whether users trust AI-powered financial tools. In other words, consumers who believe AI systems are reliable, fair, and beneficial are more likely to trust fintech platforms regardless of their actual understanding of finance or technology.

This finding suggests that users may be outsourcing judgment to AI systems without fully grasping how those systems operate or what risks they carry. In the context of financial decision-making, this dynamic raises concerns about overreliance, misplaced confidence, and asymmetric risk exposure.

The authors note that many fintech platforms deliberately emphasize ease of use and frictionless interaction. While this design lowers barriers to entry, it can also obscure the underlying complexity of automated decision-making. Users may interpret smooth interfaces and confident outputs as signals of competence, reinforcing trust even in the absence of understanding.

The result is a structural vulnerability in the fintech ecosystem. Consumers may accept AI-generated recommendations not because they have evaluated them critically, but because they trust the technology as an authority. This dynamic shifts the burden of responsibility away from users and onto system designers, firms, and regulators.

Role of AI trust in shaping financial behavior

Trust in artificial intelligence functions differently from trust in human advisors or traditional institutions. The study suggests that AI trust is shaped less by knowledge and more by perception, experience, and cultural narratives around technology. Popular discourse often frames AI as objective, data-driven, and free from human bias, attributes that can enhance perceived legitimacy even when they are only partially accurate.

In fintech contexts, this perception can amplify the influence of automated systems. Robo-advisors that recommend portfolios, credit algorithms that assess loan eligibility, and chatbots that provide financial guidance all operate within probabilistic models that reflect underlying data and design choices. Users who trust AI implicitly may be less likely to question outputs, seek second opinions, or recognize limitations.

The study's findings indicate that this trust is not meaningfully moderated by literacy. Even users with strong financial and technological backgrounds appear just as likely to defer to AI systems if they hold positive beliefs about artificial intelligence. This challenges the idea that education alone can safeguard users against potential harms associated with automated finance.

At scale, widespread trust in AI-driven finance can accelerate adoption and innovation, but it can also magnify systemic risk. If large populations rely on similar models and recommendations, errors or biases embedded in those systems may propagate rapidly across markets.

The authors highlight that trust without understanding creates an uneven risk landscape. Fintech firms benefit from increased user confidence and engagement, while consumers may face consequences they are ill-equipped to anticipate. This imbalance underscores the importance of transparency, accountability, and oversight in AI-driven financial services.

Regulatory and educational gaps in the fintech era

Traditional disclosure-based approaches assume that users can process information rationally and apply it to their decisions. The findings challenge this assumption by showing that trust operates independently of knowledge.

If trust in AI drives adoption more than literacy, then policies focused solely on improving financial education may fail to address core risks. While education remains valuable, it may not be sufficient to counteract the persuasive power of automated systems that present themselves as authoritative and efficient.

The authors argue that fintech regulation must account for behavioral dynamics unique to AI. This includes recognizing that users may place undue trust in systems they do not understand and that interface design, branding, and system framing can strongly influence perception. Safeguards such as clearer explanations of AI limitations, standardized risk disclosures for automated advice, and stronger accountability for algorithmic outcomes may be necessary to rebalance responsibility.

The study also raises questions about how trust is cultivated. Trust in AI is not inherently negative, but when it operates without grounding in understanding, it can undermine informed consent. Regulators may need to consider how trust signals are communicated and whether certain claims or design choices mislead users about system capabilities.

Educational institutions face parallel challenges. Financial literacy programs may need to evolve beyond traditional concepts to address how AI systems function, where their limitations lie, and how to engage with them critically. However, the study suggests that even improved literacy may not directly influence trust, highlighting the need for complementary approaches.

If trust in AI varies across demographic groups due to cultural, social, or experiential factors, adoption patterns may reinforce existing inequalities. Those who trust AI blindly may face greater exposure to automated risks, while those who distrust it may miss out on potential benefits.