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For this Sunday editorial, we at FinTech Weekly would like to share with you an important piece of research brought to our attention by Commonwealth, a national nonprofit committed to building financial security and opportunity for financially vulnerable people through innovation and partnerships.
Their Emerging Tech For All (ETA) initiative aims to uncover how AI can be designed to truly serve low and moderate-income (LMI) households, a demographic often overlooked in technological advancements.
Artificial intelligence, particularly conversational AI, has been widely recognized for its potential to reshape financial services. The real challenge lies in whether these systems can provide practical, reliable support for LMI households rather than simply serving higher-income, tech-savvy users. The problem is not just about access but about designing tools that genuinely address the needs of those who have been left behind by technological progress.
Understanding the Stakes
Disparities in technological advancement and accessibility have persisted throughout history. AI systems, particularly those aimed at financial services, frequently target the needs of higher-income users. Tools designed to enhance convenience for one demographic often fail to translate effectively to another.
The data from Commonwealth’s research presents this divide clearly. While nearly 30% of U.S. adults reported using AI in 2023, only a fraction of that group belonged to LMI households. This gap reflects a deeper issue: AI tools are often developed without fully considering the specific needs and concerns of LMI individuals. The systems may offer sophisticated capabilities but lack relevance where it matters most.
The enthusiasm surrounding generative AI models like ChatGPT, introduced in 2022, illustrated the potential of these systems to provide scalable, personalized financial guidance. However, the tools primarily target users with financial resources or technological fluency. The excitement of developers and companies has not yet translated into practical, effective solutions for LMI households.
Building Trust and Addressing Real Concerns
Trust remains a critical factor in whether AI systems will be embraced by LMI populations. For many users, AI systems are not just technological tools; they are services that must demonstrate real value while ensuring privacy and security. Concerns about data security and privacy remain significant obstacles to adoption.
According to Commonwealth’s research, 63% of users are concerned about AI security, and 53% worry about privacy. These concerns reflect a broader skepticism toward technologies that feel invasive rather than supportive. AI tools often promise convenience but fail to convey how they will protect users’ personal information.
Interestingly, many LMI users do not perceive chatbots as “AI.” They see them as tools designed to perform specific tasks like paying bills, checking balances, or resolving straightforward issues. This perception offers valuable insight for developers: people are not necessarily interested in AI as a concept; they are interested in practical tools that address their needs without unnecessary complexity.
Trustworthiness is established through consistency and clarity. AI systems must provide reliable, transparent services that respect users’ privacy and empower them without appearing exploitative. Effective tools prove their value through practical, measurable outcomes.
What LMI Users Actually Want
Commonwealth’s research offers important insights into what LMI households are genuinely looking for. They seek tools that provide practical assistance for real-world financial challenges rather than sophisticated technology for its own sake.
The most immediate areas where AI can make a difference include budgeting, credit-building, and payment processing. These areas are not glamorous, but they are essential for achieving financial stability. Commonwealth’s field tests indicate that users prefer AI tools offering straightforward, judgment-free guidance focused on usability and accessibility.
Financial insecurity often comes with shame and anxiety, which makes seeking help difficult. AI systems designed to offer guidance without making users feel scrutinized or judged are likely to gain greater acceptance. Building practicality and empathy into the very structure of these tools is essential.
The Potential and Limitations of Generative AI
Generative AI offers substantial potential for enhancing financial services. Its ability to provide personalized guidance and real-time insights could reshape how LMI households engage with financial systems. However, practical concerns about accuracy, privacy, and complexity remain significant obstacles.
For many LMI users, generative AI still appears unreliable. The technology’s ability to turn chatbots from simple question-answering tools into comprehensive financial guides holds promise but requires consistent, reliable performance. Systems designed to understand individual circumstances and provide tailored advice will likely have the greatest impact.
Where AI Could Make a Difference: Public Benefits and Workplace Tools
Commonwealth’s research also highlights areas where AI could significantly improve financial inclusion. Public benefits systems remain complex and difficult to navigate. Every year, an estimated $140 billion in government aid goes unclaimed because of bureaucratic barriers. AI tools capable of streamlining eligibility checks and simplifying application processes could improve accessibility for millions of individuals.
AI systems can also enhance workplace financial tools. Companies that integrate conversational AI into their human resources infrastructure could help employees access resources ranging from retirement plans to emergency savings programs. The ability to provide clear, personalized guidance about available benefits could enhance financial literacy and well-being across entire workforces.
Moving Forward with Intention
The findings from Commonwealth’s research reveal an essential truth: Creating AI systems that genuinely benefit LMI households is not just a technological challenge but a matter of ethical design. Effective tools must be built with an understanding of the real needs of those who have historically been excluded from technological advancements.
The technology exists. What remains is the challenge of building tools that are reliable, inclusive, and capable of addressing the specific needs of the people they are meant to serve. AI’s real potential will be realized only when it truly works for everyone.
