Today, roughly 32 million people in the US are ‘credit invisible’ or ‘unscorable’. About USD 140 billion in public benefits goes unclaimed each year. More than three million households face catastrophic disruption due to eviction annually. These are not edge cases but structural crises in the systems that shape economic opportunity in this country. 

Artificial intelligence (AI) is beginning to touch each of these systems. Our team recently assessed 25 AI use cases seeking to advance economic mobility and identified the top three most promising: those with high feasibility and high potential for impact, as well as strong early evidence and momentum from investors and funders. 

1. Credit access: Expanding access using non-traditional credit measures 

Millions of people in the U.S. remain outside the formal credit system because existing scoring models overlook their financial reliability. Alternative underwriting using non-traditional data (e.g., rent, utility payments, cash flow) and driven by AI systems can build a more dynamic picture of repayment capacity.  

The consumer credit card company Tilt evaluates applicants using verified income and bill payment data, generating a credit assessment for people who would otherwise be invisible to traditional scoring systems.  

While these non-traditional predictive models have been in use for decades, recent regulatory guidance and advances in responsible AI may lead to broader adoption. The market value of AI agents in the financial services market (including agents that automate customer service, fraud detection, compliance, and credit decisioning) reached USD 691 million in 2025 and is projected to grow nearly tenfold to USD 6.7 billion by 2033.  

2. Benefits navigation: Disbursing billions already on the table 

Each year, millions of eligible families miss out on benefits because the systems meant to help them are too complex to navigate, with USD 140 billion in unclaimed benefits left on the table annually. Generative AI tools can guide applicants through these systems faster, more accurately, and can do so in any language.  

GetCalFresh, a digital application platform for the Supplemental Nutrition Assistance Program (SNAP) in California, reduced application time from 60 minutes to 10 when it was introduced in 2014 and contributed to raising California’s SNAP participation rate from 66% in 2014 to 81% by 2022. This digital application is now hosted on BenefitsCal.com—a one-stop-shop for food, cash aid, and health coverage benefits—where an AI-enabled chatbot supports users in accessing benefits in 19 languages. 

However, these technological solutions can rarely survive without systemic reforms. Benefits Data Trust, a USD 20 million nonprofit using AI and data to expand benefits access, collapsed in mid-2024, presenting the open question: which agencies have the governance, data infrastructure, and funding to make this work long-term? 

3. Housing stability: Moving from crisis response to prevention 

Evictions destabilize over 3 million families in the U.S. per year, yet most assistance arrives too late. Predictive analytics can help shift policy from crisis response to prevention by identifying at-risk renters before displacement occurs and channeling aid more effectively. 

Los Angeles County’s Homelessness Prevention Unit has used predictive analytics to identify residents at elevated risk of housing loss, proactively connecting them to case management. Participants were 71% less likely to enter a shelter within 18 months, and 86% retained housing post-program. 

These systems require sensitive data (court records, benefits enrollment, mental health crises) linked across agencies and with integrated privacy protocols—a highly complex task for public data administrators. The primary barrier to expanding these tools often isn’t technology, but governance. 

But how to make it safe and fair? 

Now the essential warnings: The same methods that make generation and prediction possible also carry enormous risk. Examples of hallucinations about benefit eligibility, credit denials without justification, and data breaches are happening now. Worse yet, models can make these errors in a biased manner, amplifying the biases in their underlying training data.   

If AI is to inclusively shape the next generation of financial and public benefit systems, it will require a shift in approach: investing in infrastructure (shared data standards, interoperability across agencies), not just point solutions. Partnering with evaluators from day one, not bolting on assessment later. Building public trust through transparency, not hoping it emerges. And having the hard conversation about which tradeoffs to make between speed, scale, and equity. 

The next wave of AI driven economic mobility initiatives will not be won by the ones with the slickest algorithms. They will be won by the ones with the strongest governance frameworks.