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The Data Gap in AI Financial Guidance Tools

AI could democratize financial guidance, but the data needed to serve underrepresented consumers is often hardest to access. How do we close the gap?

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By Megan Coffey, Carly Perera, Elvis Diaz

Tuesday, June 30, 2026
The Data Gap in AI Financial Guidance Tools

Why the Right Data Matters for AI Financial Guidance

Across the financial services landscape, institutions, fintechs, and nonprofits are deploying AI-powered tools to help people manage debt, navigate financial shocks, plan for the future, and make everyday financial decisions. 

Done well, these tools have the potential to expand access to financial guidance, particularly for consumers who have been historically underserved by mainstream financial systems. Done poorly, they risk repeating and accelerating the same patterns of exclusion the financial services industry has struggled with for decades. 

To better understand what responsible AI-driven financial guidance requires, the Financial Health Network conducted a series of interviews with cross-sector experts. We examined how organizations are deploying AI and identified gaps where critical information remains inaccessible.

Our findings reveal a growing disconnect: The data most important for delivering meaningful financial guidance is often the hardest to access. As organizations race to deploy AI tools, addressing these gaps is critical to ensuring that guidance supports—not excludes—the people it is intended to serve.

Key Insights

Explore the full brief to understand the unique data challenges shaping AI financial guidance, along with actionable recommendations for institutions, employers, mission-driven organizations, philanthropic partners, and policymakers. 

financial stock market graph illustration ,concept of business investment and stock future

Critical data is fragmented across systems.
The information most critical to guiding underserved consumers often falls outside traditional reporting systems, with no clear pathway into these tools. Without data that reflects the complexity of consumers’ financial lives, AI guidance risks being technically accurate, but practically wrong for the people who need it most.

Smiling young couple meeting with their financial advisor in an office

Human oversight remains essential.
Transactional data can explain what someone has done financially, but not why. Behavioral and contextual information is key for delivering meaningful, personalized guidance, especially during complex or high-stakes financial decisions.

man using a banking app

Responsible AI standards are still emerging.
Across the sector, organizations lack shared benchmarks for responsible AI guidance, mechanisms for flagging harm, and accountability when tools fail to support the people they’re meant to serve.

Spotlight

What Data is Critical to Deliver Financial Guidance?

To better grasp what AI tools need to deliver meaningful financial guidance, we developed a framework outlining the four data categories that matter most. Together, these categories offer a clearer picture of what information is available today, and where critical gaps still remain. 

Data type What it captures Source Examples
Traditional financial data
Structured, institution- or bureau-held, relatively accessible		 Data generated through formal account and credit activity Banks, credit unions, and credit bureaus
  • Bank account balances
  • Transaction and payment history
  • Credit score and debt history
  • Direct deposits of take-home pay
Financial data outside traditional reporting systems
Financial in nature, but harder to access or standardize 		Financial data not captured by standard banking or credit bureau data systems Government, employers, and fintechs
  • Benefits eligibility and enrollment
  • Public assistance income
  • Cash and informal  income 
  • Tax filing and refund history
Behavioral and contextual data
Likely qualitative and self-reported, essential for understanding readiness and context		 Information about who someone is, how they make decisions, and what is happening in their life Consumer self-reported; financial institutions, fintechs, and coaching providers
  • Financial goals 
  • Household composition and caregiving responsibilities
  • Cultural context and values around money
Publicly available community-level data
Accessible today, but underused in financial tools		 Community-level and contextual data that is publicly available and not tied to any individual; can inform guidance by providing relevant local and demographic context that individual-level data alone cannot capture Government, research institutions
  • Regional cost of living 
  • Local benefits program availability 
  • Cultural and demographic research on financial behaviors

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Written by

Megan Coffey

Megan Coffey

Senior Manager, Financial Services Solutions
Financial Health Network
Carly Perera

Carly Perera

Director, AI Strategy
Financial Health Network
Elvis Diaz

Elvis Diaz

Associate, Financial Services
Financial Health Network

The Data Gap in AI Financial Guidance Tools

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