The next frontier in fintech is understanding the person behind the data

Fintech has spent almost two decades making financial information increasingly accessible, portable, and actionable. Open banking, digital wallets and connected financial platforms have created an environment in which consumers can move between products while allowing authorized providers to access increasingly detailed information about their financial activity. The Consumer Financial Protection Bureau estimates that more than 100 million consumers have used consumer-authorized data access, allowing third parties to access their financial information.

The volume of information available to financial institutions has grown alongside the complexity of consumers’ financial lives. The Federal Reserve’s 2024 Survey of Household Economics and Decisionmaking found that 73 percent of adults reported doing okay financially or living comfortably, while 63 percent said they could cover a hypothetical $400 emergency expense using cash or its equivalent. Those figures describe financial conditions at a particular point in time, while household income, spending, and financial resilience can change continuously.

That distinction matters because a transaction is an observation rather than a complete explanation. A purchase can reveal where money went, when it went there, and how much was spent. The same transaction pattern can carry different implications for two people with different income rhythms, obligations, savings buffers, risk tolerance, or financial goals. Research from the Bank for International Settlements has highlighted the growing importance of data quality, governance and management as artificial intelligence becomes increasingly embedded in financial services.

The question for fintech, then, is moving beyond how much information a system can collect toward what it can meaningfully understand. Amr Mohamed, founder and CEO of Neumetria, believes this represents the next important layer of financial technology. His argument is that the industry has built extensive infrastructure for accessing and processing financial activity, while leaving a significant gap between observing transactions and understanding the person generating them.

Mohamed’s perspective comes from years of building financial products and infrastructure. His experience as both a fintech user and builder repeatedly exposed what he describes as a persistent disconnect between access and context.

“As both a consumer and a builder within fintech, I have observed how rapidly the industry has expanded access to financial products while the underlying context surrounding those products has remained fragmented,” Mohamed says. “Consumers may interact with banking, investment and digital asset products separately, yet their financial decisions are fundamentally interconnected. The consumer is managing one financial life, regardless of how many products are involved.”

That observation leads Mohamed to a concept he calls ‘behavioral understanding. He believes fintech’s next evolution will require systems capable of bringing banking, spending, saving, borrowing, investing, and other activities together into a continuously evolving picture of a person’s financial situation. The objective is to create what he describes as a live financial state that changes as behavior changes.

In Mohamed’s view, this requires looking beyond basic transaction categorization. Most systems can identify whether a transaction represents dining, travel, groceries, or another category. He argues that the more consequential question is why the transaction occurred and what it means within the individual’s broader financial trajectory.

“Categorizing a transaction tells us what occurred,” Mohamed says. “The more valuable question is what the transaction reveals about the individual’s circumstances, objectives, and financial behavior. Context allows a financial system to move from recording activity toward understanding it.”

That contextual layer could allow financial products and agents to assess signals such as income stability, liquidity horizon, spending confidence, financial resilience, and borrowing or investment readiness. A person who appears similar to another customer through historical transactions may have a very different current capacity because one has stable income and a rebuilding savings buffer while the other is facing declining income and rising obligations. Mohamed’s thesis is that financial infrastructure should be able to distinguish between those situations as they develop.

This becomes particularly important as AI moves from generating recommendations toward interacting with financial systems and potentially acting through agents. An AI system can reason through information, Mohamed argues, but its reasoning is only as useful as the context available to it. Before an agent recommends saving more, moving money, taking credit, or investing, it needs a reliable understanding of whether that action fits the person’s current circumstances.

NIST’s AI Risk Management Framework emphasizes that trustworthy AI requires attention to reliability, transparency, explainability, privacy, and fairness in the context in which systems are used.

For Mohamed, this makes financial understanding an infrastructure question for the emerging agentic-finance era. The same contextual layer could serve multiple financial experiences, allowing products to determine whether a customer is ready for a particular offer, whether a prompt is appropriate, or whether an agent should proceed with a financial interaction.

Lending provides a clear example. Current transaction behavior can offer additional context around affordability, liquidity, and repayment capacity. Yet Mohamed emphasizes that this should not become another opaque scoring mechanism. Neumetria’s stated approach is to provide the financial state and execute a financial institution’s policy while leaving the policy and final decision with the institution.

That distinction is central to Mohamed’s view of responsible financial AI. “The purpose of this infrastructure is to provide a more comprehensive understanding of financial circumstances while preserving institutional accountability,” he says. “The technology can contextualize the information, but the financial institution should retain responsibility for its policies, eligibility criteria and final decisions.”

Mohamed believes the opportunity extends well beyond lending. Better financial understanding could help banks, investment platforms, savings products and financial agents determine when an offering fits a customer’s circumstances. It could also help products provide access alongside greater contextual clarity. “Expanding access to financial products is important, but access becomes more meaningful when it is accompanied by an appropriate understanding of the product and its relevance to the consumer’s circumstances,” he says. “The objective should be to improve the quality of the match between the individual and the financial product.”

The larger shift Mohamed envisions is therefore architectural. The financial industry spent decades building systems that could move money, record transactions, and make financial data increasingly available. He believes the next generation of infrastructure must help those systems understand the people behind the activity.

For Mohamed, that is the foundation of the category Neumetria is seeking to build. The company’s proposition is rooted in the belief that financial data already exists at enormous scale. The opportunity lies in developing the contextual layer that can transform that information into an evolving representation of financial circumstances.

“The financial industry has invested decades in building infrastructure capable of moving money and capturing financial activity,” Mohamed says. “The next generation of fintech must build the infrastructure capable of understanding what that activity means for the person behind it. That is where financial data becomes financial understanding.”