Open Banking Transparency Gap: How Money Became More Visible, Yet Less Clear

Over the past decade, a quiet but profound transformation has taken place in the way people engage with their money. Digital banking systems, personal finance apps, and open banking frameworks all promised one central outcome: transparency. Money, it was said, would no longer be a black box. Every transaction would be recorded, every expense categorized, and every user would know exactly where their money was going.

That promise has largely been fulfilled, at least on a technical level. Today, nearly every movement in a bank account can be viewed in real time, broken down by category, date, and sometimes even by individual merchant. Yet alongside this progress, something essential appears to be missing. Despite the abundance of data, many users do not experience greater financial control or security. On the contrary, for some, the result has been increased confusion, cognitive overload, and financial anxiety.

To understand this gap, it is helpful to borrow a well-established concept from the field of business analytics: Data Rich, Information Poor. The term describes a situation in which large volumes of data are available, but the ability to translate those data into meaningful, decision-supporting insight is lacking. In organizational contexts, this phenomenon has long been recognized, as companies drown in reports and metrics while struggling to derive actionable conclusions. In recent years, the same paradox has increasingly migrated into the realm of personal finance.

This dynamic sits at the heart of the Open Banking Transparency Gap: data availability has improved dramatically, while interpretation and decision clarity have not. A pattern long observed in corporate environments is now manifesting at the level of individual financial behavior.

Why the Open Banking Transparency Gap Keeps Growing

Consider a simple example. A person opens their banking app and sees that they spent 3,200 USD on groceries last month. The figure is accurate, categorized, and correct. But what does it actually tell them?

Is that a lot or a little? Is it reasonable for a household of their size? Is it an outlier, or entirely normal? Without a reference point, the number provides no clear answer. It does not help the user decide whether to take action, remain calm, or simply continue as usual.

This is not a problem of mathematical literacy, but a problem of missing context. Humans do not process numbers in absolute terms. We understand reality through comparisons, norms, and patterns. In the absence of such a framework, data remain suspended, unanchored and difficult to interpret.

Research in cognitive psychology and behavioral economics shows that when individuals are exposed to data that cannot be immediately interpreted, two common reactions tend to emerge. The first is anxiety. Numbers feel “high” or “dangerous”, even when there is no objective reason for concern. The second is avoidance. Users stop looking, postpone engagement, or begin to treat financial data as background noise.

The result is a paradoxical situation: more transparency, less clarity. In other words, the data themselves are not the problem, the problem is the absence of a framework that explains what those data actually mean.

Technical Transparency Versus Cognitive Transparency

Most fintech solutions today focus on technical transparency. They excel at presenting data. The implicit assumption, however, is that users will know how to interpret what they see. This assumption is deeply problematic.

In the business world, raw data are almost never presented without context. Managers are not satisfied with knowing that a cost has increased. They want to know whether it rose relative to the budget, compared to last year, or in relation to competitors. Only then can meaningful conclusions be drawn.

In contrast, in the world of personal finance, users are presented with absolute numbers and left alone to interpret them. There is no benchmark for comparison, no clear norm, and no distinction between genuine anomalies and natural variation.

This gap helps explain why, despite massive investment in financial technology, indicators of financial literacy and economic well-being are not improving at the expected pace. According to reports by international organizations such as the OECD, a significant portion of the population struggles to understand how everyday financial decisions affect their medium- and long-term financial situation, even when relevant data are readily available.

From Technical Management to Relative Understanding

The problem, then, is not a lack of information, but a lack of relative information. Not “how much”, but “where”.

In analytics, this process is known as normalization. It refers to placing a data point within a broader context, such as a reference group, an average, an acceptable range, or a recognized behavioral pattern. Only once data are normalized can they become information that supports action.

In financial terms, this means shifting from questions like “How much am I spending?” to questions such as “How does my spending compare to households similar to mine?”. Not “What is the size of my debt?”, but “Is my level of debt reasonable given my income and life stage?”.

This represents a deep conceptual shift. It moves the focus away from the number itself and toward its meaning.

Where the Need for a New Kind of Solution Emerges

This transition is far from trivial. It requires combining data, comparisons, and behavioral insight into how people perceive and respond to financial information. It also demands caution, as poorly constructed comparisons can mislead just as much as a lack of information.

Still, without such a layer of context, it is difficult to see how open banking can fully realize its potential. As long as users continue to encounter disconnected figures, financial data will remain technical rather than decisional.

One of the approaches emerging in recent years, including that of EchoNomics, is built on the recognition that what is needed is not another tool for displaying data, but a way to bridge the gap between transparency and understanding. This approach does not focus on adding more information, but on reorganizing existing information so that it is presented within a human, comparative framework.

In this sense, EchoNomics is not merely a product, but an expression of a broader shift within fintech: a move from data accumulation to meaning creation.

The Real Challenge of the Next Generation of Fintech

If the central challenge of the previous decade was access to information, the challenge of the coming decade will be interpretation. Not more crowded dashboards, but tools that help distinguish what matters, what is unusual, and what is simply part of normal variation.

Financial clarity does not emerge from additional charts. It emerges from reduced uncertainty. When users have context, they can stop guessing. And when they stop guessing, they can begin to make informed decisions.

As long as financial systems continue to focus on presenting data without providing context, the gap between transparency and clarity will persist. The true challenge facing the next generation of financial solutions is not purely technological, but interpretive: how to turn available information into understanding that enables action.