The Hidden Cost of Poor Data in Banking Risk Management-And How To Make It an Asset in the Age of AI

For years, the conversation about banking risk management has revolved around models: new VaR methodologies, more sophisticated credit approaches, ever more refined liquidity stress tests, etc. But if you listen carefully to how people actually spend their time inside of many institutions, the story is very different.

The real bottleneck in banking risk management is no longer the sophistication of models. It is the usability, consistency, and history of the data that feeds them.

Across market, credit, and liquidity risk, senior leaders still struggle to get comprehensive answers to deceptively simple questions:

Which positions are truly driving our intraday liquidity needs in a given shock? Which counterparties are simultaneously our largest credit exposures and our most critical funding providers? How exactly does a scenario propagate across P&L, collateral, and funding over the next few days? And why, when we finally think we have an answer, doesn’t it line up neatly with what our regulatory reports show?

Paradoxically, this is happening in a world where banks have more data than ever. Regulatory initiatives over the past decade have forced institutions to capture information at a level of granularity that would have been unthinkable twenty years ago. The issue is not volume. It is that this data is fragmented across systems, inconsistently represented, and very difficult to analyse at scale over time.

The result can be a quiet drag on profitability and decision‑making, a persistent vulnerability in regulatory reporting, and a serious limitation on what banks can credibly do with AI. Yet, for those who manage to tame it, the same “regulatory plumbing” can turn into a distinctive strategic asset.

The Everyday Reality Behind the Dashboards

Inside many banks, the daily reality behind polished dashboards is far messier than it appears.

In market risk, teams operate sophisticated VaR, ES, and stress frameworks, yet the underlying data is often scattered and inconsistent. Positions are booked across multiple front‑office systems that represent products differently depending on desk, region, or legacy platform. Market data is sourced from various feeds, not always aligned or historised in a comparable way. Instrument terms are incomplete or not stored in a form that models can easily consume. When markets move abruptly, it can take hours or days to reconcile which figures are “right” before anyone can focus on decisions. In fast markets, that delay is itself a risk.

Credit risk faces a similar pattern, with a different flavor. Counterparty hierarchies are maintained in parallel across systems, creating multiple, slightly different views of who the bank is really exposed to. Ratings, limits, collateral, and behavioral indicators are spread across internal applications that do not talk to each other cleanly. When a large name comes under pressure, simply building a coherent picture of the bank’s exposure, available mitigants, and current utilisation of limits becomes a time‑consuming exercise. By the time the full picture is assembled, the opportunity to act early may already have passed.

Liquidity risk is, if anything, even more data hungry. Payment flows, securities financing, derivatives collateral, deposit behavior, and wholesale funding all reside in separate infrastructures. Identifiers for clients, accounts, and internal entities are rarely harmonised end‑to‑end. Maturities, settlement dates, intraday cut‑offs, and behavioral assumptions are stitched together from multiple sources. During a stress event, treasury and risk may know that, in aggregate, the bank has sufficient liquidity, but struggle to see precisely where it is held, how mobile it is, and which operational or legal constraints apply. That uncertainty often leads to overly conservative actions - expensive buffers, defensive funding choices, or missed opportunities.

Across these three areas, the pattern is the same. The problem is not the absence of data, nor the lack of models. It is the inability to assemble a consistent, timely, drill‑down view of positions, exposures and flows that everyone trusts. When market, credit, and liquidity each tell a slightly different story, and when none of them fully match what finance and regulatory reporting show, decision‑making slows down. Internal debates shift from “what should we do?” to “which number is correct?” at precisely the moments when clarity and speed are most needed.

When Supervisors Start Connecting the Dots

For a long time, regulatory reporting could be treated almost as an end‑of‑pipe activity. Data was extracted from operational and risk systems, massaged through reporting engines, and delivered to supervisors in the required formats. As long as the numbers were broadly plausible and sent on time, the pressure remained manageable.

Those days are gone. Regulators have become increasingly sophisticated in how they look at reported data. They cross‑check figures across templates for capital, liquidity, large exposures, and resolution. They compare internal management dashboards to the numbers they receive. They scrutinise time series, looking for unexplained jumps, structural breaks, or restatements. And in meetings with banks, they expect clear, timely explanations.

Inside many institutions, this has exposed a painful reality. The liquidity ratio on an internal dashboard does not exactly match the one in the LCR report. The capital figures discussed in risk committees are not quite the same as those in the official templates. Large exposure reports disagree in subtle but important ways with internal concentration analyses.

Addressing those inconsistencies within the reporting engine alone is almost impossible. The problem lies upstream, in the lack of a common, transparent analytical layer that feeds both management views and regulatory outputs. Without that, every reconciliation becomes a bespoke exercise, and every supervisory review a potential source of friction and remediation work.

The Missing Dimension: History

In many organisations, data quality is still discussed as if it were a point‑in‑time issue: are today’s numbers correct? But risk is about change. So is supervision. So is learning.

Stress testing and back‑testing demand long, consistent time series of positions, market conditions, behaviors, and outcomes. Understanding how a portfolio behaves as rates rise and fall, or as volatility cycles through highs and lows, requires the ability to replay past periods at full granularity, not just glance at a handful of monthly snapshots.

Supervisors, too, are looking at narratives over time. They ask why a ratio moved from one quarter to the next, whether a trend is structural or temporary, how a change in reporting methodology affected the numbers. When historical series are riddled with breaks that no one can convincingly explain, doubts about control and governance follow.

Internally, boards and senior leaders want to know how the bank’s risk profile has evolved through different episodes: what the liquidity position was at the start of the tightening cycle, how concentrations built up before a sector‑wide shock, what actually happened in the books during a particular week of stress.

Without a robust, historised analytical layer, answering these questions becomes an exercise in archaeology. Data teams dig through old files, rebuild positions from partial records, and piece together flows from archived reports. It is slow, fragile work, often arriving too late to be useful.

AI Raises the Stakes

At the same time, banks are investing in AI and machine learning across risk, treasury, and finance. Use cases range from early‑warning indicators for credit or liquidity stress, to anomaly detection in reported metrics, to AI‑assisted exploration of risk data through natural language.

All of these initiatives share a non‑negotiable requirement: large volumes of well‑structured, consistent, historised data.

AI amplifies both the strengths and the weaknesses of the data it sees. When the underlying information is rich, coherent, and well‑labelled, models can pick up patterns that would be difficult to detect with traditional tools. When the data is fragmented, inconsistent or riddled with gaps and unrecorded changes in methodology, AI will faithfully learn and amplify those flaws.

A machine learning model that aims to detect emerging liquidity stress, for example, depends on a detailed, historical record of flows, balances, collateral usage, market conditions, and management actions. If that record is incomplete or inconsistent across entities and time, the model will either underperform or produce signals that no one fully trusts.

Similarly, an AI system designed to spot anomalies in regulatory metrics can only be credible if “normal” historical behavior is well understood and reconstructed. Without that, every flagged anomaly might simply reflect a past change in data sourcing or calculation logic that was never properly documented.

Even more visibly, AI‑assisted interfaces that let users ask questions in natural language about positions, exposures, or scenarios are only as reliable as the dataset they sit on top of. A beautiful conversational interface on top of inconsistent numbers is not progress; it is a new way to get into trouble.

In other words, AI does not reduce the need for good data. It raises the bar. It makes the lack of a unified, historised analytical layer not just a nuisance, but a direct limitation on innovation.

What “Good” Starts to Look Like

Given all this, what does “good enough” look like in practice? Not perfection, but a data foundation that is coherent across risk stripes, consistent over time, and accessible both to humans and to machines.

At its core, this means aligning a few basic dimensions across the institution: legal entities that match regulatory scopes of consolidation, counterparties and groups that are identified the same way everywhere, products and instruments that carry the same key terms, currencies and market factors that can be traced back to their sources, and collateral that is classified consistently in terms of type, eligibility and encumbrance.

On top of that, market risk needs trade‑level histories with clear links to risk factors and properly historised market data. Credit risk needs stable hierarchies, ratings and limits over time, tied back to collateral and guarantees. Liquidity risk needs transaction‑level cash flows and settlement schedules, with a clear view of accounts, and behavioral patterns, at both daily and intraday horizons.

Crucially, all of this must be historised and versioned. It should be possible to reconstruct what the bank “knew” about its positions, exposures, and metrics at a given date, and to see how changes in methodologies or data sources affected the outputs. And from any high‑level number - an internal liquidity gap, a concentration ratio, a reported capital figure, or a model output - it should be possible to drill down quickly to the underlying portfolios, trades, and flows.

This is not something that any single operational system was designed to do. Trading platforms, treasury systems, core banking, payments infrastructure and regulatory engines all serve important purposes, but none of them is intended to be the bank’s cross‑risk, cross‑report, historical analytical backbone.

That role falls to a specialised analytical layer.

How Banks Are Building That Layer

In practice, the institutions that are making the most progress are not ripping out their existing systems. Instead, they are introducing an intermediate platform whose job is to ingest data from those systems, harmonise it, historise it, and make it explorable.

This is the space in which Opensee operates: as a high‑performance, cross‑risk analytics layer that sits on top of existing infrastructure rather than replacing it. The platform brings together market, credit, and liquidity data at full granularity, aligns identifiers and reference data, and keeps track of history. It then provides two complementary ways to use it: an interface for risk, treasury ,and finance teams to explore exposures, scenarios, and reconciliations interactively; and programmatic access via APIs and Python libraries for quants, data scientists, and reporting teams.

One tier‑1 European universal bank took this approach to tackle a recurring pain point. Each time markets became volatile, they found themselves scrambling to understand the combined impact of price moves on P&L, margin calls, and liquidity - and to align those views with what would later appear in regulatory reports.

By building a cross‑risk, historised view on top of Opensee, they were able to bring together derivatives positions, collateral agreements, margin call histories, payment flows, liquidity buffers, and regulatory classifications. When they simulated a shock, they could see, in one environment, how variation margin would evolve, where collateral would be needed, what the funding gaps might look like by entity and currency, and how this related to their reported ratios.

Their regulatory reporting team gained a different but related benefit. When supervisors questioned a movement in a reported figure, they could trace it back through the analytical layer to specific positions and transactions, and even show how similar patterns had played out historically. That changed the tone of discussions from defensive to analytical.

At a smaller regional bank, the starting point was different. Their biggest corporate relationships were viewed through separate lenses by credit, markets, treasury and finance, and their large exposure reports did not fully match internal concentration analyses. On top of that, data scientists exploring AI use cases struggled to find a single, reliable dataset to work from.

By consolidating data in Opensee and building a unified, historised view of those relationships, the bank was able to see, for the first time, the combined picture of credit exposure, derivatives usage, collateral pledged, funding reliance and reported large exposures, both today and back through time. That empowered more nuanced conversations with relationship managers about pricing and limits, improved the alignment between internal and regulatory views, and gave the AI team a solid base for building early‑warning models around concentration and funding dependence.

“It changed the tone of our internal debates,” their CRO says. “We now talk about clients and portfolios in a genuinely holistic way, and when supervisors ask for details or trends, we all look at the same numbers - and the same history.”

A Practical Path Forward

None of this needs to start as a five‑year transformation program. The banks that move fastest tend to begin with a clearly defined slice where risk, regulatory, and AI benefits overlap: a derivatives portfolio with significant margining implications; a key funding currency where intraday liquidity and payments matter; a concentrated corporate segment that features prominently in both business strategy and supervisory focus.

They connect the relevant data sources for that slice, harmonise identifiers and reference data, and build a unified, historised analytical view. They put that view into the hands of risk and treasury leaders through dashboards and interactive analysis, and open it up to quants and data scientists through APIs and Python. They validate that they can reconcile the numbers with those in regulatory reports, and that they can answer “what happened?” and “what if?” with equal ease.

From there, they extend to more entities, more books, more currencies, and more risk factors. They embed the analytics in governance processes - risk committees, ALCO, pricing, and limit setting. They use the same layer as the foundation for AI models, anomaly‑detection tools, and more user‑friendly ways for people across the bank to interrogate risk data.

Step by step, what began as a response to supervisory pressure becomes a differentiating capability: a single, transparent, historical view of market, credit, and liquidity risk that everyone can work from.

From Burden to Advantage

In an environment of higher rates, tighter liquidity, more volatile markets, and increasingly demanding supervisors, banks cannot afford to treat data and reporting as back‑office chores. The ability to see risk clearly, to tell a consistent story across internal and external views, and to build trustworthy AI on top of that reality is becoming a core competitive asset.

Most institutions already have the raw material. They are sitting on years of detailed, regulated data. The question is whether they can turn it into an analytical foundation that serves risk, treasury, finance, reporting, and AI together.

Platforms like Opensee are one way of doing exactly that: ingesting and harmonising large volumes of market, credit, and liquidity data at full granularity and over time; providing an engine for fast, interactive analysis; and exposing the same trusted, historised dataset to the people and models that need it.

For banks that succeed, the payoff is more than compliance. It is the ability to make faster, better‑informed decisions; to engage supervisors from a position of clarity; and to move into the world of AI‑driven banking risk management with confidence that the models are learning from the right reality.