Finance
Where Does The Sustainable Finance Disclosure Regulation Go From Here?
Confusion has reigned since the EU’s “Sustainable Finance Disclosure Regulation (SFDR)” legislation went into force in March 2021. SFDR had highly ambitious objectives—not only preventing fund “greenwashing” but also shifting capital in support of the EU’s “Green Deal” to become carbon neutral by 2050. Three years later, it is worth asking whether SFDR has achieved those objectives. Or whether it has simply become a complex and ever-changing labeling exercise.
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As a starting point, it is still unclear exactly how to categorize a sustainable fund under SFDR. There has been much discussion about what exactly constitutes an Article 8 fund (so-called “light green” since they “promote environmental or social characteristics”) and an Article 9 fund (“dark green” since it goes further and “has sustainable investment as its objective”). The language here is highly ambiguous, particularly since the term “sustainable investment” is used to cover both types of funds, as discussed below. This has created a bonanza for lawyers hired by fund managers to help them substantiate how they are categorizing their funds.
The lack of clarity has created significant confusion in the market. Fund managers have “downgraded” Article 9 funds to Article 8. They have “upgraded” Article 6 funds, which are not claiming any sustainability benefits but still have to report on sustainability risks, to Article 8 and even Article 9. According to Morningstar, in the past quarter 220 funds changed their classification, 190 of these being Article 6 to Article 8.
Very sensibly, on September 14, 2023 Mairead McGuinness, Commissioner for Financial Services, Financial Stability and Capital Markets Union announced “an in-depth three month consultation for stakeholders” to determine “if our rules meet their needs and expectations, and if it is fit for purpose.”
On May 3, 2024 the EU published a Summary Report of this Consultation. It found “Widespread support for the broad objectives of the SFDR but divided opinions regarding the extent to which the regulation has achieved these objectives during its first years of implementation.” Here are some of the key findings:
· “89% of respondents consider that the objective to strengthen transparency through sustainability-related disclosures in the financial services sector is still relevant today.”
· “94% of respondents agree that opting for a disclosure framework at the EU level is more effective than national measures at Member State level.”
· “77% of respondents also highlighted key limitations of the framework such as lack of legal clarity regarding key concepts, limited relevance of certain disclosure requirements and issues linked to data availability.”
· 84% felt “ that the disclosures required by the SFDR are not sufficiently useful to investors.”
· 58% don’t feel the costs “to be proportionate to the benefits generated.”
· 82% felt “that some of its requirements and concepts, such as ‘sustainable investment ’are not sufficiently clear.”
It also found that 83% of respondents felt that “the SFDR is currently not being used solely as a disclosure framework as intended, but is also being used as a labelling and marketing tool (in particular Article 8 and 9).” That said, there was no consensus on whether to split the categories in a different way than Articles 8 and 9 or to convert them into formal product categories by clarifying and adding criteria to the underlying concepts.
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While the Consultation was clearly useful, it is telling that there is no clear path forward. It is also telling that there is substantial tension around the issue of transparency. The Consultation found strong support for it but that the current amount was insufficient, yet what there is has a questionable cost/benefit ratio. Squaring that circle will be hard, especially since transparency is seen as the key driver of capital allocation. The brutal fact of the matter is that this complex legislation has been overly ambitious in terms of allocating capital. It is time for some soul searching. Among other things, this involves addressing three underlying fundamental issues: (1) the purpose of the legislation, (2) the impacts it is intended to achieve, and (3) how it addresses the need for financial returns.
In terms of purpose, the original legislation is clearly aimed at using fund disclosure as a lever to reallocate capital to address important environmental and social issues. Here the legislative text states, “As the Union is increasingly faced with the catastrophic and unpredictable consequences of climate change, resource depletion and other sustainability‐related issues, urgent action is needed to mobilise capital not only through public policies but also by the financial services sector. Therefore, financial market participants and financial advisers should be required to disclose specific information regarding their approaches to the integration of sustainability risks and the consideration of adverse sustainability impacts.”
The language here is telling in the word “impact(s).” It appears 39 times in the 16-page directive. At the same time, the term sustainability risk(s) appears 33 times. “A sustainability risk means an environmental, social or governance event or condition that, if it occurs, could cause a negative material impact on the value of the investment.” There is a fundamental tension here that is not addressed since these are independent variables. A company can be doing a good job of managing its sustainability risks for shareholder value creation, now called “single” or “financial” materiality, while still creating negative impacts on the world, or “impact” materiality. The two combined, as is the case with the European Sinancial Reporting Standards (ESRS) developed by the Sustainability Reporting Board (SRB) of the European Financial Reporting Advisory Group (EFRAG) for the Corporate Sustainability Reporting Directive (CSRD), are “double materiality.” As with the CSRD, the EU is expecting a great deal from reporting.
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This begs the question of what is a “sustainable investment?,” as noted above. The term is used 11 times in the directive. It is only defined on the eighth time, halfway through on p. 8:
“‘’sustainable investment’ means an investment in an economic activity that contributes to an environmental objective, as measured, for example, by key resource efficiency indicators on the use of energy, renewable energy, raw materials, water and land, on the production of waste, and greenhouse gas emissions, or on its impact on biodiversity and the circular economy, or an investment in an economic activity that contributes to a social objective, in particular an investment that contributes to tackling inequality or that fosters social cohesion, social integration and labour relations, or an investment in human capital or economically or socially disadvantaged communities, provided that such investments do not significantly harm any of those objectives and that the investee companies follow good governance practices, in particular with respect to sound management structures, employee relations, remuneration of staff and tax compliance.”
This definition makes clear that SFDR is primarily aimed at directing capital to address environmental and social issues, and many are named.
At the same time, there is an added layer—not only must these investments create positive impact, but they must also “not significantly harm any of those [environmental or social] objectives.” This ignores the fact that every company, no matter how well intended, produces negative externalities even when it is diligently operating according to existing laws and regulations. It’s a kind of “have your cake and eat it too” desire. Thrown in at the end is a caveat about good governance which is mentioned three times but never defined. I suspect that most boards of directors, even in Europe, would consider shareholder value creation at the core of good governance. The essence of the message from SFDR is that fund managers should invest in companies that do good, don’t do bad, and have good corporate governance.
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The essential question, then, is whether SFDR has had any real world impact. Has there been a massive reallocation of capital in line with SFDR’s very laudable policy objectives? Although Article 8 funds now account for 55% of European fund assets, Article 9 funds only account for 3.4%. It is safe to say that the increase of Article 8 fund assets has not driven a massive shift in corporate activity to meet the EU’s environmental and social sustainability goals. So is it fair to say that SFDR has not achieved the real world impact that the legislation originally intended? In fact, it’s unclear whether there have been any efforts to actually assess whether SFDR has met the EU’s policy objectives of capital reallocation in service of achieving a more sustainable economy. As the EU revisits SFDR, it will be important to be clear about how to assess the success of any policy objective and what data would be used to measure this.
There is also the important question of how financial returns fit into the SFDR. The answer is “not much.” The term is used exactly one time: “In order to comply with their duties under those rules, financial market participants and financial advisers should integrate in their processes, including in their due diligence processes, and should assess on a continuous basis not only all relevant financial risks but also including all relevant sustainability risks that might have a relevant material negative impact on the financial return of an investment or advice.” So financial return is only discussed in the context of single materiality and completely ignored in the context of impact materiality. It’s as if the legislation assumes no tradeoffs exist. Similarly, the term “value creation” is never used. “Value” is used three times. Twice about sustainability risks and once about insurance products.
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So what should be done? Easy to say but hard to do given the political and economic capital that has been invested in the SFDR. The EU needs to carefully consider what the policy objective of the legislation is, ensure the intended impact is something that is actually achievable through fund disclosure, carefully tailor the legislation to achieve those intended impacts, consider the cost-benefit ratio, and determine how they will measure and assess whether it’s achieving the intended impact. There’s also the important missing piece of returns. Whatever politicians wish capital would do, what it does do is go to where there is the right risk-adjusted return.
Oh, and while disclosure is very important, it’s equally important to not expect too much from it alone.
New research shows the finance sector leading regulatory authorities in adopting artificial intelligence (AI).
First home buyer Jessica Ricci was just trying to save a little extra money through her superannuation in a federal government scheme intended to help people like her. But an error from tax authorities has left her paying more tax than the top income bracket on some super contributions – ironically having the exact opposite of the intended effect of the policy.
As a result, she’s lost out on an extra $2,250 in savings that was supposed to go to her house deposit. While the ATO pushed back over who was at fault for the mix-up, her case has highlighted an increasingly problematic blindspot when it comes taxpayers getting the short end of the stick when dealing with tax authorities.
“I’m definitely feeling a little bit helpless,” she told Yahoo Finance. “There’s not a clear path to rectify this.”
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Jess was tipping extra money into her superannuation as part of the First Home Super Saver Scheme which has been running for years and allows eligible first home buyers to take advantage of the tax benefits of their retirement savings and then pull those extra contributions out to use for a house deposit.
As part of the scheme, individuals need to apply to the ATO, which in turn requests the related money from the person’s super fund.
Over four years, Jess contributed the maximum $50,000 amount, ensuring not to exceed the $15,000 yearly cap. She did so with the expectation of claiming the benefit at the time of her house purchase, as per the rules of the scheme.
When she went to make the claim, much of the information was auto-populated by the ATO website. And after receiving her funds, and the amount being less than expected, she soon discovered that her first contribution was wrongly classified as a concessional contribution, meaning $2,250 was, in the words of an ATO official, “retained by the ATO as withholding tax”.
She has spent months going back and forth with tax officials trying to get the money she believes should be owed to her.
“They’ve all taken the same stance, which is; ‘Well, yeah, we made a mistake, but you didn’t catch it. You said that what we provided you was fine, so it’s your fault’.
“I think it’s crazy to put the onus or the burden on the average person. I think most people would rightfully assume that pre-filled data provided by the ATO would be accurate,” she said.
Most coverage of artificial intelligence in finance focuses on what these tools can do. Less attention is paid to how they perform under scrutiny, particularly in financial modeling, where small errors can carry real consequences.
After testing Anthropic’s Claude in real-world modeling scenarios, one conclusion stands out: Claude produces outputs that look credible at first glance but contain structural flaws that only an experienced professional would catch.
That gap between appearance and reliability is where risk begins.
Where AI Performs Well
Claude handled several foundational elements of financial modeling competently. It was able to:
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Build basic revenue models
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Generate standard financial statements
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Apply consistent formatting, labels and units
The outputs appeared polished and professional. In some cases, they resembled models produced by junior analysts. That is what makes them risky.
The models looked right. The structure appeared logical. Formatting signaled credibility. For a time-constrained professional, those cues can create trust before a full audit is completed.
The Errors That Hide in Plain Sight
A closer review revealed issues that would likely go unnoticed without technical expertise:
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Broken linkages between financial statements
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Hardcoded values instead of centralized assumptions
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Non-dynamic formulas and inconsistent logic across periods
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Balance sheets that did not balance
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Timing mismatches between beginning- and end-of-period values
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Circular reference issues in areas like revolving credit
These are not edge cases. They point to a broader issue. The model may function, but it is not built on a reliable or auditable foundation.
Where Best Practices Break Down
Beyond individual errors, the models often failed to follow core financial modeling principles:
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Assumptions were not clearly separated from calculations
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Error checks were largely absent
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KPIs lacked depth and industry-specific nuance
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Formula design was inconsistent or inefficient
These gaps affect more than presentation. They determine whether a model can be trusted, adapted and audited under pressure.
The Real Risk Is Overconfidence
The key distinction is not between AI and human-built models. It is between models that are understood and those that are not. When a professional builds a model, every assumption and linkage is intentional. Even limitations are typically known. With AI-generated models, that understanding is outsourced.
This creates a different kind of risk:
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The logic behind the model may not be fully clear
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The structure may not align with internal standards
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The review process may be less rigorous because the output appears complete
In practice, credibility is inferred from how the model looks, not how it was built.
Reviewing Is Not the Same as Building
There is also a practical workflow issue. Reviewing an AI-generated model is not equivalent to building one.
When reviewing:
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You are interpreting logic you did not design
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Errors can be harder to trace
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Inconsistent structure increases audit time
In some cases, it is faster to build a clean model from scratch than to fix a flawed AI-generated one.
What This Means in Practice
Financial models support decisions involving significant capital. Even small issues can cascade:
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Misstated cash flows can distort debt capacity
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Timing errors can affect liquidity assumptions
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Weak KPIs can lead to incomplete analysis
There is also a question of accountability. Regardless of how a model is created, responsibility for its output remains with the professional using it.
Where AI Fits Today
AI tools can still be useful in financial modeling. They can help:
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Speed up repetitive components
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Generate starting points for analysis
But they are not a substitute for professional judgment. Nor are they ready to operate without close oversight. For now, their role is best defined as assistive, not authoritative.
A More Practical View of AI in Finance
The conversation around AI in finance does not need more optimism or skepticism. It needs more precision. AI can produce outputs that are visually convincing and directionally correct. In financial modeling, that is not enough.
The real risk is not that AI makes mistakes. It is those mistakes that are easy to miss, especially when the output looks finished. For financial professionals, the takeaway is simple: treat AI-generated models as drafts, not decision-ready tools.
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