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EU AI Act likely not a hassle for most use cases

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1 year ago

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While the EU AI Act—which officially went into force about a year ago—creates a number of new obligations regarding AI, its more stringent mandates apply only to the minority of organizations involved with high-risk use cases, with the rest able to mostly get by with what they already do to comply with the EU’s General Data Protection Regulation. 

This is according to Dr. Rafae Bhatti, chief information officer of Thunes Financial Services and a speaker at the Governance, Risk Management and Control conference in New York, hosted annually by the Institute of Internal Auditors and ISACA (formerly the Information Systems Audit and Control Association.) While navigating the EU AI Act might seem intimidating to people, he said that, for the majority of organizations, the things they’re expected to do are better thought of as extensions of current regulations with which many are already complying. 

“It is not completely a situation where you need to start from scratch. You may already have certain cybersecurity controls, certain data privacy controls, and that is one of the important pieces of guidance that I’d like to share with you so that you can feel a little bit more comfortable about not having to start from scratch as it relates to cybersecurity and data privacy,” he said. 

EU AI Act

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Before anything, he said, understand the scope of the regulations, which vary based on the AI in question as well as the specific use case toward which it is applied. Certain use cases, such as social scoring or real-time surveillance, are outright prohibited. Below that are high-risk use cases that involve industries such as health care, employment, education and other sectors with major societal implications. After this are those who fall in the limited risk category, which includes chatbots and image generators, followed by those deemed minimal risk, such as AI-enabled video games or spam filters. 

Another way to think of these various risk levels and their consequences, said Bhatti, is in terms of “career ending, sleepless nights, committee meetings or PowerPoints.” 

The good news, he said, is that very few organizations are involved in prohibited use cases, and even those involved in high-risk ones will be uncommon considering they’re restricted to specific sectors. 

“If you are a company that is only creating an application which is a game, you are probably not subject to most of the requirements. If you’re just a shopping website and not doing anything to do with employment or health care or education, there is going to be very minimal you’re required to do,” he said. 

Anything of a limited or minimal risk, he said, doesn’t trigger the AI-specific requirements of the EU AI Act, meaning entities should just continue doing what they’re already doing to comply with existing regulatory frameworks, “and if you’re doing it well you should be OK,” said Bhatti, adding that generally, “the only thing you still have to worry about is GDPR principles.” 

If something is considered high-risk, however, not only is it subject to greater GDPR scrutiny—meaning “if previously you were not taking it seriously, now is the time to take it seriously because there will be a requirement for a conformity assessment.” But some of the AI-specific measures also kick in. Part of this is more stringent security requirements, such as controlling for AI-specific attacks such as data poisoning and prompt injections (broadly referred to as ‘adversarial robustness’ controls.) 

Beyond this, those involved in high-risk use cases must also consider fairness and nondiscrimination controls; transparency and explainability controls; accountability and human oversight. What exactly counts within these categories, though, can be a matter of debate, starting with whether the use case is even high risk or not. 

“Is this AI high risk? The lawyer might say, ‘legally yes.’ The engineer might say, ‘technically no.’  They are both at medium risk of losing their careers. This is going to be a back and forth. Just be prepared to have that argument,” he said. 

Then there are the other controls that, themselves, can rest on slippery definitions. For instance, the fairness and nondiscrimination control requirement ostensibly is to mitigate the effect of bias in AI models. But the definition of these things can be tricky. An engineer might ask what exactly is the definition of fairness; a lawyer might answer, “whatever keeps us out of court,” which he conceded was an unhelpful answer, but one that some will likely use. 

Similarly, while explainability might seem like a simple enough concept at first glance, the detail and granularity of these explanations can be a point of contention. Some people may go into exhaustive detail about how their AI works while others might try to say, “It works in mysterious ways.” Such an answer is not necessarily in the spirit of the rule, but some try to use it anyway. However, he said such questions are only required to be addressed by those using high risk use cases. 

Transparency controls will be more common, as they are required for those involved in limited-risk use cases. Generally, he said, people need to know that they’re interacting with AI, such as through a privacy clause that tells users the system uses it to process their data, or even a note in the interface. However one does it, following this regulation needs documentation as well as a conformity assessment. 

The last bucket is accountability. Who is responsible for the AI? He cautioned against taking a cavalier approach to this question. There needs to be real accountability, along with the ability to escalate further up the chain. 

“Your answer shouldn’t be that it leads to a voicemail. A 1-800 number is not going to cut it, an email is not going to cut it,” he said, though noted that only high risk cases require documentation. Still, even if it’s not strictly required, he said it’s a good idea to consider this anyway. 

He stressed that most of the time organizations will only need to account for transparency. This does not, however, mean they should ignore all other controls. While it may not be specifically required to control for fairness and explainability, he said it is likely still a good idea for any organization dealing with AI. 

Bhatti said AI itself can be a valuable tool in complying with the EU AI Act, as it can do things like perform initial risk analyses and gap assessments, as well as monitor and retrieve vast stores of organizational data. However, he cautioned against letting AI agents perform actual remediation steps, as he felt there is still too much risk (noting, for example, how an agent accidentally deleted a company’s entire codebase by accident).

“If someone is trying to convince you that automatic remediation using [AI agents] is happening now, and that you should adopt it, proceed with caution,” he said. He noted that a few years from now “we can get to a point where we have enough confidence with what automatic remediation is doing. But not today.” 

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Accounting

SEC’s Semiannual Reporting Proposal Faces Investor Pushback: What CFOs Need to Know

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1 week ago

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August 25, 2026

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U.S. Securities and Exchange Commission (SEC)

A proposal from the U.S. Securities and Exchange Commission to potentially shift some public companies away from quarterly financial reporting toward a semiannual model is drawing significant pushback from investors, even as it continues moving through the regulatory process. The debate has direct implications for corporate finance teams, auditors, and the broader transparency of U.S. capital markets.

What the SEC Proposed

According to a summary published by accounting advisory firm Cohen & Co., the SEC issued a proposed rule on May 19, 2026, aimed at simplifying financial reporting requirements for many U.S. public companies. The proposal would potentially reduce the frequency of certain mandatory disclosures from quarterly to semiannual, a structural change that has not been made to core U.S. reporting requirements in decades.

The proposal follows an extended debate within U.S. policy circles, with proponents arguing that reduced reporting frequency could lower compliance costs and free up management time for longer-term strategic planning rather than quarter-to-quarter results management.

Why Investors Are Pushing Back

Comment letters submitted in response to the proposal have been extensive, and according to Cohen & Co.’s review of the public record, investors “appear to be largely opposed” to the shift, viewing frequent interim reporting as a core benefit of U.S. capital markets relative to other jurisdictions.

Accounting and law firms have taken a more measured position, generally urging any changes to remain aligned with the Financial Accounting Standards Board (FASB), whose existing disclosure requirements and guidance are built around a quarterly reporting cadence. A shift to semiannual reporting without corresponding changes to FASB guidance could create friction between SEC filing requirements and GAAP-based disclosure expectations.

Lessons From the U.K. Experience

The debate is not without precedent. The United Kingdom moved away from mandatory quarterly reporting for listed companies in 2014, returning to a semiannual disclosure requirement. According to Cohen & Co.’s analysis, that experience offers a cautionary data point: there was no measurable increase in capital expenditure or R&D investment following the change, while analyst coverage of affected companies declined as reliable interim information became less available — a particular risk for smaller and newly public companies that rely on analyst coverage to maintain investor visibility.

Practical Implications for Finance Teams

Beyond the debate over disclosure philosophy, the proposal carries practical complications. Many companies have debt covenants and credit agreements structured around quarterly financial delivery; a shift to semiannual reporting could require renegotiating those terms. Reduced reporting frequency would also extend the “window of market silence” between disclosures, a factor that governance and investor-relations teams would need to manage carefully to avoid information asymmetry.

Separately, and unrelated to the reporting-frequency debate, the SEC and FASB have continued finalizing more routine updates this year. New Accounting Standards Updates are taking effect for December 31, 2026, fiscal year-ends covering income tax disclosures, credit loss measurement, induced debt conversions, and stock compensation, according to Eide Bailly’s review of 2026 ASU activity. Additional guidance on paid-in-kind dividends and environmental credits is also on the near-term horizon.

What to Watch Next

The semiannual reporting proposal remains in the comment and review phase, and no final rule has been adopted as of this writing. Finance leaders should monitor the SEC’s regulatory agenda for further movement, while treating the current quarterly reporting requirement as the operative standard until any final rule is issued and an effective date is set.

Given the extent of investor opposition documented in the comment file, a full shift to mandatory semiannual reporting appears more likely to result in either a scaled-back compromise or continued study rather than swift adoption — though the SEC’s ultimate direction remains uncertain.

 

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Accounting

AI-Driven Automation and Continuous Accounting Frameworks

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3 weeks ago

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August 15, 2026

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The accounting profession is undergoing a fundamental structural transition as enterprise finance departments shift from periodic month-end closes toward automated continuous accounting models. By integrating specialized machine learning algorithms directly into enterprise resource planning (ERP) platforms, chief accounting officers are transforming financial reporting from a retrospective exercise into a real-time operational asset.

The Shift from Periodic Close to Continuous Financial Reporting
Traditional accounting workflows heavily relied on manual data reconciliation, spreadsheet calculations, and multi-week closing cycles at the end of each fiscal period. In contrast, continuous accounting frameworks utilize automated software agents to process, validate, and post transactional data in real time as business activities occur.

Automated bank reconciliation tools cross-reference incoming bank feeds, invoice records, and purchase orders automatically. By resolving transactional variances instantly throughout the month, corporate accounting teams eliminate the traditional workload spikes associated with quarterly and annual closes.

Machine Learning in Audit Trails and Anomaly Detection
Advanced natural language processing (NLP) and machine learning tools are redefining internal audit and financial control environments. Automated systems analyze 100% of general ledger entries, identifying anomalous transactions, duplicate payments, and unauthorized journal entries in real time.

Rather than relying on random statistical sampling, corporate internal auditors can focus their attention on high-risk flags automatically surfaced by algorithmic monitoring platforms. This continuous risk assessment strengthens internal controls over financial reporting (ICFR) and significantly reduces fraud risk.

Evolving Roles for Accounting Professionals
As routine data entry and manual reconciliation tasks become fully automated, the skill set required for accounting professionals is shifting toward data analysis, system design, and strategic business advisory.
– Systems Governance: Accountants are increasingly responsible for monitoring algorithmic accuracy and managing data integration pipelines.
– Business Partnership: Finance professionals leverage real-time financial dashboards to advise operational leaders on margin management and working capital allocation.
– Regulatory Compliance Management: Accounting teams utilize automated platforms to ensure compliance with dynamic tax codes and international accounting standards.

Core Implementation Recommendations
1. Deploy Automated Reconciliation Tools: Integrate continuous transaction processing modules into existing enterprise ERP architectures.
2. Establish Algorithmic Governance Controls: Implement strict internal testing protocols to ensure automated accounting rules comply with GAAP/IFRS standards.
3. Reskill Accounting Teams: Invest in training finance staff on data analytics, workflow automation, and predictive financial modeling.

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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4 weeks ago

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August 9, 2026

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Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.

Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.

In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.

Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.

Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.

Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.

Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.

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