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3 types of AI assessment: governance, performance, conformity

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The number of regulatory frameworks that involve assessments of AI systems for various reasons has exploded in a short time and yet there is little consensus on questions like why, when, how, and for whom they should be done in the first place. 

Big Four firm EY, in a recent report, said (citing the OECD) that there are currently over 1,000 AI policy initiatives that include legislation, regulations, voluntary initiatives and agreements in 70 different countries. Despite all being concerned with AI, they often differ in their overall policy objectives, the subject matter being assessed, the purpose behind the assessments, the methodology for conducting them, who is expected to perform the assessment, and even the terminology used to describe them. “AI has been advancing faster than many of us could have imagined, and it now faces an inflection point, presenting incredible opportunities as well as complexities and risks. It is hard to overstate the importance of ensuring safe and effective adoption of AI. Rigorous assessments are an important tool to help build confidence in the technology, and confidence is the key to unlocking AI’s full potential as a driver of growth and prosperity,” said Marie-Laure Delarue, EY’s global vice chair of assurance. 

On this last point, this is the reason why EY chose to simply use the term “assessment” to describe the veritable galaxy of different ways of inspecting AI systems.

EY's London office
The EY offices in London.

Jack Taylor/Photographer: Jack Taylor/Getty

Overall, though, the firm identified three main types of assessments based on looking at all the different regulatory frameworks, from international to national to state to local: 

  • Governance assessments: To determine whether appropriate internal corporate governance policies, processes and personnel are in place to manage an AI system, including in connection with that system’s risks, suitability and reliability. 
  • Conformity assessments: To determine whether an organization’s AI system complies with relevant laws, regulations, standards, or other policy requirements. 
  • Performance assessments: To measure the quality of performance of an AI systems’ core functions, such as accuracy, non-discrimination and reliability. They often use quantitative metrics to assess specific aspects of the AI system. 

EY conceded that the categorization can be a little slippery sometimes, an assessment that evaluates governance over an AI system, for example, may also be an assessment of conformity such as an assessment of an organization’s AI Management System against the ISO/IEC 42001 standard.
The report said that confusion over the myriad AI assessment frameworks can make it difficult to ensure consistent quality and accountability. Even when the objectives align, the specific requirements may still vary across jurisdictions: For example, various U.S. cities and states have policies that include assessments for bias in the AI systems used in hiring and employment, all with different requirements. NYC Local Law 144, for example, has different requirements for measuring bias than the state laws requiring bias assessments in Colorado and Illinois. 

There is also little uniformity over things that build confidence in conclusions, such as the extent of evidence required or the requirements for the providers of the assessments. EY noted, though, that assessments conducted by third parties may be viewed as more credible than those conducted by internal teams, especially if third party providers adhere to standards of professional responsibility, ethics and public reporting that internal teams might not be obligated to follow.

Another complicating factor is that mandatory AI assessments that evaluate compliance with a regulation will often be very different from voluntary assessments against a governance standard, such as the voluntary AI Risk Management Framework of the US National Institute of Standards and Technology (NIST). 

The report also noted that while there can be very little specificity when it comes to terminology, particularly where it concerns broad terms like “fairness,” “trustworthiness” and “transparency,” which can create ambiguity unless specified further. Without this specificity, the usefulness of certain assessments may be more easily called into question. 

There’s also the fact that AI systems themselves are often complex, integrated into larger environments and involve multiple stakeholders, making it difficult to even identify the appropriate subject matter for an assessment. Further, the variation in a model’s results over time (“model drift”) can make assessment outcomes outdated and misleading, and the variability of AI systems can complicate reproducibility. Lastly, the rapid advancement of AI technology may outpace the development of technical standards for evaluating performance.

The EY report said professionals need to understand just what, specifically, they’re assessing and do so with a well-specified business or policy objective. The purpose should then inform the selection of appropriate methodologies and reference standards. AI assessment frameworks should also have a clear and sufficiently defined scope, including the type of assessment (e.g., governance, conformity or performance), the subject matter, and guidance regarding when the assessment should occur. 

When determining who should actually perform the assessment, EY said people should keep in mind the competency and qualifications, the objectivity and the professional accountability requirements of potential providers. This is not only to hold the provider accountable, EY said those that follow these standards and guidelines enable confidence and help stakeholders understand how assessments are provided. 

The report said business leaders should consider having someone assess their AI systems regardless of whether there is a regulatory requirement to do so, as they can help identify and manage evolving risks, as well as whether the systems work as intended. Further, market dynamics, investor demand or internal governance considerations may make a voluntary AI assessment advisable to build confidence in a business’s AI systems. Moreover, if some AI systems are subject to regulatory obligations, business leaders may choose to use assessments to help measure and monitor compliance. 

“As businesses navigate the complexities of AI deployment, they are asking fundamental questions about the meaning and impact of their AI initiatives. This reflects a growing demand for trust services that align with EY’s existing capabilities in assessments, readiness evaluations, and compliance,” said Delarue. 

The report calls to mind another published last month by the AICPA and Chartered Professional Accountants Canada which said the rapid rise of AI throughout the global economy opens up new opportunities for accounting professionals to provide independent assurance of these systems to help build trust and confidence in their functions.

It made a similar point to the EY report as well in saying that many are colloquially using terms like “AI audit” or “AI assurance,” to refer to a variety of different types of engagements and assessments. The report noted that some of the services described as assurance services are performed by entities, such as technology consultancies or internal audit teams, that may not follow the same professional standards as assurance engagements performed by CPAs.

The report clarified that what they mean is an engagement in which an assurance practitioner designs and performs procedures to obtain sufficient appropriate evidence, based on the practitioner’s consideration of risk and materiality, in order to express an opinion or conclusion about the subject matter in the form of an assurance report. The two organizations see great opportunity in this area, though not without challenges.

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Accounting

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

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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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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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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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