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AI in advisory: Valuation | Accounting Today

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Artificial intelligence has brought new efficiency and productivity to the valuation field, particularly where it concerns data entry, processing and analysis, which is a major part of the process, as well as generating the reports to explain what the data says. Lari Masten, who heads valuation advisory firm Masten Valuation, said that working the technology into her own processes has saved countless hours, turning tasks that were once an interminable slog into relatively quick jobs that can be completed in minutes. 

One of the biggest use cases in the valuation world, said Masten, is data entry and processing. Valuation tends to require a lot of data inputs that can take hours or even days to complete, but recent AI advances have allowed her to sort through literally thousands of documents to find data patterns. This not only aids her own insights but also thwarts those who want to hide those insights in massive piles of unstructured data. 

“Recognizing patterns [means] you can dump a ton of PDFs into a model and it can quickly summarize what is going on and put things in order a lot of times. I do a ton of litigation, and it’s so helpful there because for valuation purposes generally it’s not somebody who wants somebody looking at all their financial records and it comes in a big old pile, and so it helps organize it — ‘Here’s 4,000 pages; organize it by date and by financial record first and then underlying quantitative data second, etc.,'” said Masten. 

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Her firm also uses AI for coding support when creating new macros, some of them quite complex and developed for specific clients for evaluation purposes. Instead of having to constantly test and retest an ineffective macro, professionals now can describe what it is they need to do and why, and the AI can provide assistance. 

“We’re not having to go out to Microsoft or Google and ask how to do a particular task. We can just go to one place and not be distracted by it,” she said. 

But while these might seem like purely quantitative processes, Masten said there are uniquely qualitative elements that make it difficult to imagine AI ever completely taking her place, saying professional valuation is both a science and an art. While valuation involves a lot of calculations, what exactly is calculated, and how, can come down to the holistic judgment of the professional. This has been the case, she said, even before AI came onto the scene. 

(See our feature story, “Staying ahead of AI.”)

“For decades, valuation has had software out there. You can dump a bunch of numbers in there and churn, but it doesn’t mean that it gives you a good output. I don’t have confidence that if I put in a bunch of information into a tool that it’s going to be able to understand how Lari Masten would reason through and solve that problem. It can do the math, but did it do the math in the way that when I do valuations? When anybody does a valuation, there’s three or four dozen decisions that they make and each decision gets them to a different place,” she said. 

There are reasons she does the things she does that a computer can’t necessarily understand at this point in time. While the calculations can be automated, according to Masten, the thinking behind them cannot, particularly where it concerns the ultimate conclusion of a valuation engagement. She has yet to find an AI model that can do that. 

“They’ll say, ‘Here’s three ways to solve it’ but they don’t understand if this one is more reasonable than that one. If you work on it you can automate a lot of portions … but the bigger picture of, what is the problem I’m having to solve? What standard of value do I have to follow based on that problem? How is the valuation date going to make a difference? What was known or knowable on a date? AI is not going to be able to really sift through that depending on what the inputs have been already. It’ll just pull information and it may not know when something became known or knowable, so there’s that professional judgment. The good reasoning, the backbone really behind any valuation, can’t be automated,” she said. 

Overall, she is supportive of AI and believes it will be of great benefit to the profession, but noted that there are some cons, particularly the need to vet information and not automatically trust what the AI says. Further, she expressed concern that even though AI cannot replace a valuation expert, professionals over time might lose some of those inherent human qualities that prevent this from happening now. 

(Read more: AI in advisory: What work is at risk?)

“There’s some measure of responsibility on the valuation people to go in and make sure that they’re vetting that information, that they’re still applying their logic, overlaying their skills, knowledge, expertise, training, that kind of stuff because [no matter] how great it is as a tool, it can’t use that logic that we have, it’s not a replacement for the interactive qualitative piece that the valuation analyst knows and tie that necessarily to the quantitative art that it does,” she said. 

This ties into communicating to clients the value of a human valuation expert: Yes, a computer can crunch the numbers, but that’s not all there is to an engagement, and not even necessarily why someone might hire a human professional in the first place. 

“It’s not [just] how to do valuation. The value ad is that you understand the problem,” she said. 

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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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