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AI in advisory services: Forensic accounting

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If artificial intelligence is eroding the base for the analytics-based transactional advisory work (see our feature story), what’s left? Where are the long-term, relationship-driven client-centric advisory services that call for holistic judgment take account of human psychology and emotion? 

Fortunately for accountants, there are a lot to choose from, including the four we’re examining: forensic accounting, valuation services, M&A advisory and estate planning. These are just some of the areas that are seen as relatively safe from disruption by AI and automation — at least for now. 

Forensic accounting, which often involves analyzing huge amounts of financial data and finding the story behind the numbers, might seem at first a natural fit for AI disruption. But look a little closer at what forensic accountants actually do, and it becomes clear that this is only part of story, because while data remains vital to their work, it’s useless without the human element, according to David Zweighaft, a partner with RSZ Forensic Associates, a New York City-based forensic accounting and litigation consulting firm.

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“The ability to explain the nature of a fraud scheme and how it was detected and who was running it and why they did it is something that probably will not be comprehended by AI anytime soon. We’re tasked with answering questions and, at the end of the day, parties also like to know [the why behind the answer],” he said. 

This is not to say that forensic accounting professionals do not use AI. Machine learning-driven models play a vital role in sorting through all the data and picking out possible red flags. Zweighaft said that forensic accountants have long used AI models, sometimes bespoke to the client, to contextualize a dataset so as to more easily spot anomalies and understand the financial damages they could represent. Still, he said it can be a “chicken and the egg” scenario, because those models still need to be validated one way or another. 

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

“You can use AI to build and run the model, and then you have to validate it yourself or you can sweat out the model and then validate it using AI,which I think is very cool. It gives you a lot of latitude in how you want to take advantage of it, and at the end of the day, it’s really up to the practitioner to determine what he or she feels is the most prudent way to approach it,” he said. 

Still, once such models are prepared and validated, they can produce insights that even human professionals might miss. Zweighaft talked about an older case where someone was booking flights first on Delta and then would “dummy up” a second airline voucher for American Airlines. This was before AI analysis was in heavy use, so it was human investigators who eventually realized this person was putting the same ticket number on both documents, which eventually exposed him. Zweighaft said that feeding the flight data into an AI might produce the same sort of finding but faster. 

“Given the same set of circumstances, you could probably feed this into AI and AI would be able to pick these out just with a great deal of precision: ‘This is not a Delta flight number. This flight never occurred.’ [It] could look at the airline flight guides and come up with all of this information far more quickly. So that’s a potential timesaver and that’s something that a human being might miss,” he said. 

Like many practitioners, forensic accountants also use AI for data entry and processing, which before were time-consuming and frustrating, as well as “document interrogation” and analysis. 

“Back in the old days, you used to get stacks and stacks of paper that you had to scan. Now you get reams of PDFs and you have to convert those into machine-readable format. So whether they’re bank or brokerage statements or other structured data sets, doing that used to be very time-consuming and tedious; now you can do multiple terabytes that can be done overnight. It really is amazing,” he said. 

This plays into his larger view of AI as, at best, a “benign tool” that assists forensic accountants with research and data analytics in various parts of the workflow, versus something that could conceivably automate the entire engagement. This is because, while AI is great at handling the data-related aspects of a forensic engagement, it can’t yet handle tasks that rely more on human interaction — such as interrogation. 

“[I’ll] ask ‘So that’s your signature on this document. Would you like to explain that?’ And the physical manifestation of the confession moment is when the person contracts, they hunch down, they take a deep breath. They exhale and they’ll say something like, ‘I was only doing it to keep the company afloat, the medical bills were crushing us and I needed the money, I intended to pay it back.’ You’re not going to get that from someone being interviewed or questioned by a HAL 9000,” he said. 

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

With this in mind, he is confident that what forensic accountants do won’t be replaced by AI, at least in the immediate future. There are just too many squishy human elements that don’t necessarily conform to a cold data analysis. He concedes that maybe one day in the future there could be AIs doing full financial forensics, but he is not sure whether this would be a good thing. 

“What we do as forensic accountants is never going to be replaced. AI will augment, it will support what we do, [but] I don’t know that skepticism can be programmed. And that is going to always be a differentiator when we’re looking at when we’re doing in-person interviews. [For instance], they’ve done great work with detection of dishonesty using video. Is it perfect? I don’t know. Is it going to take the place of me doing admission-seeking interviews? I don’t know. It’s scary to think that you can take the human element out of investigations, but remains to be seen,” he said.

(See how AI is impacting firm services in valuation, estate planning, and M&A.)

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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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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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