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Accounting

Finance-grade GPT-5? Not yet, but get ready

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Leading AI models, like ChatGPT-5, continue to get faster and better. But finance teams still cannot — and should not — trust it to close the books. 

This is not because the models lack intelligence, it’s because they lack the needed context and integrity to truly be “finance-grade.” 

For instance, an AI model may not know that debits must equal credits — always. Or that cash flow from operations has to tie back to net income and working capital. Today’s AI models don’t have finance-native guardrails that recognize journal entries that violate cash flow identities. They lack verifiable finance reasoning graphs that reveal a number’s origination and the logic used to put it there. There are not yet external assurance standards for auditors to meet to sign off on AI-generated narratives. The list goes on.

Sure, AI models can draft plausible entries and smart reports, but they have no inherent sense of whether it broke accounting logic. As such, it can look good, but still be wrong.

In finance, there’s no place for wrong. Every action must be explainable, auditable and defensible. That’s how I define “finance-grade.” And while AI tools are getting faster and better at pieces of the finance team’s work, it still doesn’t make a finance system safe enough for go-it-alone AI.

Pillars of finance

We’ve rebuilt systems before. Think about it: pilots didn’t disappear from cockpits once autopilot arrived. Instead, cockpits were redesigned and the role of the pilot was redefined. Trust in autopilot rose because the entire system — of autopilot and pilot — proved trustworthy. Finance is at that moment now with AI. 

To get to finance-grade AI, I break it down to four key buckets:

  • Control: This entails traceable outputs, enforceable constraints and systems that can be audited. When things can be verified, they can be trusted.
  • Integration: Many companies face fragmented data, disconnected analysis tools, too many spreadsheets and too many manual workflows. AI was not built to jump over such crevasses and work its machine reasoning. “Garbage in equals garbage out” remains true even now that AI is on the scene. You need data that is clean, correctly curated and explainable so AI can integrate with it. 
  • Reliability: The world changes all the time, but so do policies, interest rates, exchange rates and so on. If AI models don’t keep up — and they won’t — the work it did an hour or day ago will no longer be optimal when you pull a trigger. Any automated workflow needs guardrails to allow human intervention. This means stop rules and other red flags that signal need for human oversight. You want intervention before payments are wrongly made or outdated forecasts infuse sales teams’ targets — not just after.
  • Accountability: It needs to be clear who owns decisions. Finance teams, like teams in all industries, are starting to use more AI agents to work autonomously. As they do this in finance, roles for human workers change too. Controllers become control architects. Reviewers look for exceptions. Auditors check systems, not just outputs. Still, it needs to be clear who owns every decision so, if one goes off track, there’s a way to accountability and correction.

 

Planning for the inevitable

While AI is not yet, on its own, “finance-ready,” it will get there. Increased capabilities are already in motion. They’ll arrive even faster once the infrastructure is in place to house them.

In the meantime, finance leaders need to take steps for the short and long term. 

For the quarter ahead, if you want to prove that AI belongs in your finance team, try it on something that causes your team pain and offer relief that scales.

Start with the mundane: chasing receipts, approvals, last-minute clarifications. These are simple tasks that suck time and energy out of highly skilled finance people. Give these tasks to AI agents trained to understand urgency, context and policy. They won’t ask, “Is this right?” but they will ask, “Is this overdue or out of policy?” AI is great at taking action on domains where it can propose before a human approves and domains where logs track every message, action and verification.

Procurement is another likely target for an AI pilot. There’s often a lot of rules around procurement — and a lot of grief for employees to know and follow them. Imagine an intelligent assistant that starts where the employee is — with a natural-language request — and guides them through the procurement process. It figures out whether to raise a purchase order or fund a card. It collects approvals based on pre-set logic. It gives finance visibility before the money moves. The end result is that something gets correctly procured and purchased within policy rules.

By addressing your finance team’s pain points, you’ll engage human employees in the value of having automation make their lives easier and their jobs more fulfilling. Your finance team will love an AI agent that nudges employees for receipts instead of having to do it themselves. As you amass ROI, you’ll also amass employee belief that AI is a worthy colleague. That’s how trust scales.

For the year ahead

Plan bigger and go wider as you consider the year ahead. Be ready for a scenario in which trust in AI builds steadily along with the tool’s capabilities and one in which AI moves really fast and you need to keep up.

With the first one, assume AI adoption will mirror other enterprise technologies. Take the time now to design control environments. Ask audit and risk to give feedback so that, when automation scales, trust does, too. Document everything so you know how to tweak as you go.

With the second one, assume AI reliability leaps ahead of the controls you’ve built into your infrastructure. Prepare now. Get guardrails approved before you need them. This way, when the tech is ready, your system and team will be, too.

Scaling trust, not AI

No AI will ever remove the need for trust. In fact, as machines do more tasks, the trust bar goes even higher. Invest now in things that will build that trust: provenance, constraints, clean data, clear roles, human-in-the-loop intervention. AI will then be finance-ready.

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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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