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Study finds the more efficient the AI, the more complex its implementation

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The efficient way for accounting firms to integrate generative AI into their workflow is through robotic process automation that interfaces directly with the model’s application programming interface, though this method also requires the most expertise to implement and maintain.

This is the conclusion of a recent paper published in the American Accounting Association’s Journal of Emerging Technologies in Accounting, authored by Rutgers University professors  Huaxia Li and Miklos A. Vasarhelyi. The paper presented a general analysis of how accounting firms deploy large language models (e.g. ChatGPT, Claude, Gemini, etc.), and the pros and cons of each approach. Overall, it appears that more complex tasks are best performed by more complex deployment methods, which tend to be more difficult to use. Conversely, simpler deployments are better suited to simpler tasks but are much less efficient.

The paper specifically named four different ways firms deploy generative AI. 

The most straightforward way to do so is through a user interface with visual and interactive elements–picture ChatGPT’s web interface as an example. The paper said this method is most accessible for accounting researchers and practitioners seeking to implement LLMs, as it simply requires an internet-connected computer. It is also the cheapest in terms of access cost. At the same time, it is the least scalable and customizable of all the options and the slowest as well due to token limitations. This in mind, the study’s authors said this method is best used for client engagement and consultation, basic financial analysis and reporting and basic compliance checking. 

The second is through connecting to the model directly via an API, a type of software interface enabling computer programs to communicate with each other, enabling direct passage of data. Firms can leverage an API to establish connections between their local applications/systems and the LLM service, enabling data interaction between them. This API approach can be integrated into existing workflows without significantly altering their structure, is well suited for scalable processing and allows for a greater degree of parameter setting and customization. At the same time, deployment is more complex, requiring skilled personnel to pull it off. Another limitation is the potential incompatibility of the existing workflow with API connections. The authors said some accounting tasks that benefit most from the API approach include basic financial data extraction, transaction classification and verification, and basic fraud detection. 

The third is using RPA to interact directly with a traditional user interface. This allows for batch querying that the user interface method alone cannot accommodate, and is easier to integrate than the API method alone as RPA can mimic human interactions and so even if the existing system does not support underlying programming-level interaction, RPA can still connect it with the model’s user interface to enable automatic querying. Additionally, the UI-RPA method can also be combined with manual efforts that require human judgment. However, the setup is even more complex than the API method alone, and the maintenance process will also require skilled personnel who can update the bots based on changes in the user interface and the working process. Further, not every system integrates with RPA, and introducing new software might create additional privacy and cybersecurity issues, especially for accounting tasks. The authors said UI-RPA is suitable for accounting tasks such as expense management and auditing, asset management and depreciation scheduling, and budgeting and forecasting that require interaction between LLM and local systems.

The fourth is using RPA to interact with the API connected to the large language model. This is the most in-depth integration a firm could have with existing workflows, and the paper said this method maximizes the efficiency of implementing LLMs in the accounting domain. It is more efficient than even the RPA to user interface method as RPA enables the process to robotically collect raw data from existing systems by recognizing graphical-level elements and inputting them into the LLM via the API to achieve efficient queries. After the LLM’s processing, the bot can automatically retrieve the output and transmit it back to the internal systems. However, this method has all the same problems of the RPA to user interface method, but is even more difficult to set up and maintain. In general, the authors said the best use for this method is systematic financial data extraction and analysis, regulatory compliance and reporting, and trail analysis and fraud detection.

The paper found this method is the most efficient in terms of the time it takes to extract 500 unstructured financial statements. The User Interface method alone took 1,800 minutes; the API method alone took 142 minutes; the combination of user interface plus RPA took 67 minutes; and the API plus RPA approach took 42 minutes. 

In terms of pure access costs, processing those 500 financial statements was just 83 cents through either the user interface or user interface plus RPA method versus $18 for the API and API plus RPA methods. However, given the time it takes to perform this task, the pure user interface method wound up being most expensive, as researchers added $52 in labor costs to those 83 cents. The API method alone, when accounting for labor costs, was the second most expensive, as the $18 access cost was combined with $31.25 in labor costs. 

All this in mind, the researchers concluded that the API plus RPA method was the most efficient in terms of both time and money. 

“The study finds that currently, the API-RPA is the most efficient method for large-scale accounting tasks. On the other hand, the API and API-RPA approaches are the most expensive methods to apply under the current price rate of GPT4 API,” said the paper. 

However, researchers warned that the discussions of each method are based on the current level of technological development and cost. 

“Some limitations might be overcome in the future with the adoption of new models. Additionally, the costs associated with each approach might change based on computing costs and market demand. Further research is needed to discuss additional application methods and cost-benefit models based on future developments of LLMs,” said the paper. 

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Accounting

Mandatory ESG Reporting Standards Demand Standardized Non-Financial Audit Trails

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Mandatory ESG Reporting Standards Demand Standardized Non-Financial Audit Trails

Corporate accounting departments face an expanded regulatory mandate as mandatory sustainability and Environmental, Social, and Governance (ESG) reporting frameworks take full effect internationally. Governed by the European Union’s Corporate Sustainability Reporting Directive (CSRD) and the International Sustainability Standards Board (ISSB) IFRS S1 and S2 standards, enterprise financial controllers are now legally required to track, verify, and report non-financial data with the same internal controls and auditability as traditional financial statements.

The expansion shifts ESG compliance

This regulatory expansion shifts ESG compliance from marketing departments to corporate accounting offices. Financial managers are now responsible for gathering, consolidating, and verifying carbon emissions metrics, supply chain labor conditions, water usage, and climate risk exposures across multi-tiered corporate structures. These non-financial metrics must be integrated into standardized general ledgers to withstand rigorous third-party audit assurance processes.

To comply with these rigorous reporting mandates, accounting software providers have added dedicated ESG modules designed to aggregate data from IoT sensors, utility platforms, and vendor management systems. Controllers are implementing internal control frameworks—modeled after traditional COSO frameworks—to ensure the completeness, accuracy, and consistency of sustainability disclosures, protecting organizations against greenwashing penalties and litigation risks.

The transition requires significant cross-functional collaboration between accounting teams, legal counsel, and operational directors. Accounting professionals are expanding their technical expertise beyond financial ledgers to master carbon accounting methodologies, lifecycle assessment standards, and non-financial data governance protocols, fundamentally expanding the role of the modern corporate accountant.

Why This Information Matters
Mandatory ESG disclosures require companies to treat environmental and social metrics as audited financial records. Executives, accountants, and board members must institute formal tracking and assurance processes to satisfy legal mandates, maintain investor confidence, and mitigate regulatory non-compliance risks.

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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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