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Accounting

Fieldguide launches AI agent to automate audit testing

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1 year ago

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Advisory and audit solutions provider Fieldguide released Field Agents for Financial Audits, which comes with an agentic AI “Audit Testing Agent” to automatically execute the testing workflow end-to-end. 

Specifically, the new Audit Testing Agent automates the process of matching client evidence to samples, extracting and validating key data from documents, and annotating and documenting test results. The Audit Testing Agent supports a wide variety of document-based audit tests, including revenue cut-off, expense verification, unrecorded liability testing, and fixed asset additions. 

It is being rolled out as an enhanced feature of Fieldguide’s existing audit platform, meaning current Fieldguide customers can now access its capabilities. Fieldguide CEO Jin Chang, in an interview, described the new offering as a true end-to-end audit solution that encompasses the entire engagement lifecycle. He contrasted it with similar products in the market, which he said are more like point solutions that handle only one or two steps in the process or are meant for very specific applications like invoice testing. 

Fieldguide booth

“When we talk about agents, we think of agents as a more holistic, multistep workflow approach,” he said. “Our argument is actually that many solutions in the market are not quite agentic. They’re more [like the] AI workflows that Fieldguide has already been building.”

He noted that many solutions will surface discrepancies and possibly make suggestions for manual adjustment. In contrast, he said, Fieldguide’s new AI will go beyond these steps and do things like suggest follow-up questions to clients, draft the communication, evaluate the client response, perform a quality check on the new evidence provided by them, and “connect the dots back to what the auditor is testing for.” 

“By the time the response gets back to the audit team, Fieldguide AI has already pre-tested for quality after evidence and responses come back [to them]. Our agents will test again, document the results, and ultimately the results that are documented flow all the way through to the end financial statement reports,” said Chang. 

He noted that this approach still retains a human in the loop philosophy, which means it’s not actually initiating the client communications with no supervision. While theoretically it could act much more on its own, he said CPA firms are not yet comfortable with that level of independent action from their tools. He contrasted this with other industries, such as software development, where agents are being built with a significantly higher degree of autonomy. Still, this does not mean the AI sits idle waiting for the human to interact with it. Even with this more controlled approach, the agents are still performing some tasks independently. 

“Fieldguide field agents can be autonomous at a very extreme end,” said Chang. “However we need to make sure to meet CPA firms where they are in their AI transformation journey. … What we found is that current levels of comfort in the industry [necessitates] a human in the loop approach where our agents are suggesting next steps and doing proactive analysis. For example when the client uploads evidence, our agents are [performing this analysis without] waiting for the auditor to check. I would say we are about halfway in the journey of more full autonomy, mostly because the level of comfort in the industry is at this current place.” 

Understanding that audit methodologies can vary greatly, the solution sports a high level of customization at multiple levels. This includes the ability for users to set their own materiality thresholds along with their own risk preferences and other best practices. Once set, the AI will use reinforcement learning to better understand how the auditor does things and match itself to their habits. 

“We have customization at every level: firm, practice, partner and down to per engagement preferences too, because we found that even with the same partner, two different clients, he or she may prefer a different way of doing things too,” said Chang. “So what we have incorporated is reinforcement learning at the engagement level, at the audit level, so that the client specific preferences continue on a year to year basis.”

Chang said he is “very confident” in the quality of the AI’s outputs, saying they had to design with quality in mind: while consistent accuracy is important for everyone, it is “non-negotiable” for audit professionals. This quality is at least partially driven by what he said was a proprietary evaluation framework that generally involves a series of specialized LLMs monitoring the outputs of the primary LLM for errors and exceptions it may have missed. Using this framework as a check on accuracy, Chang said the AI has been able to not only perform tests much faster than humans, but it has also been able to find errors that human teams made in previous audits. 

“Fieldguide’s goal is to enhance the quality of audits. We want to help CPA firm partners sleep better at night too, knowing that their audit quality is market leading, not just [producing] efficiencies at the margins. We take a lot of pride in the quality of our AI outputs. We actually would love to see other AI players in the space care more about quality, not just speed. We think that’s just better for the market,” he said. 

While some developers take the approach of having the LLM simply interpret and communicate the calculations made by more deterministic AIs, Fieldguide has the LLMs themselves doing the work, with the specific task matched to the model best equipped to perform it. By giving them access to the right tools, said Chang, LLMs can carry out a wide variety of tasks on their own. 

“Based on our evaluations, certain LLMs tend to be better at math and other very deterministic use cases, whereas other LLMs are better at creativity or understanding documents or images and so on. I will note that anyone who makes blanket statements around LLMs not being good at one particular thing, I would argue, is not doing a proper evaluation across other LLMs,” he said. 

In general, client data is encrypted and stored in Fieldguide’s secure AWS environment. In some cases, when working with very large firms, they will work through their own cloud infrastructure instead, but Chang noted this is more of a premium enterprise service for international firms with global mandates.

Chang added that Fieldguide is ISO 27001 certified, completes annual SOC 2 reports, and will soon be ISO 42001 certified as well. 

He estimated that, for a mid-sized firm of 100 professionals, implementation time would be between three to four weeks; for a larger firm it might be between three to nine months, depending on the scale of the rollout. 

Pricing is generally per-engagement, as the intention is to help CPA firms be more efficient. He argued that per-seat pricing disincentivizes efficiency, as the vendor makes more money the more people use the product. The purpose of this new solution, said Chang, is to enable firms to grow more without having to hire more, a goal that would be at odds with a per-seat pricing plan. 

“A lot of CPA firms who’ve been using our generative AI features the last several years are now reaching a point where they could use another step change in human productivity and quality. … The firms upgrading to our Field Agent solution can grow the top line without necessarily growing headcount one to one,” he said. “Our goal is to help CPA firms create nonlinear growth with revenue compared to headcount.” 

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Accounting

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

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1 day ago

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August 25, 2026

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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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2 weeks ago

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August 15, 2026

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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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2 weeks ago

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August 9, 2026

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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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