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When accounting judgments may lead to legal liability

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1 year ago

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At first glance, accounting judgments may appropriately be viewed as routine accounting practices done in the normal course of business: estimates required due to business uncertainty, and assumptions necessary to complete financial reporting obligations. But when such judgments are overly optimistic, unsupported or poorly documented, they can tip into the territory of accounting errors or fraud, leading to restatements, public scrutiny and even regulatory enforcement. And in the current U.S. enforcement climate, the stakes remain as high as ever.

While the Securities and Exchange Commission has, under the new administration, indicated publicly that it may reduce its enforcement focus in areas of ESG and crypto disclosures, its scrutiny of accounting and auditing practices will remain robust. In 2024 alone, the SEC brought more than 45 enforcement actions involving financial misreporting. This pattern suggests that, even amid a broader shift toward deregulation, financial reporting integrity is still very much in the crosshairs, primarily due to concerns that inaccurate financial reporting erodes investor confidence and the market as a whole.

Given the significance of accounting estimates in financial reporting, it’s no surprise that many enforcement actions cite a registrant’s failure to appropriately consider all relevant facts and circumstances that could materially impact key assumptions that form the basis of accounting estimates, or intentionally ignore them. A prime example: In late 2024, United Parcel Service was fined $45 million by the SEC for materially misrepresenting its earnings. The company relied on an external valuation of one of its business units but withheld key information from the consultant. As a result, the unit was grossly overvalued, and UPS avoided recording a goodwill impairment. This case illustrates how selective disclosure, even without overt intent to deceive, can result in significant enforcement and reputational damage.

The judgment-fraud continuum

Management accounting judgments are not inherently problematic — after all, no standard can prescribe treatment for every unique transaction. But it’s when those judgments lack a sound basis, are inconsistently applied from one reporting period to the next, ignore contradictory evidence, or aren’t clearly documented that problems arise.

Case in point 1: Percentage of completion accounting

Consider revenue recognition in long-term contracts, a recurring hotspot in SEC enforcement. U.S. GAAP and IFRS both permit revenue to be recognized based on progress toward completion. This requires assumptions about future costs, contract modifications and the likelihood of contingent income. These assumptions should be reasonable and evidence-based — but our investigations often reveal overly optimistic revenue forecasts or misreporting of costs that can artificially boost profits.

A recent example relates to the AI-enabled robotic company Symbotic Inc., which reported errors related to its revenue recognition practices in 2024 due to material weaknesses in its internal controls that prematurely recognized expenses related to goods and services it was providing to customers under milestone achievements. In addition, the company failed to recognize cost overruns that could not be recovered and should have therefore been written off. Due to Symbotic recognizing revenue under a percentage of completion basis, the recognition of expenses prior to the satisfaction of key milestones resulted in the early recognition of revenue. Symbotic was sued for securities fraud in a class-action lawsuit, following a more than 35% decline in its share price and has announced an ongoing investigation by the SEC. 

This issue also crosses industries and geographies. U.K. oilfield services and engineering company Wood Group plc experienced an over 68% decrease in its share price since it announced in November 2024 that it had identified “inappropriate management pressure and override to maintain previously reported positions” leading to information being withheld from internal auditors. Reports have suggested the company engaged in over-optimistic accounting judgment and a lack of evidence to support assumptions made and positions taken.

Case in point 2: Straightforward accounting estimates

While fraud may be easier to conceal in complex areas of accounting, it can also manifest itself in more straightforward recurring practices. At the end of each reporting period, companies are required to estimate and record accruals for expenses that have been incurred but for which an invoice has not yet been received. Macy’s reported that a single employee had made unsupported or unjustified adjustments to the retailer’s accrual entries to conceal $151 million of small-package delivery expenses over a two-year period from Q4 2022 through Q3 2024. While the SEC has not announced a formal investigation, a securities class action was filed against the company, and the company announced it had initiated a $600,000-plus clawback in executive bonuses.

A company’s response: Getting ahead of the risk

The key takeaway? Judgment-related risks aren’t going away — and neither is regulatory scrutiny. U.S. enforcement bodies may be shifting their focus, but accounting misstatements remain a primary concern. And with the SEC’s emphasis on financial transparency and accurate reporting, businesses can no longer afford to treat accounting judgments as mere technicalities.

Key areas that a company can consider to address the risk of inaccurate or unsupported accounting estimates include:

  • Identify those accounting estimates that are most significant to the business from both a qualitative and quantitative perspective: what estimates and key assumptions have a) the most significant impact on reported results, and/or b) have the greatest element of uncertainty and, therefore, highest probability of being incorrect.
  • Understand the methodology for developing accounting estimates including the availability and reliability of data sources, how such sources are generated, whether there have been adjustments to how the data is compiled from period to period, and whether there are alternative or supplementary sources that can better inform the facts.
  • Stress-test the estimates by assessing the impact of applying alternative assumptions or weightings of information sources. Similarly, conduct regular retrospective testing of historical assumptions relative to actual results to identify how accurate prior estimates were, what factors or assumptions contributed to the accuracy and inaccuracy of prior estimates, and identify amendments and modifications to future estimation processes.
  • Ensure all significant judgments are clearly documented and evidence-based. Regulators and litigation plaintiffs use hindsight to “re-audit” or “recreate” accounting estimates so it’s critical that companies document a complete account of the information available to them at the time, and the assumptions, thoughts and alternatives that were considered when generating accounting estimates. Estimates project future events and will inevitably be incorrect. However, a well documented record that demonstrates the company made a balanced, thorough and good-faith approach to developing its estimates provides a strong mechanism to defend against any scrutiny that may be levied in the future.
  • Ensure that estimates, the processes followed and assumptions made are done in a clear and transparent manner with the company being open to the thoughts, ideas and comments from others within the business, including those outside the accounting function.

Ultimately, sound accounting judgment is not just a matter of technical compliance — it’s central to maintaining stakeholder trust and avoiding costly regulatory action.

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Accounting

AI-Driven Automation and Continuous Accounting Frameworks

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7 hours 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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6 days 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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Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

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1 week ago

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August 5, 2026

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Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.

The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.

In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.

AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.

When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.

Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.

This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.

Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.

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