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US Securities and Exchange Commission

XBRL Helps Enforcement Activities – SEC Report

Maureen Leddy  

· 5 minute read

Maureen Leddy  

· 5 minute read

Machine-readable data has enabled the Securities and Exchange Commission’s (SEC) enforcement division to more efficiently analyze individual companies’ disclosures and accounting practices and perform more sophisticated analyses of disclosures across wide-ranging cross-sections of companies.

“Both types of analyses, together with traditional investigative tools, have resulted in the filing of actions against issuers and related individuals alleging various types of misconduct that violated the federal securities laws,” the SEC said in a June 2024 report to Congress.

The SEC requires companies to tag certain disclosures, including financial statements, using eXtensible Business Reporting Language (XBRL).

Enforcement staff are able to review the financial data of thousands of public companies to detect indications of earnings management or other types of financial misconduct. And it resulted in charges against six public companies and several related individuals in the last four years for violations of the securities laws for engaging in certain practices intended to artificially meet or exceed consensus earnings-per-share estimates.

Without machine-readable data, the SEC said it would have been “significantly more difficult” to detect and pursue alleged violations.

“Enforcement utilized risk-based data analytics to uncover potential accounting-related disclosure irregularities caused by, among other things, earnings management practices,” the report states.

The enforcement division analyzed machine-readable data of one other action brought in 2023. The case was against a Colombian conglomerate for violations of the Foreign Corrupt Practices Act.

“In the course of performing background due diligence, Enforcement staff reviewed financial statements and notes and was able to view period-over-period changes more efficiently due to the machine readability of the data,” the SEC said.

Costs and Benefits

XBRL has reduced information processing costs, made stock prices more information, and reduced market inefficiencies and risks.

Machine-readability has also enhanced market competition by reducing insider advantages.

“The reduction in information processing costs has heightened monitoring of issuers by investors and other external parties (e.g., financial analysts, press) which often helps to inform investors and markets,” the report states. “Greater monitoring has driven firms to provide more quantitative disclosure and report earnings in a more consistent manner.”

The SEC believes that such benefits will likely increase over time as tools for using machine-readable data become more widely available.

It’s not only users who benefit from XBRL. Some companies paid less in audit fees while increasingly timeliness of audit and financial reports.

In addition, companies experienced higher liquidity, lower cost of capital, higher return on investment, and improved performance benchmarking and acquisition analysis.

The SEC has also been able to analyze large amounts of information to better perform risk assessment, rulemaking, and enforcement activities.

Issuers can check for certain errors with freely usable technical validation rules before submitting XBRL data. This in turn helps streamline the compliance process by reducing SEC staff time that would otherwise be spent communicating to companies about technical errors.

On the flip side, there are costs.

Companies either incur compliance costs or pay a third-party tagging service provider to include XBRL tags.

Costs for new tagging requirements tend to be higher but decline as companies gain experience and develop new tools to comply with XBRL requirements.

Costs vary.

In 2023, the SEC estimated that smaller companies typically pay between $1,500 and $5,000 per year for third-party structured data compliance services and/or software. Larger companies typically pay between $5,000 and $30,000 per year.

The commission also incurs costs to develop taxonomies and schemas for new structured disclosures and establish the infrastructure to intake, validate, publish, and use structured data. Such costs can vary based on the volume, complexity, and novelty of new structured disclosure requirements.

 

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