Does Audited Mean Accurate? How the FDTA Will Improve Municipal Financial Reporting

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The recently passed Financial Data Transparency Act (FDTA) mandates that securities regulators develop machine-readable data standards for issuer disclosures in several securities markets, including municipals. While there is plenty of anxiety in the issuer community about the cost, resource needs, and timeline to develop and implement data standards in the municipal market, there are many benefits for investors, including the ability to access and aggregate disclosure data quickly and accurately across the entire market.

While issuers and investors may disagree on the benefits, we believe all market participants, including issuers, will benefit from one of the less discussed features of machine-readable data standards– higher data quality. This is because of the built-in “business rules” — data and logic checks — used when transforming disclosure documents into machine-readable formats such as Inline XBRL prior to uploading to the regulator’s repository. These data checks do not exist in current document standards for municipal bond disclosure, primarily Adobe’s PDF.

Is there a Data Quality Problem with Municipal Disclosure?

But is there really a data quality problem in municipal disclosure? In short, yes. In monitoring data quality of financial information displayed in the School Improvement Partnership Database, which currently contains approximately 2,400 audited financial statements, we routinely find errors in disclosures of municipal issuers who filed their reports on the Municipal Securities Rulemaking Board (MSRB)’s central disclosure repository, Electronic Municipal Market Access (EMMA). Here’s a sample of the errors we have encountered, most of which will be familiar to municipal analysts and investors who spend a lot of time reviewing municipal disclosure:

• Disclosure type classified incorrectly, for example, a budget classified as an audited financial statement
• Incorrect report date, such as an audited financial statement classified as June 30, 2022, when in fact it’s a prior year’s audited financial statement
• Document classified as belonging to one issuer when it’s in fact another issuer’s document
• Mathematical errors in audited financial reports

The first three types of errors are possible because there are limited ways to check the validity of a PDF document when an issuer or their representative uploads it to EMMA and classifies the document. This article focuses on the fourth type of error because many market participants are unaware of how common it is. It’s not intuitive that audited financial statements would contain errors. Audited financial reports go through multiple layers of checks by a municipality’s finance department, auditors, and finally the municipality’s governing body, which reviews and approves the final audited financial statement.

But reporting data in non-digital format allows basic errors to slip through. For example, data that should sum across columns and rows — like the Governmental Activities fund and Business Type Activities fund on the Statement of Net Position — doesn’t have to when there are no data checks or business rules applied to the report. Furthermore, accounting rules specify certain relationships must hold. For example, Total Assets minus Total Liabilities should equal Total Net Position. Similarly, Gross Capital Assets minus Accumulated Depreciation should equal Net Capital Assets. When these basic accounting rules and mathematical relationships aren’t followed, it can result in overstated or understated assets, liabilities, income, and incorrect covenant calculations.

Some of these errors may be small, easy to fix, or not material to an investor’s decision. But they really shouldn’t happen. Investors should be able to rely on the accuracy of information reported by issuers, and it’s hard for them to determine the materiality of an error they may not know exists, nor should it be their responsibility to double check audited financial information. Issuers also have an interest in reporting accurate information to the market because future capital access is affected by their market reputation, which can be damaged by publishing financial reports with mathematical errors.

Jacqueline Reck, a professor at the University of South Florida, whose research focuses on governmental accounting and auditing, accounting information systems, and the reporting and use of financial information, noted that when financial statements contain errors, there can be implications for agencies that do fiscal monitoring and oversight. “Many municipal entities report their financial statements to a state agency or the federal government. For example, school districts and charter schools may be required to send their audited financial statements to the state’s department of education. Similarly, federal grant recipients are required to file their audits annually. Finance officers who are filing financial statements — whether it’s going to EMMA if they are debt issuers, to a state agency, or the federal government — need to be mindful of the quality of the information they are filing”.

Professor Reck also pointed to potential implications for the financial statements of related entities: “Some of the errors my team reviewed are in the audited financial statements of charter schools that are component units of another entity such as a public school district. When the charter school’s audited financial statements contain errors, they may be carried over to the district’s financials”.

Transforming from PDF to Digital Reveals Data Quality Gap

Before displaying financial data in our database, School Improvement Partnership runs multiple mathematical checks on the accuracy of reported numbers. By transforming audited financial data from PDF into a digital format like Excel, we can conduct data checks on several key schedules such as the Statement of Net Position, Statement of Activities, Statement of Cash Flows, and various schedules in the notes to the financial statements, such as gross and net capital assets, changes in long-term debt, and future minimum lease payments. These checks are based on simple accounting rules and arithmetic. We don’t attempt to make judgements related to accounting’s many areas of subjective criteria such as how to measure the likelihood that a donor will make good on a multiyear pledge.

When we find a mathematical error in an audited financial statement, we generally notify the auditor, management, and/or governing body to verify our findings. Many don’t reply, but many do, and often they correct and reissue the audited financial statement. None have disagreed with our findings that an error existed.

Here’s a small sample of the errors we found in about 50 unique audited financial statements of municipal issuers. These errors were in audits applying both GASB and FASB accounting standards. They are geographically diverse and produced by auditing firms large and small. None were disputed by the auditors, although again, some did not respond.

• Cash flow statement: net increase (decrease) in cash not calculated correctly
• Future minimum lease schedule: enumerated amounts do not sum to reported total
• Net capital assets on the Statement of Net Position don’t match the breakout in the notes
• Statement of Net Position: some Liabilities in Governmental Activities fund not carried over to Total column; Total column not mathematically accurate
• Total Assets minus Total Liabilities does not equal Total Net Position
• Capital Assets schedule does not add up correctly, and opening balance for fiscal year doesn’t match ending balance of prior fiscal year
• Total Liabilities don’t foot to the sum of reported liabilities
• Reported revenues don’t sum correctly to Total Revenues
• Current Liabilities do not sum correctly; Net Position overstated

In one issuer’s case, we found errors in their audited financial statements three years in a row — different errors each time.

Scope of the Data Quality Gap

It’s difficult to quantify the full scope of the problem because the document format used for municipal disclosures — PDF — is an image file, making errors harder to identify. Only by transforming disclosures into digital form first, then checking accuracy against established accounting rules and basic arithmetic equations can one identify errors in disclosure reports. Furthermore, audited financial statements can be very lengthy. For example a large city’s annual comprehensive financial report (ACFR) is often 300–400 pages. It’s not impossible to cross check that much data from a PDF but it’s certainly not practical, it’s hugely time intensive, and shouldn’t be necessary.

In addition, our sample is not comprehensive or scientific. For example, we have not reviewed all the audited financial statements of all municipal issuers. We have not reviewed all historic years (generally we collect the most recent 3 years of audits for each issuer, a rolling sample). We don’t conduct data accuracy checks on all schedules in audited financial statements, just the ones that are included in the 75+ data points we collect from each audit.

As a result, we don’t believe it’s reasonable to extrapolate an error rate for municipal ACFRs. However, given the number of verified errors we find in the relatively small sample of municipal ACFRs we analyze, we do believe it’s fair to surmise that there are many more errors out there, even if most audited financial statements are perfectly accurate.

The inability to measure the scope of the data quality problem in municipal securities is in stark contrast to the corporate securities market, where filings are already machine-readable, and error rates can be calculated and monitored with ease. For example, XBRL US, a nonprofit standards body that is responsible for the most widely used open-source data standard for financial reporting, monitors error rates of corporate filings, encouraging companies to utilize XBRL’s free business rules to their fullest extent. “Although using our business rules isn’t mandatory, we found that as soon as we started publishing error rates for each company, errors started to plummet, as companies saw the benefit of using these quality tools before filing their statements,” said Campbell Pryde, CEO of XBRL US.

How Will Data Standards Help?

The intent of the FDTA in mandating the application of data standards is not to alter the data disclosed by issuers — in fact the law specifically states that it does not create new disclosure requirements (Sec. 5826). Rather, the intent is to render data reported under current rules machine-readable by applying uniform standards to describe data. However, if data contains mathematical inconsistencies, it can’t be unambiguously captured by a machine. For example, if the sum “Total Assets” is incorrectly reported on the financial statement, should the machine capture the reported amount of Total Assets, or the mathematically correct amount? Hence, technology tools that help statement preparers transform documents into machine readable form such as Inline XBRL contain “business rules” that conduct checks on the integrity of the data to reduce the potential for error.

Depending on the type of taxonomy and the document recipient’s requirements, these data checks may or may not result in a hard stop when an error is encountered. In many cases, they produce warnings for statement preparers to review, which may in some cases be overridden. In the US, where certain regulators, including the SEC, already use machine-readable standards for some corporate filings, regulators have typically not taken a hard line with companies uploading filings containing errors, allowing the filing to proceed anyway. However, scrutiny is growing over company filings containing errors because investors and others can easily run the business rules themselves to determine if a company’s filings are accurate.

Examples of the types of data checks that might be included in the business rules for municipal audited financial statements include:

• Sum totals across rows and columns are mathematically correct
• Certain numbers should rarely or never be negative (for example, cash or net capital assets)
• Total Assets minus Total Liabilities equals Total Net Position (including deferred outflows/inflows in the case of GASB)
• Gross and net capital assets are mathematically correct
• Annual schedule of future minimum lease payments sums to reported total

Campbell Pryde believes that machine-readable reporting will make the types of errors we found a thing of the past. “We believe that almost all errors that School Improvement Partnership found in audited financial statements would be caught by the kind of business rules that could be applied to municipal audited financial statements. These types of errors can be caught up-front and corrected before they ever reach investors, regulators, and oversight agencies”. According to Mr. Pryde, XBRL US’s Data Quality Committee (DQC) produces a set of business rules for public companies’ SEC filings, providing the rules for free on their website. XBRL product vendors often build the rules into their platforms so companies can test their filings against them and resolve problems before uploading filings to the SEC’s EDGAR.

Market Confidence Relies on Quality Data

The implementation of machine-readable data standards provides opportunities to improve the quality of municipal disclosure by leveraging accounting rules, logic and mathematical checks to apply a needed quality overlay to audited financial statements and other types of municipal filings. Additionally, many classification errors found on EMMA such as budgets classified as audits or filings with incorrect dates, will be much harder to make if the documents are machine-readable, because the embedded tagging in the document will identify the document type, date, and entity, which could be read by the regulator’s system prior to accepting a filing.

Although the data quality benefits of machine-readable data standards are not discussed as often as other benefits, higher data quality will benefit issuers and investors alike, which should lead to increased investor confidence in the municipal securities market.

Liz Sweeney is Chief Data Consultant for School Improvement Partnership, which specializes in data and transparency in the charter school capital markets. In this role she advises on data strategy, oversees financial data quality, provides subject matter expertise, and conducts market outreach and education. She is also President of Nutshell Associates, LLC.