SEC To Utilize Data Analytics To Identify Accounting Irregularities
February 5th, 2013
Data is valuable. The question is once you have it, how can you use it? Data analytics uses processes to extract and evaluate information from large data sets and identify potential areas of risk, fraud, or error. Among the often used data analytics activities is benchmarking or trending of data. Developing these strategies is part of the SEC’s current plan to combat accounting irregularities.
As the repository for a vast amount of financial information from a wide array of companies, the SEC has always had access to a tremendous amount of data. Using data analytics will help them use this information to stratify industries and run comparisons to identify risky accruals, questionable accounting policies and unusual disclosures. Craig Lewis, SEC Chief Economist and Director of the Division of Risk, Strategy, and Financial Innovation calls this undertaking the Accounting Quality Model (“AQM’). In a December 13, 2012 speech, Lewis described AQM as:
“a model that allows us to discern whether a registrant’s financial statements stick out from the pack, while taking into account the contemporaneous attributes of that pack. The goal is to facilitate comparison across firms within their industry while accounting for and illustrating industry differences as well.”
AQM is still in the testing phases, but should be operational in the near future. A key ingredient is the XBRL (eXtensible Business Reporting Language) tags that public companies are required to include in their financial reportings to the SEC. These XBRL tags allow financial statement information to be downloaded directly into spreadsheets and other tools for analysis in the AQM.
An area of analysis that will be a focus of the AQM is distinguishing between discretionary and non-discretionary accruals to identify possible earnings management. Lewis’s speech included the following description of its goals and approach:
“Our Accounting Quality Model extends the traditional approach by allowing discretionary accrual factors to be a part of the estimation. Specifically, we take filings information across all registrants and estimate total accruals as a function of a large set of factors that are proxies for discretionary and non-discretionary components. Further, we decompose the discretionary component into factors that fall into one of two groups: factors that indicate earnings management or factors that induce earnings management. Discretionary accruals are calculated from the model estimates and then used to screen firms that appear to be managing earnings most aggressively….The classification process should be informed by staff experience, intellectual capital, and the substantial accounting literature related to earnings quality and discretionary accruals.”
Lewis also described some of the factors the SEC will take into account in identifying outlier discretionary accruals:
“We can characterize discretionary accruals as different types of risk indicators and risk inducers. Risk indicators are factors that are directly associated with earnings management while risk inducers are factors that are associated with strong firm incentives to manage earnings.
In our model, for example, the choice of accounting policy and firm interactions with independent auditors may be indicative of specific types of earnings management. An accounting policy that could be considered a risk indicator (and consistently measured) would be an accounting policy that results in relatively high reported book earnings, even though ﬁrms simultaneously select alternative tax treatments that minimize taxable income. Another accounting policy risk indicator might be a high proportion of transactions structured as “off-balance sheet.” Although the vast majority of ﬁrms use oﬀ-balance sheet ﬁnancing for legitimate business purposes, many of the largest accounting scandals used oﬀ-balance sheet activities to hide poor ﬁnancial performance. In both instances, the metrics associated with accounting policies can be consistently estimated from filings data and compared to peers. Another risk indicator could be the frequency and types of conflicts with independent auditors, as measured by changes in auditors or delays in the release of financial statements or earnings. Again, these risk indicators could be consistently estimated from filings data and compared to peers.
On the other hand, risk inducers are designed to capture managerial incentives to mask poor absolute or relative performance. For example, a firm may be losing market share or it may be less profitable than its competitors. A firm experiencing performance problems, particularly those it considers transient, may induce a response that inflates current earnings numbers in exchange for lower future earnings.
The factor-based approach is a flexible modeling framework that easily accommodates new modeling factors as we add and delete proxies for potential earnings management. The additional flexibility lets us efficiently respond to model feedback and customize the model to suit different missions within the Commission while allowing for sensitivity to the nuances of those differing goals.”
Like many data analytics projects, there is a risk of false positives. Just because something falls outside industry norms doesn’t guarantee it’s wrong and the raising of a red flag does not necessarily indicate fraud. However the efficient identification of areas worthy of further investigation should provide the SEC with a substantial head start and a basis for a dialogue with the company as to why the difference is occurring.
Although false positives are an important issue, there is conversely no comfort that the AQM can be relied upon to uncover every possible fraud. Despite the risk of wasted time and effort associated with potentially flagging a substantial number of firms that have done nothing wrong, the risk of missing some actual fraudulent activity remains. The SEC acknowledges that back testing against known prior frauds has not resulted in the AQM net catching all the perpetrators; in some cases, those statements now known to be fraudulent fell within what AQM would consider normal accounting policies.
Fulcrum Inquiry performs forensic accounting services.