May 2019

Welcome to the “post-busy season” release of Ai Auditor, just in time for your Not-for-Profit audit engagements! Our new release of Ai Auditor includes a host of new features that enable more use cases, such as Accountant Reviews. This release includes several great features including:

Libraries: Contains all the business logic needed to perform analysis within a particular industry or market and allows you to customize analysis based on industry types with different ratios, filters, and Control Points.

Ratio Builder: Enables you to build and save ratios that are relevant to your clients and industry. Analyzing finances with these custom ratios can help you identify trends and other data that inform essential business decisions.

Not-for-profit: We have introduced not-for-profit (NFP) General Ledger analysis support, allowing you to perform financial analysis for NFPs with and without a fund-based structure (fund accounting).

Annotations and exporting analytics: Allows you to document and store insights gained during the planning of an audit or an analytical review to create supplementary documentation without the need for other tools.

Let’s dive deeper into each area.

Libraries

Do you make use of specific filters when you perform an audit for a healthcare client vs. a construction engagement? Do you have particular ratios that you track during the planning phase for different industries? Do you wish you could customize Ai Auditor based on an industry type with different ratios?

In this release, we’re introducing the concept of a Library. Libraries help you tailor the work done in Ai Auditor based on the industry that your client operates in. Libraries are used to manage and maintain the financial ratios needed for a specific type of analysis. In our next release, the ability to change filters and Control Points will be added to Libraries to simplify the selection of settings for different types of analysis. We’re making sure that you’re always doing the most relevant work possible so that you can differentiate your firm and drive profits. Libraries also allow you to reuse configurations across different users and departments within your firm.

An Administrator can create a new Library under the Libraries tab in the Admin section of Ai Auditor. Creating a library allows admins to define their own custom ratios, filters, or Control Point weighting so that you’re always seeing the right information.

To get you started, we’ve released two new NFP libraries along with the for-profit library, our original analysis type.When creating a new engagement, there is now a selection available in the engagement creation section where you can choose from the available Libraries. Choosing a Library selects the financial ratios that you’ll see in your analysis.You can construct and add ratios to Libraries. In this release, filters and Control Points will be read-only. In a future release, filters and Control Points will be included in Libraries for additions and configuration.

Through Libraries, you will find the right information at the right time, and this is a big step forward in helping you complete your work faster and with greater transparency.

Ratio builder

We recognized that the ratios presented were not always the most relevant for your client, and in many cases, alternate ratios or different types of calculations were required. Administrators now have the ability to define new ratios at the Library level. If, for example, the ratio of “Staff cost to Revenue” is particularly important for your client, we want to make sure that information is always at your fingertips.

When creating a ratio, users can select from any account or account level within the account structure, as well as specify different balance amounts such as the closing balance, opening balance, or the monthly movement. They can string together multiple accounts to create complex ratios, which can be saved for reuse in the Library.

Each custom ratio, as illustrated in the image below, contains a ratio name, a category (i.e., Activity, Profitability) and numerator and denominator for the ratio formula. Account amounts, specific values, and constants within the ratio builder can be included in ratio formulas. These values are displayed to the user for selection in the Add drop-down.When an engagement is created, a Library is selected from the list of available Libraries. The ratios corresponding to the Library, are made available on the Trending dashboard.

Not-for-profit support

NFP organizations are often complex, with restrictions in place that dictate the way they can spend and the way they handle income streams. NFPs can often be risky entities to audit and their books can be complicated and sometimes messy.

To support NFP analysis, we’ve updated our account grouping hierarchy to include NFP concepts such as contributions, pledges, grants, funds, and restricted and unrestricted net assets. You can leverage the new NFP-augmented account grouping through the selection of supplied Libraries.

To complement NFP analysis, we have added specific NFP-based ratios such as Liquid Funds Amount, and Defensive Interval, along with many more. With the new Ratio Builder feature, you’re also free to add your own as well.

With NFP analysis, we’ve also developed new Control Points to support specific types of industry analysis for NFP.

In this release, we’re providing two new libraries for NFP.

NFP Library

The NFP Library has an updated account grouping structure to suit NFP concepts such as contributions, grants, and new ratios specific to NFP, such as Operating Reserve, Change in Net Assets, and Operating Margin. We’ve also added a new Control Point that looks for abnormal amounts of expense activity.

NFP-Fund Library

The NFP-Fund Library supports fund-based accounting. If your client uses funds to separate accounts for tracking and reporting purposes, this is the Library to use.

This Library comes with an updated account grouping structure to suit NFP and fund concepts. When you create an engagement, you can choose this Library and upload an augmented Chart of Accounts (COA). For the fund-based NFP analysis, you are required to include a Fund ID and optional Fund Description column in your COA file.

Note that for a Wolters Kluwer CCH fund-based NFP analysis, Fund ID details will be ingested automatically from the CCH General Ledger file and do not need to be added to the COA file. When ingesting a CCH General Ledger file, Fund ID details will be pulled from the “Group” code in the CCH General Ledger file.

For fund-based NFP analysis, we’ve introduced several new Control Points, such as Fund Expense Flurry, Interfund Transfer, and Split Expense that offer quick and easy ways for you to identify transactions of interest in an NFP’s General Ledger.

Annotations and analytics export

This feature provides the ability for you to document and store insights observed during audit planning or analytical review to assist in getting that information into audit documentation without recreating in other tools.

As you are exploring trends and ratios, you can add annotations to each graph under the Trending tab. Annotations, graphs, and the underlying data can then be exported into Microsoft Excel for inclusion in final reports and papers.

In addition to having a list of annotations on each graph on the Trending tab, you can view a list of all annotations on the Annotations tab and filter by account to find all annotations related to an account as well as the corresponding charts and visualizations.

Conclusion

This release is driven by our philosophy that it’s important for auditors and accountants to both customize and automate what they are doing. By giving you the proper tools to create and organize, we’re setting the foundations for even more powerful features in the future!

Why audit sampling doesn’t work

In Daniel Kahneman’s seminal work, “Thinking, Fast and Slow”, the author deals with problems related to “the law of small numbers,” the set of assumptions underlying prevailing statistical sampling techniques.

People have erroneous intuitions about the laws of chance. In particular, they regard a sample randomly drawn from a population as highly representative, that is, similar to the population in all essential characteristics. The prevalence of this belief and its unfortunate consequences for the audit and assurance business are the countless high-profile audit failures. The mounting issues related to outdated standards and problems related to transparency and independence have prompted regulators to go as far as tabling legislation for the break-up of the dominant Big Four firms.

Kahneman makes the point that we’ve known for a long time: The results of large samples deserve more trust than smaller samples. Even people with limited statistical knowledge are intuitively familiar with this law of large numbers but due to human bias, judgmental heuristics and various cognitive filters, we jump to problematic conclusions/interpretations:

Humans are not good intuitive statisticians. For an audit professional, sampling variation is not a curiosity, but rather it’s a nuisance and a costly obstacle that turns the undertaking of every audit engagement into a risky gamble.

There’s a strong natural bias towards believing that small samples closely resemble the population from which they are drawn. As humans, we are prone to exaggerate the consistency and coherence of what we see. The exaggerated faith of auditors in what can be learned from a few observations is closely related to the halo effect. The sense we often get is that we understand a problem or person or situation when we actually know very little.

This is relevant for auditors because our predisposition for causal thinking exposes us to serious mistakes in evaluating the randomness of a truly random event. This human instinct and associative cognitive machinery seeks simple cause and effect relationships. The widespread misunderstanding of randomness sometimes has significant consequences.

The difficulty we have with statistical irregularities is that they call for a different approach. Instead of focusing on how the event came to be, the statistical view relates to what could have happened instead. Nothing, in particular, caused it to be what it is – chance selected from among its alternatives.

An example shared by Kahneman illustrates the ease with which people see patterns where none exist. During the intensive rocket bombing of London in World War II, it was generally believed that the bombing could not be random because a map of hits revealed conspicuous gaps. Some suspected that German spies were located in the unharmed areas. Careful statistical analysis revealed that the distribution of hits was typical of a random process and typical as well in evoking a strong impression that it was not random. “To the untrained eye,” the author remarks, “randomness appears as regularity or tendency to cluster.” The human psyche is rife with bias and errors in calculation, that have meaningful consequences in our work and lives. Algorithmic and computational tools like MindBridge Ai Auditor stand to improve the human ability to make better and less biased decisions.

Minimizing risk exposure

In Kahneman’s article “Belief in the Law of Small Numbers,” it was explained that intuitions about random sampling appeared to satisfy the law of small numbers, which asserts that the law of large numbers applies to small numbers as well. It also included a strongly-worded recommendation “that professionals regard their statistical intuitions with proper suspicion and replace impression formation by computation wherever possible”. As an example, Kahneman points out that professionals commonly choose samples so small that they expose themselves to a 50% risk of failing to confirm their true hypothesis. A coin toss.

A plausible explanation is that decisions about sample size reflect prevalent intuitive misconceptions of the extent of sampling variation. Technology such as machine learning and pattern recognition are removing this bias to the enormous benefit of practitioners currently at the mercy of mere sampling luck to find what is important.

Thanks to recent advances in cognitive psychology, we can now see that the law of small numbers is part of two larger stories about the workings of the human mind:

Exaggerated faith in small numbers is only one example of a more general illusion – we pay more attention to the content of messages than to information about their reliability. As a result, we end up with a view of the world around us that is simpler and more coherent than the data justifies. Jumping to conclusions is a safer sport in the world of our imaginations than it is in reality.

Statistics produce many observations that appear to beg for a causal explanation but do not lend themselves to such an explanation. Many facts of the world are due to chance including accidents of sampling. Causal explanations of chance events are inevitably wrong.

We are at an important crossroads where we must reconsider traditional approaches like audit sampling in the context of the incredible technology that is now available. For companies that are struggling to interact with huge volumes of digital transactions, detect risk, and extract meaningful insights, MindBridge Ai Auditor is an elegant and powerful solution.

New Ai Auditor release: May 2019