Audit Archives

A better approach to journal entry testing: Audit analytics automation

May 26, 2022September 7, 2020 by Solon Angel

Internet companies have been driven by data for decades. For instance, Amazon was using basic AI systems over 20 years ago. Netflix, Microsoft, Google and many others have dominated their categories by using a data and algorithms-first approach. Yet when we look at the accounting world, many still believe that data and analytics are a novelty, optional, or separate from the work that they do.

When it comes specifically to journal entry testing, most auditors today have been using antiquated approaches and sampling techniques. Many justify the use of these limited audit risk methods by saying they comply with existing standards. But these standards such as SAS 99, Consideration of Fraud, actually only require auditors to gain an understanding of the business and focus on identifying items that warrant further auditor considerations.

According to SAS 99 or other international standards, there is nothing to discredit the use of advanced methodology and latest AI-powered technologies. In fact, almost 20 years ago, the American Institute of CPAs published the 2003-02 Practice Alert with guidance for the use of analytics. Today, recent advancements in auditing software allow accountants to better evaluate audit risks and deliver pertinent insights to various stakeholders.

The challenges with traditional journal entry testing

Traditionally, accountants had a lot of groundwork to do during an audit risk assessment. First, they would spend a considerable amount of time doing data preparation on usually limited data columns and file sizes. Then, they would try to determine which analytics to apply to the data.

As Enterprise resource planning (ERP) systems grow more complex, not all audit procedures can keep up. Data clipping or manually converting a GL report into an Excel file is known to exclude data or cause errors during the audit process.

Existing script-based data analytics engines are exclusionary based, meaning they extract data as an auditor applies various procedures. This decreases the chance of detecting anomalies and doesn’t allow for a truly comprehensive audit risk assessment. This is why many leading accounting firms, including the Big Four, are moving away from these outdated auditing procedures. These more traditional methods for risk-based journal entry testing cause inherent liability and poor quality.

Using more advanced AI-powered auditing software, an audit team can gain more far-reaching insight. By pinpointing control points, the AI auditing software can identify and learn what’s normal or not and then analyze a wider range of data without inherent exclusions.

3 ways to automate risk-based journal entry testing

1. Start with a data-first approach

Before thinking about which audit tests or procedures to apply, you need to start with the data. This is called a bottom-up approach to audit risk assessment, instead of top-down. The idea is to let the data speak first. Then, you can look for standard procedures and identify any underlying risks.

Seek to get as much information on the system available as possible from your client: GL reports, Charts of Accounts, opening and closing balances, bank statements, as well as the previous year’s data.

With this modern approach, you can leverage historical data in new ways. This can include automatically doing pre-emptive calculations and forecasts to better understand potential audit risks.

For example, MindBridge Ai Auditor automatically generates ratios and forecasts that you can annotate and add to your audit plan, seamlessly.

2. Leverage the community effect

Try to avoid reinventing the wheel and be curious of what automation can accomplish. It is not just about using new auditing technology. Try to understand the definition of risk that is built into the automation. A few AI or cloud accounting software vendors like MindBridge have spent countless hours with industry partners embedding specific risk analysis into their software packages.

Auditors are required to “test the appropriateness of journal entries recorded in the general ledger and other adjustments”. In the past, you would have had to define the procedures yourself. But today, with everyone connected online, communities have emerged around your choice of tools. These communities include other accountants that might have implemented fully automated procedures into their methodology and are eager to contribute best practices and tips with others.

During the Influence 2020 conference, some MindBridge Ai Auditor customers such as Baldwin CPAs and GRF CPAs shared their first-hand experience of using our AI accounting software as well as practical advice for other users.

3. Pay attention to complex transactions

Your clients are not in the business of ensuring the right controls or worrying about anything else other than running their business. They simply don’t anticipate bad behavior, bad actors, or white-collar criminals. It is not enough to just design procedures or automate the classic CAATs-style audit tests. Instead, you can leverage the full power of advanced audit risk assessment techniques such as “Rare Flows” and “Expert score” using powerful AI auditing software. These improve your ability to detect high-risk transactions or the sidestepping of the company’s internal controls.

Some employees, including senior management learn ways to work around a specific control. For example, employees can post numerous smaller journal entries to various departmental general ledgers to circumvent approval processes. This also makes it more difficult for auditors to detect the fraud.

This is where AI can excel and really help you. Rare flows and unusual transaction analysis can help you quickly identify audit risks and conduct a more thorough journal entry testing. After saving time on the previous tasks, you will be able to dig into the data and ask the right questions.

Evolving audit risk assessments and your business

Accountants and auditors are not here just to perform repetitive tasks or follow outdated procedures. The core principle of the profession is to be business advisors to their clients.

By using advanced technology for risk-based journal entry testing, auditors can streamline the auditing process and avoid spending billable hours digging for issues in only one area. Instead of limiting themselves to simply extracting data from a general ledger, they can ask for more reports and more data. This allows them to get a deeper understanding of all the anomalies in client files to perform a more thorough audit risk assessment.

With greater automation in journal entry testing, auditors will be able to get more insights from a larger dataset in minutes, and their clients will notice. That’s because after using AI accounting software in the auditing process, the audit team will be able to ask more relevant questions that lead to smarter business outcomes.

Want to learn more about the benefits of AI auditing software? Read how K·Coe Isom embraces AI accounting technology to gain new insights into their clients’ businesses.

How the new SAS-142 audit evidence standard embraces technology and automation

September 28, 2021July 29, 2020 by Stefano Colaiacovo, CPA, CA

A new audit evidence standard has been released by the American Institute of Certified Public Accountants (AICPA) that includes significant updates around how technology and automation can be leveraged throughout the audit process. Here, we’ll examine this standard and some of the most significant examples of how the AICPA has explicitly considered the applicability of analytics and automation to how audit evidence is gathered and concluded upon.

The Statement on Auditing Standards (SAS) No. 142 Audit Evidence is relevant for private company audits and takes effect for periods ending on or after December 15, 2022.

While the effective date of the guidance allows for lead time for the appropriate methodology changes and technology investment to be contemplated and implemented by firms ahead of calendar 2022 audits, the updates reflect the massive tailwinds of how data analytics and automated tools and techniques are well-positioned as catalysts for the reimagining of the audit life cycle. Furthermore, the potential afforded by these technologies to drive monumental improvements in both quality and effectiveness is only amplified further in today’s remote work environment.

Key concepts around audit evidence

It’s worth revisiting some of the basic principles around audit evidence and the responsibilities of the auditor before discussing how data analytics and automation can be transformative to how evidence is collected and generated.

The new standard clearly defines the auditor’s objective around audit evidence as follows:

“The objective of the auditor is to evaluate information to be used as audit evidence, including the results of audit procedures, to inform the auditor’s overall conclusion about whether sufficient appropriate audit evidence has been obtained.” (SAS No. 142, par 5)

The term audit evidence may conjure up images of stacks of source documents (invoices, purchase orders, cheque stubs, etc.) and detailed documentation of ticking and tying them all together in an Excel spreadsheet. But audit evidence isn’t just the outcome of detailed transaction-level testing, it’s more broad and includes the results of your risk assessment procedures and inquiry, any testing of controls, and the results of both detailed and analytical-based substantive testing (SAS-142, par A44).

In other words, the auditor, in exercising their professional judgement as to whether identified risks are properly responded to, has a wide net of support to consider on balance and weighed together to make that conclusion effectively.

So what type of things influence whether evidence is sufficient and appropriate? This comes down to how much evidence is required to respond to the identified risks of material misstatement, and how relevant and reliable that evidence is. The appendix to the standard specifically includes a number of examples and contemplation of what these key terms mean in practice and some of our takeaways (not exhaustive) include:

What types of factors impact the reliability of audit evidence?
- Source
  - Is the information from an external source, and therefore less susceptible to management bias (SAS-142, par A22)?
- Nature
  - Is the evidence “documentary” vs. provided orally through inquiry?
- The controls over the information and how it’s produced
  - How automated is the process by which data is generated and what is the relative strength of controls that the entity has in place? How is the accuracy and completeness of the information ensured?
- Authenticity
  - Has a specialist been involved in validating certain assumptions?

What types of factors impact the relevance of audit evidence?
- The accounts and assertions it relates to
  - Does the evidence tie directly to identified risks at the assertion level of an account? For example, purchase documents matched to payable transactions right before balance sheet date provides evidence against an early-cutoff risk but not a late-cutoff risk.
- The time period it pertains to
  - Does the evidence relate to the period under audit or specific subsets of that period where risk is relevant?
- Susceptibility to bias
  - How much influence over the information does management have?

These concepts are critical to keep top of mind as we consider the role of data analytics and automation because introducing technology to the audit process doesn’t diminish the auditor’s overall objective and requirement to obtain sufficient and appropriate evidence to support their opinion. Rather, the tests and techniques that we’ll review enable the auditor to more efficiently gather, interpret, and perhaps even generate the evidence that satisfy these criteria.

Facilitating high-quality and data-rich analytical procedures and risk assessment

Let’s consider the following excerpt from the new standard:

A59. Analytical procedures consist of evaluations of financial information through analysis of plausible relationships among both financial and nonfinancial data. Analytical procedures also encompass investigation as necessary of identified fluctuations or relationships that are inconsistent with other relevant information or that differ from expected values by a significant amount. Audit data analytics are techniques that the auditor may use to perform risk assessment procedures…

A60. Use of audit data analytics may enable auditors to identify areas that might represent specific risks relevant to the audit, including the existence of unusual transactions and events, and amounts, ratios, and trends that warrant investigation. An analytical procedure performed using audit data analytics may be used to produce a visualization of transactional detail to assist the auditor in performing risk assessment procedures….

Automated techniques such as the ones described in the guidance can be a very powerful and efficient method to assess relationships across the financial ledger. Having this type of analysis “out-of-the-box” at your fingertips, without detailed scripting or manual data wrangling, promotes efficiencies as well.

Here are a few examples of how the capabilities of MindBridge Ai Auditor align with a technology and data-driven analytical review and risk assessment that the standard explains.

Trend analysis

Our Trending analysis allows you to visually compare how one or more accounts moves over time. This allows you to assess how accounts or financial statement areas that you expect to be correlated (accounts receivable and revenue, revenue and costs of sales, etc.) are indeed tracking consistently. It’s important to note that this analysis is available on a monthly basis and is not just a simple year-over-year comparison. This empowers you to have a more nuanced view of what these relationships look like seasonally and more broadly.

You are also able to layer in filtering of the trends you are seeing, across additional operational dimensions of the financial ledger. For example, if an organization manages it’s P&L by department or region, you can examine how revenue breaks down across one or more of these dimensions with one click.

Ratios

Over 30 critical ratios are automatically calculated by Ai Auditor and the results are visualized on a monthly basis throughout the audit period. How each ratio trends in the current period against prior periods is readily apparent and points of deviation can be flagged for further investigation with your client.

With an appropriate amount of prior period data available, Ai Auditor performs a regression analysis called seasonal autoregressive integrated moving average (SARIMA) to graphically visualize the expected ranges for the ratio in the current period in addition to the trend lines. This is extremely valuable in identifying algorithmic outliers for further audit procedures and input to risk assessment.

Transaction-level analysis

The new standard specifically contemplates how unusual transactions or events in the financial ledger impact risk assessment and this aligns perfectly with Ai Auditor’s core competency of an ensemble-based AI algorithm that runs against every transaction and tags it with a single risk score:

A61. Analytical procedures involve the auditor’s exercise of professional judgment and may be performed manually or by using automated tools and techniques. For example, the auditor may manually scan data to identify significant or unusual items to test, which may include the identification of unusual individual items within account balances or other data through the reading or analysis of entries in transaction listings, subsidiary ledgers, general ledger control accounts, adjusting entries, suspense accounts, reconciliations, and other detailed reports for indications of misstatements that have occurred. The auditor also might use automated tools and techniques to scan an entire population of transactions and identify those transactions meeting the auditor’s criteria for a transaction being unusual…

In Ai Auditor, the ensemble-based algorithm includes over 30 different tests, termed Control Points, which range across rules-based, statistical methods, and machine learning-based techniques. The ensemble specifically includes tests for Unusual Amounts posted to an account, Rare Flows of money between accounts that don’t normally interact, and Outlier Anomalies.

With Ai Auditor, you can visualize the results of these tests in aggregate via dashboarding and drill down to the most granular level of a particular entry to see which Control Points are contributing to a certain score.

Techniques that facilitate highly efficient “dual-purpose” procedures

The new standard includes an illustrative example where a series of audit data analytical techniques are used as both a risk assessment procedure and a substantive procedure:

A46. An auditor may use automated tools and techniques to perform both a risk assessment procedure and a substantive procedure concurrently. As illustrated by the concepts in exhibit A, a properly designed audit data analytic may be used to perform risk assessment procedures and may also provide sufficient appropriate audit evidence to address a risk of material misstatement.

The exhibit being referred to in the passage above is quite compelling and certainly worth a detailed review (beginning at page 42 here). As an extension of the previous discussion around transaction-level risk scoring, assuming that additional considerations are satisfied, such as the effectiveness of controls over how the information is produced and the auditor’s confidence as to the accuracy and completeness of the information, the ability to “profile” transactions into relative risk buckets using an audit data analytic (ADA) routine is explicitly contemplated here.

If the results of that “profiling” can be used to not only to inform risk but also the nature, timing, and extent of further substantive audit procedures, the investment into building and integrating these types of techniques into your methodology could provide significant ROI in terms of execution efficiencies.

Take the first step towards a modern, data-driven technological approach to audit, contact sales@mindbridge.ai.

Assessing audit risk during engagements

May 26, 2022July 22, 2020 by Michael Bottala, CPA

Man using MindBridge to access financial risk

Three ways Ai Auditor strengthens your audit planning

The determination of where audit risks of material misstatement lie is a critical output of the audit planning process. Usually, identifying those risks is based on the auditors understanding of their client and the client’s operating environment. Auditors can now rely on a data-driven approach to better understand that environment. And this will positively impact the nature, timing, and extent of the audit procedures which respond to the identified risks.

Below, we’re exploring three ways that our Ai Auditor solution helps you streamline your audit planning, from start to finish.

How to enhance your audit planning using Ai Auditor:

1. Conduct thorough assessments for better audit planning

Just looking at a balance sheet or income statement at one point in time isn’t enough. Analyzing more financial data during the planning phase allows for a deeper understanding of the client’s operations.

Auditors have long used analytics to help assess a client’s operations. These tools help them gain insights and identify aspects of the entity that were either unknown or unfamiliar to the auditors. These data analytics essentially help them to better assess the risk of material misstatement, as well as provide a basis for designing and implementing responses to the assessed risk.

Working with Ai Auditor, the auditor can select a view of the ending balance or monthly activity. They can also analyze different transactional relationships within the general ledger to ask better questions and make more precise judgement calls.

For example, let’s say an auditor finds out the accounts receivable (AR) has a 10% change from the prior year to this year. The auditor can explore the AR activity and find out if this change was a normal increase or if there was any unusual activity that could indicate a new large customer or purchase at year end.

Another example would be if there was an account that had no significant change from the prior year ending numbers, but the activity was much different. Having more data would provide the auditor with better insight into the client’s operations.

Using Ai Auditor, an audit team can also look at relationships between accounts to identify if there are any unusual patterns. For example, perhaps they’ll notice that the cost of goods sold (COGS) and inventory trends appear to not follow consistent patterns. The auditor can then include a very specific and strategic task in the audit plan — to pinpoint the time when the trend does not follow expectations and investigate further.

2. Quickly identify unusual transactions across all data

The MindBridge Ai auditor solution automatically scores the risk of each transaction using Control Points. These Control Points include tests based on business rules, statistical models, and machine learning to identify the most uncommon and unusual items in the data set.

The machine learning engine in Ai Auditor looks at each unique data set and analyzes the frequency and amounts of the transaction. The engine also explores relationships between the account’s transactions that are being recorded.

Ai Auditor helps automate the analysis by flagging items that just don’t fit typical transaction patterns. It’s then up to the accountant to focus on the most uncommon and unusual items and dig deeper.

For example, Ai Auditor might flag the write-off of inventory because insurance-related payments seem uncommon or unusual. During the audit, the auditor might learn that this was due to a warehouse fire.

Essentially, the platform gives auditors better visibility on these unique circumstances right from the start of the audit. The auditor can then focus on these higher risk transactions, consider the ramifications of the transactions, and understand how those riskier items might impact the financial statements.

3. Retrieve and view transactional breakdown by audit area

Using Ai Auditor, an accountant can filter risks by category. This allows them to breakdown risk by account, branch, program, type of transaction, time, monetary value and more.

With this breakdown, the auditor will gain a better understanding of where relative risk lies across operations. They will also be able to see which control points are being triggered within a specific area and consider how that impacts the overall audit risk.

For example, let’s assume an auditor notices that the accounts payable (AP) entries are triggering a significant amount of risky transactions at year end, specifically in the Southwest branch of the operations. This might indicate cutoff issues. Or, if the sequence gap control point is triggered, perhaps the auditor will assume there are completeness issues.

During audit planning, auditors who think critically about how these control points might factor into the assertions for the various accounts will drive stronger results.

A stronger audit plan leads to stronger audits

A deeper level of critical thinking in the audit planning stage ensures a more efficient and effective audit. Auditors can leverage our MindBridge Ai Auditor solution as a feedback loop to further their understanding of the client’s operations.

Using the AI auditing platform, accountants can then uncover valuable insights to supplement their discussions with management and existing knowledge of the client. Those insights might include uncommon patterns in transactions, abnormal stratifications, unusual relationships between accounts, and breakdowns of trends or ratios. With this information at hand, auditors can ensure a well-planned and successful audit.

Do you think audit analytics make auditors even more relevant? We do. Read this next blog to find out more.

Don’t get left behind: A case for adopting accounting software

December 18, 2020July 14, 2020 by Shaye Thyer, CA

Accounting software trends have impacted the accounting profession in big ways. And in my view, one of the greatest analogies of this impact, and even of the way our team at MindBridge delivers value to our clients, comes from Sam Daish, Head of AI and Data Science at Qrious.

A story of three types of businesses

In his previous role as General Manager of Data Innovation at Xero, Sam addressed a room full of very traditional big-firm accounting partners. During this talk, he described the evolution of manufacturing in the time when electricity was new. He summarized the journeys of three business types:

Those who thought electricity was some strange wizardry and continued on as they always had
Those who tried to adapt their processes around electricity to make things work
Brand new businesses that sprung up native to electricity

Sam continued to tell the story of how manufacturing evolved in the 1880s. Businesses in the first category simply could not compete. They buried their heads in the sand. Their refusal to adapt was largely due to long-held pride in traditional expertise. The second group worked really hard to re-invent efficient processes—to make electricity bend and work around the way they’d always done things. The third set of businesses built operations with electricity at the heart. What happened to them?

The ‘ostriches’ were completely obliterated by the rest of the market
The ‘adapters’ really tried, but many businesses did not survive
The ‘electricity natives’ absolutely consumed the market. They shifted customer expectations and quickly devoured customer relationships that were long-held by large, big-brand traditional businesses that once dominated the industry

The parallels with the accounting industry’s state of flux surrounding technology adoption are profound.

First comes cloud accounting software, then AI accounting software

At one point, there was so much fear, worry, and apprehension about cloud accounting software. Many believed the accounting software would steal jobs from bookkeepers, graduates, and accountants in general. Yet the only ones who have experienced any negative outcome have been those who failed to adopt and adapt. Accounting firms who have embraced cloud accounting software and the client-centricity of the single ledger, and who have assisted their clients in doing the same, are dominating the market. It is not accounting technology replacing accountants – it’s accountants adopting technology that are replacing those accountants who are not.

So what about AI now?

Most would agree that diversification into advisory services is the key to modernizing accounting firms and aligning with client expectations. During Covid-19 times, we have seen a reversion back to the bread-and-butter of compliance for many accountants. What we will see moving forward is the evolution of compliance; it will feel less like putting numbers in a box and filling out forms (as this becomes more and more automated over time) and more like compliance risk mitigation, or ‘compliance advisory’. So for the future-fit compliance and advisory firm, AI accounting software comes to the fore when we ask ourselves: “So you have access to all this real-time data via cloud—what are you doing with it?”

When we look at accounting software trends, the message to support the adoption of AI is like that of cloud: “AI—it’s about task replacement, not human replacement”. The automation and ‘task replacement’ we now enjoy with cloud accounting software is similar to AI accounting software—these technologies are just doing parts of the job which no one likes anyway. For example, we love presenting insights to clients, showcasing our deep expertise of industry, and offering fancy visualizations that break down the complex into a simple picture. But we don’t like entering or churning through the data to get to the insights. So for this, we have AI. In a recent Accounting Today article titled ‘What AI does for accountants’, the author describes three areas in which accountants can leverage AI accounting technology right now:

Invisible accounting to automate reconciliations for clean, timely data
Active insight to drive better decisions
Continuous audit to build trust through better financial protection and control

Stepping towards success with AI

No matter where accounting firms are in their journey towards adopting new accounting software, one thing is clear—businesses need to, at the very least, start looking at the latest advancements in AI and all the advantages it offers, or risk being left behind. Some may be just jumping onto the cloud accounting software train. Others may begin courageously diving into AI. Regardless, there is a necessity for our established industry of accounting professionals to be deliberate about their re-learning journeys when it comes to accounting software. Those who seek to not only survive, but thrive, must ensure that data literacy and conceptual knowledge of what both cloud and AI accounting software can deliver are key to their business strategy moving forward.

How AI and data can power an effective audit plan

September 29, 2024July 2, 2020 by Gena Song, CPA, CA

An effective audit starts with a solid audit plan. While the overall audit strategy and plan can vary between clients, an auditor will usually establish risk assessment procedures and a how-to response for the risk of material misstatement.

The challenge is that sometimes, even the most thorough and comprehensive audit plans can still have gaps. In fact, every auditor understands there will likely always be some degree of uncertainty and unidentified risks before an audit begins. It’s in the initial audit planning stages that an audit team will often ask:

How can we lessen those unknown risks?
Is there an opportunity to confirm initial assessments about the industry or company?
Are there blind spots that we haven’t considered?

This is where machine learning (ML) and artificial intelligence (AI) can help. In this blog, you’ll learn how you can use MindBridge AI to spot risks and shift resources during preliminary engagement activities through each phase of the audit planning process.

Pinpointing audit risks using a data-driven method

Identifying the inherent business risks associated with the company is an important first step in the audit planning process. An auditor must analyze key risk factors such as understanding the industry risks, the company’s business, and any recent changes within the company to determine if and how these considerations will impact the audit plan.

Using Ai Auditor, an audit team can enhance the risk assessment process by retrieving powerful risk insights. That’s because Ai Auditor examines 100% of the company’s transaction data and alerts the team to any anomalies or underlying risks associated with the entity. With detailed data at-hand, the audit team can then move forward with greater confidence in the audit engagement, trusting that the risk assessment is comprehensive and complete.

Ai Auditor can also help the team to identify new risk areas that have might not been flagged in previous audits and include them in their audit plan. Not only does this ensure a well-planned audit, but it also minimizes the potential for duplicating audit procedures later on.

Evaluating the effectiveness of the company’s internal control over financial reporting is another area where using Ai Auditor can be a benefit. Much like traditional testing, the platform automatically identifies control points to spot high-risks transaction data. The auditing team can also adjust these control points and use other capabilities within the platform to recreate traditional control testing models. This data-driven audit method saves the team time while ensuring high levels of accuracy and diligence.

Building an effective audit strategy with Ai Auditor

After initial risk assessments and tests, the auditors will be able to establish an overall audit strategy. This sets the scope, timing, and direction of the audit and guides the development of the audit plan.

For instance, the audit team will derive important conclusions after evaluating the effectiveness of internal control over financial reporting. These will help them decide whether to use control testing, substantive testing, or a combination of both in their audit plan.

When planning the timing of the audit, the team might also consider using Ai Auditor during interim analysis and take advantage of roll-forward capabilities at year-end to ensure a more effective audit.

Considering how much time and resources go into an audit, Ai Auditor can become a force-multiplier for an audit team. The platform provides insights that help them become more efficient as they move through audit planning to engagement completion.

Developing an audit plan with data at your fingertips

As an auditor begins developing and documenting the audit plan, the reporting features within Ai Auditor can help. An auditing team can export powerful graphs and data to support the audit plan regarding details such as the planned nature, timing, and extent of the risk assessment procedures; the planned nature, timing, and extent of tests of controls and substantive procedures; and other planned audit procedures.

The team can also use Ai Auditor to download a single report that details any flagged items and automatically add this report to the audit plan. This ensures the team conducts deeper investigations on those transactions or simply helps to justify why certain samples were selected.

Completing the audit engagement with success

Ai Auditor helps to simplify auditing planning. The platform offers valuable insights and data that help an auditing team streamline risk assessments, build an effective strategy, and outline a comprehensive audit plan. And since an audit team will be able to conduct investigations easier and faster through every phase of the plan’s process, they’ll have more time to offer clients valuable insights and guidance.

Looking for more? Register to access our on-demand webinar titled ‘Riding the Waves of Transformation’ with Tom Hood, CPA.

Improving audit risk assessments with AI-driven analysis of Accounts Receivable and Accounts Payable subledger data

September 29, 2024June 18, 2020 by Stefano Colaiacovo, CPA, CA

Improving audit risk assessment longterm

The cornerstone of well-planned and high-quality audit engagements is a robust risk assessment process. Such a process is critical to identifying risks of material misstatement and their relative significance by providing a fulsome understanding of the entity subject to audit and the environment in which it operates.

The nature and extent of these audit risk assessment procedures will certainly differ from engagement to engagement, reflecting different types of operations, industries, and financial reporting complexities, however preliminary analytical review procedures are a common thread across all audits as a requisite component of the risk assessment process.

Traditional preliminary analytical review procedures

Practically speaking, preliminary analytical review procedures could include any combination of the following (not exhaustive):

Comparing actual financial performance to historical trends and balances
Reviewing actual financial performance (ratios, key financial metrics) against industry benchmarks
Reviewing actual financial performance compared to management forecasts and/or budgets
Performing inquiry of management to ascertain operational drivers for certain trends and patterns in the year-over-year results (i.e., “what’s changed?”)
Examining any material new contractual agreements executed in the period (leases, customer contracts, debt agreements, etc.)

Traditionally, these types of analytical review procedures take place at the level of how the financial statements aggregate the data by account or class of transactions, or perhaps at more granular levels of the chart of accounts. For example, you may compare how gross margin in the current period compares to historical periods or how increases in inventory year-over-year tracks with corresponding movement in the cost of sales accounts. In any case, it is ultimately the general ledger trial balance data and activity detail that underpins this type of review.

With a view towards a robust risk assessment process and obtaining a deep and operationally relevant understanding of your client’s business environment and financial performance, analysis and interrogation of the AR and AP subledger data as a complement to the traditional preliminary analytical review procedures at the financial statement level could be a source of highly valuable context to the results and empower you to conduct a more focused inquiry of your client’s management.

Accounts Receivable & Accounts Payable as critical inputs to audit risk assessment

Visualizing and interrogating subledger data can provide high-value insights and expose “root causes” behind some of the general ledger variances and patterns identified as part of your traditional preliminary analytical review procedures. This empowers you to better pinpoint an assessed risk and tailor your testing approach to most efficiently respond to that assessed risk.

Some examples of how to best leverage subledger information include:

Understanding how certain vendor and customer aged balances trend throughout the year

The aggregate total values of AR and AP at balance sheet dates might be relatively consistent year-over-year but there may be cause for further investigation and inquiry if, for example, the monthly ending balances demonstrate significant volatility throughout the year or seem out of pace with corresponding monthly sales or purchasing trends.

Understanding operational key performance indicators for customer and vendor “health”, and tracking those over the audit period

Tracking basic operational metrics like Days Outstanding and Turnover ratios, for specific vendors, customers, and in total, provides a lens of relative customer “quality” or vendor settlement patterns that may allow for risk to be identified more granularly. Comparing these ratios for a particular customer against the “aggregate” value allows you to identify specific customers or vendors that lag the overall average and therefore may indicate an existence or valuation risk around those balances or underlying contracts.

Expose the nature and volume of transactions on credit with related-party customers and vendors

Reviewing the subledger detail for transactions with all related entities is information that may not be readily available on the surface of the general ledger data and the relative dollar volume and activity of these transactions could be relevant to how risk is assessed around the accuracy, valuation, and presentation assertions.

Surface invoices or other records in the subledger (debit or credit memos, unapplied payments, etc.) that may be significantly aged

Isolating items in the subledgers that are significantly aged may tie directly to the risk around valuation and existence of these items specifically. Under a more nuanced lens, the existence of these types of stale records (or lack thereof) may be a relevant consideration to corroborating your understanding of the controls framework and how closely the subledgers are being reconciled and actively maintained.

Evaluate the volume and frequency of transactions at the level of a specific customer or vendor to corroborate inquiry of management and your understanding of the entity

Understanding basic data points around volume and frequency of transactions with a particular customer or vendor may help corroborate information learned from inquiry or your knowledge. For example, reviewing transactions with the entity’s landlord to confirm that 12 monthly equal rent payments were posted. Scanning this type of activity (either manually or with automated techniques) can surface invoices or payments for amounts that are potentially unusual for a certain customer or vendor and therefore perhaps may be indicative of risk.

Review for the volume and frequency of manual adjustments directly to the subledger detail

Manual adjustments or entries directly to the subledger, i.e., entries that don’t have a commercial document of record (invoice, cheque, credit memo, etc.) associated to them, may indicate fact patterns or internal processes that warrant further consideration from an audit perspective.

Perform basic statistical and rules-based tests and interrogate the subledger data to inform risk assessment

Certain procedures around data quality that are traditionally associated with journal entry testing, such as the following, may be very relevant to the subledger information. This includes any “hits” that would be relevant to deepen your understanding of your client’s accounting system and internal control framework and also advise the severity of assessed risk:

Reviewing descriptions for suspicious keywords
Duplicate document IDs
Two-digit Benford analysis
Other rules-based tests

How MindBridge automates and streamlines AR & AP subledger analysis

MindBridge AI has dedicated AR and AP modules that automatically analyze the subledger data and, without any scripting, provide high-value visualizations of the data and transaction-level analysis. These capabilities empower you to leverage subledger-level insights and anomalies as critical inputs to the audit risk assessment process.

Trends and patterns

Ai Auditor provides the ability to visualize how monthly AR and AP balances or net monthly activity tracks over multiple years, at the customer and vendor level and also in aggregate. The visualization is customizable and provides the ability to compare certain customer or vendor trend lines against each other and identify patterns of deviation.

Vendors and customers who are related parties to the entity subject to audit are flagged directly in the summary detail to identify for specific review.

Key performance indicators

Days Outstanding and Turnover ratios are calculated at the customer and vendor level and visualized on a monthly basis, allowing you to identify where there are periods of potential distress or deteriorating quality. Similar to the ending balances and activity, you are also able to customize the visualization and compare certain customers or vendors against each other along the lines of these metrics to expose patterns of interest.

Ai Auditor also automatically identifies any new customers or vendors in the audit period, allowing you to identify the related volume of sales or purchasing growth specific to these entities.

Aging

Aging at the customer and vendor level is automatically calculated and captured across respective buckets of days outstanding (0-30 days, 31-60 days, etc.). For certain entries that are significantly aged or stale, you’re able to drill-in to all the transactions with a particular customer or vendor and ascertain which invoice(s) are contributing to those totals.

Data interrogation and risk

Navigating and querying the transactional level data via the Data Table in Ai Auditor provides a powerful and effective way to explore and validate the subledger activity. The Filter Builder functionality allows for multiple conditions to be placed on a query, using any element of the transactional record (date, amount, user, entry type, etc.). This allows you to build and save functions that allow you to get a sense of the type, frequency, and volume of transactions with certain vendors or customers.

Control Points, which are various statistical, rules-based, and machine learning tests, are run against every transaction and the results are summarized on a dashboard that supports interactions like filtering and drill-through.

Combining the query building capabilities of the Data Table with the feature of every transaction being scored against the various Control Point tests, you are empowered to identify relevant populations for sampling and have selections automatically identified on a risk-stratified basis. Approaching the sampling process through the lens of transactional risk ensures that you’re focusing your audit procedures around the entries which appear anomalous.

Take the first step towards unlocking critical subledger-level insights for risk assessment

To learn more about Ai Auditor and subledger analyses, contact sales@mindbridge.ai.

Audit in the age of COVID-19: A guide

May 26, 2022June 4, 2020 by John Bednarek

a complex amount of data put into a pattern

In today’s world of COVID-19, accounting firms can be pressed by clients for short-term basic deliverables. There are still expectations for getting audits completed as pragmatically as possible per the standards along with the new norm of engaging clients virtually and securely.

I believe this challenge presents an opportunity for firms to re-position themselves for success in addressing this new environment. Humans adjust to new challenges well and this will accelerate the digitization and automation of key accounting processes, including auditing. This automation will not happen overnight but will transition as a step change, where the audit of the future bears little resemblance to the audit of today.

“We believe that the application of artificial intelligence will be immensely valuable in helping companies adapt to these trends. The most successful use cases will be those that seamlessly combine AI with human judgment and experience.”

– The Rise of the AI-Powered Company in the Postcrisis World, Boston Consulting Group

The accounting firms that adjust with speed and agility will be well-positioned to grow today and in the future.

Audit analytics as a competitive differentiator

The topical issues we are witnessing today include ‘going concern’ and financial performance analysis. Tremendous focus is being applied to cash flow, debt management, and yes, business survival. Businesses that are feeling financial stress are seeking additional education and guidance.

Remember, many times clients do not know what they want or what you can do for them. This is a perfect time to make your firm relevant to your clients. You can critically evaluate what is good for your clients and do so by leveraging audit analytics tools to educate them and develop appropriate plans to address their challenges.

Audit analytics builds usable market or industry-specific knowledge, so while you might not be able to sell advisory services directly to your audit clients, having better market knowledge improves your ability to sell this value to other companies and win new customers. Your firm becomes more relevant to their business and you have created a competitive advantage for your firm.

Remote auditing begins with security

It will also be paramount to ensure that the technology and services you leverage are designed to work in a virtual environment securely. Ensure that your audit tools are cloud-based, have primary and backup infrastructure providers that are fully ISO 27001 and SSAE 16 compliant, and use data encryption using NIST-approved algorithms (AES 256). It’s also critical that the vendor has completed the AICPA Security Organization Controls (SOC) compliance and has completed its SOC 2 Type 2 certification against all five trust services criteria.

Audit analytics in action

A MindBridge customer spoke recently about the value of our platform and how it positions the CPA firm for success. They stated that Ai Auditor improves the client-auditor relationship by allowing them to ask better questions and enhance the knowledge of their client business more than traditional tools. The platform becomes the single source of information that leverages the general ledger and provides 100% coverage with its transactional approach.

This positions accounting firms to:

Understand and guide their clients appropriately, especially in the storm we are witnessing today
Critically evaluate what’s good for their clients, including risk awareness and management
Educate clients on their options and provide appropriate advice in making better decisions for their business

Accounting firms can re-invent and position themselves for success in this new world by delivering value-added services primarily driven by what is good for their clients. Your commitment to deploying innovative new solutions, such as audit analytics, and creating a competitive advantage for your firm is critical in today’s world of a virtual practice. Internally, firms and their employees will be inspired with the latest innovations and automations that our platform delivers.

There are risks in making this transition and investment but by working with the right partners and being astute about it, the benefits for your practice will be considerable.

Streamlining your going concern audit procedures

May 26, 2022May 14, 2020 by Stefano Colaiacovo, CPA, CA

In these challenging times, virtually all organizations are faced with disruptions to the status quo. Depending on the industry and profile of one organization to the next, these disruptions can range from creating slight instability in day-to-day activities to having a catastrophic impact on the basic viability and sustainability of operations.

Transparent and accurate financial information and the auditor’s conclusions thereon are a cornerstone of market and investor confidence in the best of times, and the criticality of this responsibility is only magnified in the current environment.

The auditor’s procedures around the going concern assumption are therefore receiving an increasing focus and consideration by audit teams, particularly for engagements that are well-in-flight and in the concluding phase (i.e., just ahead of financial statements being formally issued). For example: a calendar 2019 audit engagement where financial statements haven’t been issued yet will need to consider a renewed examination of going concern.

How can MindBridge add value and assist in this assessment?

A going concern refresher

Management is required to make the assessment of whether the organization is a going concern for the foreseeable future and the auditor’s responsibility is to obtain sufficient and appropriate audit evidence to support this. If there is a material uncertainty (substantial doubt) regarding the entity’s ability to continue as a going concern, there are additional disclosure requirements in the financial statements and/or qualification of the audit report that may be required by the prevailing audit guidance.

While the applicable auditing standard framework and/or regulatory environment specific to your audit engagement may differ, this assessment must generally consider at least the period of 12 months from the reporting date (i.e., the balance sheet date). Often this requirement is extended for the auditor to consider the period of 12 months from the issuance of the financial statements.

Evaluating the going concern assumption under a new lens

Against the backdrop of the current crisis and considering the auditor’s responsibilities around the going concern assessment, some practical challenges may arise:

The question of going concern may suddenly be relevant for an engagement or client that had previously never had any doubt regarding the sustainability of its operations. The company may be well capitalized, have a strong liquidity position, and demonstrable sales growth but is perhaps in an industry that is hard-hit by the crisis, introducing uncertainty into the assumption. Two considerations become relevant in this case:
- Management may not have the historical experience in providing material to support the going concern assumption (forecasts of P&L, cash flow, summarized non-financial data, etc.) for the foreseeable future, that is up to 12 months from reporting date or issuance date.
- The auditor may not have the historical experience in obtaining sufficient and appropriate audit evidence around the accuracy of the kind of “forward-looking” material on which management would base their going concern assertion. Further, there may not be any robust experience from previous years in the relationship with the client that objectively supports management’s ability to estimate forecasts effectively.
There may be significant uncertainties around future outlook, especially in these (relatively) early stages of this crisis, and management may be challenged to make even the most basic assumptions in their forward forecasting. What will revenue look like and how long may it take to return to pre-crisis levels? How successful will receivable collection be? Where is there flexibility in discretionary expenditures? Various scenarios may be required to be modelled out, allowing for sensitivities in critical financial statement areas, that the auditor may have to review and consider on balance.
These uncertainties around future outlook are currently mirrored in the realm of potential government assistance or relief that may be relevant for the organization. Governments and financial institutions are stepping in with significant relief measures being announced and operationalized but information around eligibility and interpretations is changing rapidly. Whether the form of this relief is subsidies, cash flows loans, or tax relief, these are critical inputs to support sustainability of operations that the auditor needs to consider.

How MindBridge helps evaluate going concern

MindBridge Ai Auditor is a powerful enabling tool for auditors to test the going concern assumption:

Key trends and patterns can be surfaced
Critical ratios can be visualized
Transaction-level data can be interrogated

Let’s review each of these capabilities.

Trends and patterns

Ai Auditor allows for financial data to be visualized, layering in current year results against prior periods to surface critical trends and material deviations from history. The example here showcases trending of the accounts receivable line over time.

Within your engagement in Ai Auditor, you are able to create multiple analyses of your client’s financial information. You can create a new analysis that includes financial data from the period under audit and extend the analysis to include the most current financial information into the next fiscal year to assess the going concern assumption and management’s forecasts. This allows you to quickly understand more recent trends and let the data speak for itself against management’s assessment of near-term performance.

Other trended views, with examples, to consider around the going concern assessment may include:

Visualizing cash over-time to get a sense of monthly burn and compare against management’s cash flow analysis. This allows for the review of sudden peaks that may indicate cash from financing or investing activities and can be normalized to derive a sense of cash flow from operations.

Visualizing accounts payable and the recent ability to settle balances owing to vendors. This can be compared against management’s cash flow analysis.

Other potentially relevant trending patterns to explore, as a reference:

Visualizing short- and long-term debt facilities and consideration of whether these are indicative of a deteriorating liquidity position.
Visualizing revenue trends and comparing them with the expense/cost of sales trends to understand where levers potentially exist for management to manage the economic shock.

Ratios

Ai Auditor includes a library of critical financial ratios that enable you to visualize how each of these metrics move through the year. You can easily create custom ratios that account for a specific industry that your client operates in or perhaps to mirror debt covenants that are in effect.

Similar to the Trending graphs, by extending your data to include more current financial information via a new analysis in your engagement, the ratio visualizations capture the recent movement in these key metrics and surface insights quickly.

Some examples:

Visualizing debt-to-equity ratio over time, and for the most recent period, to assess whether bank covenants are well within acceptable ranges or to get a sense of how operating lines are being tapped as a source of cash flow.

Visualizing gross profit as a percentage of sales over time, especially in recent months, to understand how the economic “shock” of the crisis has impacted margins and income statement relationships.

Other potentially relevant ratios and metrics to explore related to the going concern assessment, as a reference:

Current ratio
Working capital
Debt to assets ratio
Debt to equity ratio
Expenses to sales ratio
Long-term debt to net working capital ratio
Accounts receivable to sales ratio

Pro tip: With more than four years (48 months) of historical financial information, a regression analysis called seasonal autoregressive integrated moving average (SARIMA) analysis is performed by Ai Auditor to graphically visualize the expected ranges for the ratio in the current period in addition to the trend lines. In the context of a going concern assessment, this can be extremely valuable to indicate whether a particular ratio or series of ratios is not only potentially down from pre-crisis level but also outside of a normal range. The latter might be more of a signal as it relates to sustainability of operations.

Interrogation of transaction-level data

Navigating and querying the transactional level data via the Data Table in Ai Auditor provides a powerful and effective way to explore and validate the nature of certain accounts in the ledger. The Filter Builder functionality allows for multiple conditions to be placed on a query, using any element of the transactional record (date, amount, user, etc.) as well as the relevant Control Points (the algorithms behind the analysis) that apply to a particular transaction.

Some examples of where this could be applied to the going concern assessment:

Validating whether certain criteria for government assistance are met by your client, i.e., query for total amounts spent on payroll, rent, etc., in a particular period of time
In evaluating management’s forecasted P&L against historical results, evaluate the implied assumptions of certain income statements accounts for reasonability, by understanding the nature and frequency of underlying transactions. For greater clarity, validate whether certain material categories of expenses are fixed or flexible in nature.

As the current crisis unfolds, it’s more important than ever for auditors to add value for clients and a going concern assessment is critical in understanding the basic viability and sustainability of operations. MindBridge Ai Auditor can help test this assumption by identifying key patterns (layering in current year results over prior years to surface critical trends), visualizing critical ratios (such as debt-to-equity or gross profit as a percentage of sales), and allowing for the in-depth interrogation of data through customizable filters.

To learn more about how Ai Auditor can help your going concern assessments, contact sales@mindbridge.ai.

Baldwin CPAs enhances technology-first services with MindBridge

September 29, 2024May 5, 2020 by Gillian Fischer

In the lead up to their panel presentation at Influence 2020, we connected with Myron Fisher, CPA, CGMA from Baldwin CPAs, PLLC (Baldwin) to talk about their approach to technology and audit.

Baldwin is experienced and qualified to provide accounting services to a wide variety of businesses and industries. The firm has concentrations in the industries of construction contractors, governmental, not for profit, and medical. Baldwin is a member of the Private Companies Practices Section of the American Institute of Certified Public Accountants and the AICPA’s Governmental Audit Quality Center and Employee Benefit Plan Audit Quality Center.

Baldwin’s vision is to be an innovative firm that creates value for their clients, supported by their mission statement and core values:

We are dedicated to strategies that enhance the growth and success of our team and clients.

Core values

Integrity and honesty
Respect and trust
Personal development and growth
Accountability/Responsibility

What is Baldwin’s overall strategy and approach to adopting new technology?

MF: Baldwin’s approach to technology is based upon a comprehensive and holistic strategy, from an aspect of ‘what do we want to look like in 3-5 years’?

We look at the adoption of new and emerging technologies and try to position ourselves on the early adopter position of the bell curve. We do not want to be a late adopter as we believe that it does not agree with our vision statement.

Examples of technologies that have been incredibly critical to our success as a firm are adopting virtual/cloud-based platforms early in our past, such as moving to a virtual platform 16 years ago, and moving to a paperless retention environment in 2003. Recent examples are the use of Suralink for a secure document exchange platform for assurance engagements and complete firm integration of Microsoft Teams as communication platform. All these applications made the challenges presented by the COVID-19 pandemic fairly seamless.

What problem(s) were you trying to solve when engaging with MindBridge?

MF: When we look at implementing specific tools, it’s not just about solving one problem, it’s multi-faceted. With MindBridge AI specifically, it’s not just about efficiency, making intelligent samples, doing audit procedures faster, or audit evidence quality — it’s all of these things combined. In addition, the adoption of an innovative tool such as Ai Auditor aligns with shaping the future of our firm.

How is Ai Auditor providing value to your firm & clients?

MF: Overall, it’s driving better conversations internally and externally.

Internal discussions

Internally, we’re just having better conversations within our teams when we open ourselves up to data and insights. We are becoming more aware of transactional details within our clients’ ERP systems.

Just recently, we had a planning meeting on an audit client. In the previous year, we would have put the risk assessment as higher, however, after internal discussions and analysis, we came back and said that the risk assessment should be lower, allowing us to more confidently assess audit risks.

We will also be implementing use of the tool for review engagements. Our team will be able to perform more informed analytic procedures resulting in improved inquiries.

External with clients

We don’t have a “we found this” per se but it’s driving better conversations with our clients, which drives value. The response has been, “you’re asking better questions.”

One example is a client relationship with a city. We approached them early about leveraging Ai Auditor and asked for their data. One of our accountants was able to show the client what the analysis looked like right there onsite. It drove a meaningful discussion and provided more value to the client.

What are some challenges and lessons learned from your experience with implementing emerging technologies?

MF: We know that taking a proactive approach to technology adoption comes with significant challenges and we accept those challenges. Implementation is a challenge with all applications and we are not immune. Early realization and acceptance allows for better planning and depth of understanding within the entire team. Resource allocation is paramount.

Specifically, with tools like Ai Auditor, we recognize the need to have team members better trained in the application of data analytics. We have enabled team members to invest time to become application champions to assist the entire team.

Our biggest pain point and process challenge is getting data from our clients early. We have addressed this area by implementing an internal tool designed to increase communication of expectations from clients.

Communication is critical. We try to ensure that we keep communications flowing and keep all team members informed. Ai Auditor is a standing agenda item for bi-weekly assurance team meetings.

Myron D. Fisher, CPA, CGMA

Baldwin CPAs, PLLC, Member. As the Firm’s Leader of Assurance Services, Myron Fisher brings more than 25 years of assurance and tax experience to the Firm. Myron leads assurance engagements including financial statement audits, reviews and compilations, ERISA employee benefit plan audits and other agreed-upon procedures engagements. He performs assurance services for private businesses, governmental clients, non-profit organizations, financial institutions, construction clients, and medical institutions. Myron also serves as the firm’s Quality Control Director.

In addition to assurance engagements, Myron has been very involved in the Kentucky Peer Review program. Myron has worked with the AICPA Peer Review Enhanced Oversight Program. Myron performs engagement and system peer reviews throughout the state of Kentucky. He enjoys working with other CPA firms in helping them with achieving a high level of compliance in their assurance practice.Over the years, Myron has been tremendously involved in both the accounting community and the local community. He has served as a member of the KyCPA Peer Review Committee and currently serves on the Peer Review Alliance Program with the Illinois Society of CPAs. Myron has also served as a member of the KyCPA Board of Directors and is an Audit Quality Partner for the AICPA Employee Benefit Plan Audit Quality Center. In his local community, Myron has served as the Chair of the Richmond Chamber of Commerce from 2016 to 2017 and is currently a member of the Central Kentucky Regional Airport Board.

How AI restores the public’s trust in the fiscal accountability of governments

May 26, 2022April 29, 2020 by Andrew Popp

Handshake illustration between government and public

The public’s trust of governmental budgeting, fiscal management, and reporting is at an all-time low, especially in the aftermath of the 2008 financial crisis, where only four out of ten people in OECD countries expressed confidence in their government. Cases of fraud, bid-rigging, and pay-to-play are never far from the headlines, and have continued to undermine trust in the public servants and elected officials tasked to oversee the complex work of managing government finances.

A large portion of this mistrust can be attributed to the struggle that government finance managers and auditors are facing in analyzing the increasing amount of financial data. Current financial control and audit techniques, including legislated audit requirements, are not able to scale to keep pace with the massive data explosion coming from their own accounting, payroll, and expense management systems. One government response to this issue, open data, enables a sense of fiscal transparency with the public but it doesn’t replace the rigorous professional analysis required to identify fraud, errors, and omissions in large amounts of data.

Enter artificial intelligence (AI). Leveraging a mix of machine learning and natural language processing (NLP) techniques, AI can help government auditors and finance officials deal with the massive amounts of data they are required to professionally process in a timely fashion to meet their fiduciary responsibilities to their taxpayers, which in turn will help restore the public’s trust in government.

The financial data explosion in government

Imagine you are running a government department that has 150 different operational entities but only have the resources to audit four or five a year. That’s a massive financial blind spot that will make your comptroller or CFO lose sleep at night. It’s not a question of whether fraud or errors are occurring, it’s a question of when the news will break that they have happened.

Equally challenging is the government audit department that must perform 150 audits per year by legislative mandate. With so many audits required, where is the time to dive into different areas of analysis and reveal insights that can help lead to improved service to the public?

PwC estimated that 18 zettabytes of financial information was created globally last year. Visualize a standard pitcher of water as a byte. One zettabyte is all the water on planet earth. PwC also estimates that only 0.5% of that data is analyzed, and it’s in this unreviewed data that the errors, omissions, and frauds that the press reports on is occurring.

Let’s also be clear on what governments are catching in the data they are analyzing. Current financial controls, audit methodologies, and analytics catch about 3% of the total global fraud as estimated by the Association of Certified Fraud Examiners. Tips, on the other hand, uncover 50% of the major corruption cases.

Current government processes and professionals are not catching the errors faster than whistle-blowers are reporting it. So it’s not surprising that governments are perceived as being ineffective in how they deal with the detection of fraud and errors in their financial statements.

Fraud hotlines & open data: First steps

Governments have responded to this issue in a number of ways. One of the first has been the rapid rollout of fraud hotlines, driven by statistics on how fraud has been uncovered to date in the majority of cases.

The other approach has been to broadly publish budget, financial, and audit reports to the public. This approach has been tied to the open data/open government movement and has been seen by many as a more citizen-inclusive approach to solving this problem. The idea is, by releasing all the data to the public, concerned citizens can dig into finances and find errors and mistakes to help share the burden of analysis.

This refreshing approach to transparency in government has its benefits, including seeing governments become better stewards of their own data, and being more open to feedback. However, the open data movement hasn’t been able to put an end to the public’s mistrust as first promised. Missteps in areas such as the standardization of data formats and APIs, the frequency of updates to the released data sets and the scope of the data released has limited analysis by external parties.

In addition, as larger data sets are released, individuals are no longer able to perform a full analysis of the data in a single pass. Microsoft Excel, the world’s most widely available financial analysis tool, has a million-row limit for data processing, and other available data science and accounting tools and resources are out of reach for the majority of citizens. The data might be readily available, but the professional tools and skills are not.

Artificial intelligence: Creating government efficiencies

Artificial intelligence is part of what is being dubbed the Fourth Industrial Revolution and has the ability to dramatically improve the efficiency of organizations. In the financial and audit worlds, AI offers an approach that includes:

Continuously ingesting large amounts of financial data from different sources
Risk assessing 100% of all transactions against all current and past data
Indexing all transactions in a way that can be interrogated with common language questions, such as “Show me all transactions with high risk at the end of 2018”
Producing reports that allow auditors and financial officers to extract insights into how their government is operating, and take corrective or reinforcing actions accordingly

In this way, AI builds on what the open data movement has started, as it offers a means of democratizing the application of complex analytics to governmental financial review. Governments can now load and analyze all their financial data, applying the open government standard of transparency to both the data and the algorithms they are using, and then releasing the results for the public to review when completed.

AI solves the problem of the department with 150 potential audits and having the resources to run 4-5 audits only. AI can run continuously on every department and help to direct the limited resources to the departments that exhibit high risk instead of burning resources using round-robin audit approaches and random sampling of transactions to review. For the departments not chosen for an audit, financial managers can be sent risk reports in each department allowing them to take corrective measures in advance of a future audit.

And for the organization facing 150 mandatory audits, AI can drive cost efficiencies as standard procedures can be automatically performed, freeing up auditor time for deeper interrogations of the data.

AI can also make fraud hotline tip review more efficient, namely the requirement that anonymous tipsters have to be convinced to give up their anonymity to prove the claim being made is true. Upon receiving a tip, an AI tool can be directed to review the data claimed at risk. This allows the financial data to speak for itself, relieving the tipster of having to reveal their identity early on in the risk assessment of the tip.

Artificial intelligence: Finding financial anomalies

So how does AI find anomalies in financial data and allow auditors and financial officers to search the data quickly? These capabilities are found in the application of machine learning and NLP.

Machine learning is a sub-field of artificial intelligence that focuses on the application of algorithms to large amounts of data to enable further insights. MindBridge Ai uses both supervised and unsupervised algorithms to risk rank all the financial transactions loaded into platform. Supervised algorithms are based on training data, and we developed an algorithm based on known patterns of fraud that was provided to us by forensic accountants.

Unsupervised algorithms are special, because they are developed to allow the data to speak for itself, meaning that transactions are clustered into neighborhoods of numbers that are interesting to accountants, such as rare connections between two accounts. These algorithms can also identify transactions that fall outside neighborhoods of numbers, called outliers.

These algorithms, run in concert with standard accounting rules and statistical techniques, such as Benford’s Law, allow us to risk score every transaction in a financial ledger. While this is done, the data is also indexed for rapid search capabilities, which brings us to the application of NLP.

NLP is another sub-field of AI and, in the accounting context, allows auditors and financial officers to ask questions of the data that has been risk ranked using machine learning, returning a list of risky transactions that fit the criteria.

Together, machine learning and NLP have been empirically tested to show that we can conservatively detect financial anomalies 10-30x better than current audit and financial analysis methodologies.

Artificial intelligence: Restoring public trust

The inability of government audit and financial departments to analyze 100% of the data in the government’s trust has been a major factor in its inability to spot financial anomalies. While fraud hotlines and open data techniques have been a step in the right direction, AI offers an opportunity for the government to actively pursue the detection of financial anomalies before whistle-blowers think to act on ethical and moral grounds.

Private sector audit firms are already turning to platforms such as MindBridge Ai Auditor, and over 40% of the top 100 audit firms in North America are currently engaged with us. In the public sector world, the auditors and financial officers in the Canadian and UK federal governments have already tested the MindBridge platform and measured the advantages of using AI against current techniques.

Students in accounting programs at over 60 universities in Canada, the UK, US, and Australia have also engaged in using Ai Auditor in their accounting courses. As AI-ready individuals move to becoming workers and taxpayers, they will demand that AI be employed by all levels of government to help in the detection of fraud, errors, and omissions in financial data. Citizens are already becoming aware of the capabilities of AI, as they hear about it every day in its application to autonomous vehicles and other government services, such as automated government decision making.

The Fourth Industrial Revolution is upon us, and there are significant benefits for governments in the early application of AI to detect financial anomalies to turn the tide on fraudsters and bad actors. Beating whistle-blowers to detect and mitigate fraud is an achievable goal through AI and will make major leaps towards restoring the public’s faith in a government’s ability to manage the public’s finances.

To learn more about our government solutions, including real use cases, visit our government finance page.

Quality audit vs. efficiency: How technology bridges the gap

May 26, 2022April 6, 2020 by Shaye Thyer, CA

As someone who has worked in big-firm transformational change for quite a while, I’ve often reflected on the perils of success and its ability to stand in the way of innovation and agility. We work really hard in transformation teams to articulate URGENCY, and NECESSITY, and ABSOLUTE DIRE NEED to transform. But no matter how creative we are, without a great deal of immediate pain, it is incredibly difficult to steer a new course for big ships that have been historically successful.

Undeniably, COVID-19 is a catalyst for urgent and mission-critical pivoting across most areas of professional services, and indeed business in general.

I’ve been checking in with many of my clients and industry colleagues over the last couple of weeks, seeing how I can help them, seeing if they’re ok. This has helped me learn a lot about how many audit firms across Australia and New Zealand are coping.

	CLOSING	DECLINING	PROVIDING	THRIVING
CURRENT STATE	Hibernating or liquidating	Open (just), panic over decline in recurring revenue	Open and BAU (as far as the market is aware)	Open & thriving
REVENUE MIX	None	Traditional recurring engagements only	Mostly traditional recurring engagements	Maintained traditional recurring engagements, adding parallel services and /or opportunistic blue-sky work
CLIENT APPROACH	In the wind	Overwhelmed by client emotional burden	Reactive client emotional support	Proactive, deliberate and direct client management
WORKFLOW FOCUS	Nil	All client work reducing	Laser focus on core client deliverables	BAU on core + new (relevant) service offerings
STRATEGIC FOCUS	Nil	Holding breath, can’t see past next week	Wishing for the dust to settle	Planning for acceleration when the dust settles

Regardless of which state you and your firm fall into, no one will argue that ‘business as usual’ is no longer a thing. All those posters we’ve all seen about needing to embrace change seem to have come rushing at us all with force.

The push-pull between audit quality and efficiency

How are firms around the country reconciling the absolute mission-critical need to be agile, to adapt, to change rapidly while protecting their sole reason for existing: Audit quality?

Materiality

Many firms are considering their materiality levels. Are those that have always been used still appropriate in the current climate? If lowering them is most appropriate to capture risk properly, how will firms deal with the significant uptick in sampling effort required as a result?

Focus on fraud

Pressure – Employees within our client’s businesses will be under enormous stress, thinking about potential job loss, financial strain, and social distancing.

Opportunity – Management review may not be as vigilant due to distractions by the crisis and other management responsibilities, and internal controls may be circumvented in times of crisis for the reason of expediency to keep business processes operating.

So firms are considering how they can appropriately resource, looking more closely in areas that are ripe for fraud in these economic conditions – areas like payroll, termination payments, shrinkage of inventory, revenue recognition…anywhere where there’s not great segregation of duties, management override of controls, etc.

Big picture mindset

Following a normal process, one designed for ‘business as usual times,’ is just not sufficient in the current state of COVID-19, so firms need their auditors to step back and look at more of the big picture. Considering the broader context of what they’re doing, evaluating the effects of COVID-19 and surrounding behaviours on their risk assessments is critical for effective audits. This requires perhaps more space and ‘thinking time’ than the normal production line of audit engagements so again, firms are contemplating the effect of this change on resources and profitability of current engagements.

Carrying on as normal is unacceptable. What was identified as the top risk when you did your audit planning and risk assessment —what you are auditing as you read this —is almost certainly not the top risk today.

Normal Marks, author of Auditing That Matters

Each of these considerations means a diversion from very structured and process-driven methodologies and practices that have been refined over the recent years to deal, in many cases, with the downwards fee pressure on audit engagements. And pivoting away from ingrained processes risks the need for more WIP and more resources.

As Tim Kendall, BDO National Leader for A&A shared with the AFR recently: These things coupled with any rigidity in ASIC’s views around current financial reporting time deadlines pose a significant risk to audit quality as firms are forced to squeeze engagements into a very tight delivery timeframe. (full AFR article HERE)

The key difference between those firms who are Closing, Declining, or Providing and those firms who are Thriving is simple – either an ongoing commitment to best-of-breed tech to support the most efficient and effective high quality audit engagement delivery OR an immediate prioritising of adoption of such. Or both.

Thriving firms already have, or are seeking to have, tech that provides ways to be more efficient AT THE SAME TIME as expanding the lens to have a broader contextual view of risk.

How auditors use AI-driven ratios to understand risk

Effective audit execution in remote work environments

Getting ready for AI-powered audit in 2020

Why the ICAEW Technology Accreditation Scheme matters to you

May 26, 2022March 31, 2020 by Roy Sarkar

ICAEW Technology Accreditation for MindBridge

It should come as no surprise that the accounting software market is exploding. With increasing demands on chartered accountants to be smarter, faster, and more data-driven than their peers, we’re seeing massive growth in available tools, and the leading-edge technologies behind them.

This new landscape means that we’re in an age of faster time-to-market, constant product evolution, and, with the adoption of AI, taking on new ways of doing things. That’s why it’s critical for vendors to ensure a high degree of testing and quality assurance behind their products and for accountancy firms to understand the technology being offered to them.

The ICAEW Technology Accreditation Scheme helps accounting firms take the guesswork out of choosing the best software solution and MindBridge Ai Auditor is the first and currently only data audit & analysis software solution provider to have gone through this rigorous evaluation methodology.

The ICAEW methodology

The Institute of Chartered Accountants in England and Wales (ICAEW) is a professional membership organization providing insight and leadership to the global accountancy and finance professions. With over 181,500 chartered accountants and students worldwide, ICAEW provides qualifications and professional development, insights and technical expertise, and protects the quality and integrity of these professions.

The ICAEW Technology Accreditation Scheme is a rigorous evaluation methodology that goes into a software company’s management structure and financials, software development details, customer satisfaction processes, and more. This typically entails all departments coming together to answer questions in support of the company’s ability to successfully deliver features, value, and support to users.

Why it matters

This accreditation gives current and future Ai Auditor customers every confidence about choosing and using our solution. This is important for the following reasons:

The ICAEW Technology Accreditation Scheme is an independent evaluation of software packages, giving you the confidence that Ai Auditor brings value to your practice
Completion of the accreditation involves the completion of detailed questions about our product, functionality, support processes, and corporate management, including an on-site visit by the ICAEW
All submissions are evaluated and verified by an independent top UK accountancy firm, RSM UK, who has the final say as to whether a product has passed the accreditation process
The Scheme’s independence is very important as it means software companies cannot simply pay to join the Scheme but must meet the required criteria laid out in the questionnaire

As Ai Auditor is the first and currently only data audit & analysis software solution provider to have gone through this evaluation methodology, we are uniquely positioned to deliver real value and support to audit practices.

Craig McLellan, manager of the ICAEW Technology Accreditation Scheme, has the final word: “We are delighted that MindBridge has been accredited by the ICAEW. With the ICAEW Technology Accreditation Scheme being the benchmark for software used by accountants in both the business and practice markets, we welcome industry and software companies in embracing a modern approach to finance and accountancy.”

Learn more about MindBridge Ai Auditor here.

The challenges with traditional journal entry testing

3 ways to automate risk-based journal entry testing

1. Start with a data-first approach

2. Leverage the community effect

3. Pay attention to complex transactions

Evolving audit risk assessments and your business

Key concepts around audit evidence

Facilitating high-quality and data-rich analytical procedures and risk assessment

Techniques that facilitate highly efficient “dual-purpose” procedures

Take the first step towards a modern, data-driven technological approach to audit, contact sales@mindbridge.ai.

Three ways Ai Auditor strengthens your audit planning

How to enhance your audit planning using Ai Auditor:

1. Conduct thorough assessments for better audit planning

2. Quickly identify unusual transactions across all data

3. Retrieve and view transactional breakdown by audit area

A stronger audit plan leads to stronger audits

Pinpointing audit risks using a data-driven method

Building an effective audit strategy with Ai Auditor

Developing an audit plan with data at your fingertips

Completing the audit engagement with success

Traditional preliminary analytical review procedures

Accounts Receivable & Accounts Payable as critical inputs to audit risk assessment

Understanding how certain vendor and customer aged balances trend throughout the year

Understanding operational key performance indicators for customer and vendor “health”, and tracking those over the audit period

Expose the nature and volume of transactions on credit with related-party customers and vendors

Surface invoices or other records in the subledger (debit or credit memos, unapplied payments, etc.) that may be significantly aged

Evaluate the volume and frequency of transactions at the level of a specific customer or vendor to corroborate inquiry of management and your understanding of the entity

Review for the volume and frequency of manual adjustments directly to the subledger detail

Perform basic statistical and rules-based tests and interrogate the subledger data to inform risk assessment

How MindBridge automates and streamlines AR & AP subledger analysis

Trends and patterns

Key performance indicators

Aging

Data interrogation and risk

Take the first step towards unlocking critical subledger-level insights for risk assessment

“We believe that the application of artificial intelligence will be immensely valuable in helping companies adapt to these trends. The most successful use cases will be those that seamlessly combine AI with human judgment and experience.”

Audit analytics as a competitive differentiator

Remote auditing begins with security

Audit analytics in action

A going concern refresher

Evaluating the going concern assumption under a new lens

How MindBridge helps evaluate going concern

Trends and patterns

Ratios

Interrogation of transaction-level data

The financial data explosion in government

Fraud hotlines & open data: First steps

Artificial intelligence: Creating government efficiencies

Artificial intelligence: Finding financial anomalies

Artificial intelligence: Restoring public trust

The push-pull between audit quality and efficiency

Materiality

Focus on fraud

Big picture mindset

Read more

The ICAEW methodology

Why it matters