AI and technology Archives

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.

AI in finance: Helping professionals shift from hindsight to insight to foresight

December 18, 2020June 11, 2020 by Eli Fathi

We are facing an unprecedented time of global uncertainty created by the COVID-19 virus that has unleashed a global healthcare crisis. Humanity is fighting a war against an invisible enemy that is attacking humans around the world and sparing no country. We need not be pessimistic or optimistic but rather realists and learn from the history of humanity. Human ingenuity will prevail, and humanity will survive.

We have entered a new world after COVID-19 with very different assumptions than we had in the old world when the world GDP yielded a record high of $85T. The world GDP has been severely impacted by the lockdown stipulations that were imposed to minimize the spread of the virus within the population. The key pillars of the economy are consumer and companies’ spending. If this slows down, it can lead to a recession and even depression. The lockdown restrictions are being relaxed and governments and central banks around the world are injecting massive amounts of funds into the hands of individuals and companies in an effort to reopen the economy to avoid an economic crisis.

How can artificial intelligence in finance help organizations pull through?

A renewed focus on financial errors

During economic uncertainty, an added vigilance is needed by those responsible to ensure the accuracy and integrity of the financial records that are being relied upon to make decisions about the operations of their organizations. A report by the Association of Certified Fraud Examiners (ACFE) “2020-Report to the Nations”- 2020 Global Study on Occupational Fraud and Abuse estimates that the yearly cost to the world due to fraud and abuse is about $4.5T or 5% of the world GDP. They examined over 2500 cases from 125 countries with combined losses of $3.6B with an average loss by case of $1.5M and a typical case lasting 14 months before being detected.

Whereas corruption was the most common type of fraud, the most costly were financial statements fraud schemes, even though they represented only 10% of the cases. The breakdown of the detection methods reveals that analytics plays only a small role in the detection of occupational fraud: Human tips; 43%, internal audit; 12%, management reviews; 5%, by accident; 5%, whereas external audit catches only 4%.

A 2019 survey by Blackline provided insights into the concerns by executives with inaccuracies in financial data. With over 1100 C-suite executives and finance professionals from mid- to large-size organizations around the world, the white paper stated that:

“55% are not confident that they can identify financial errors before reporting results, 70% claim that their organizations made a significant business decision based on inaccurate financial data and 26% are concerned over errors that they know must exist but they have no visibility”.

The power of AI in finance

Finance professionals that rely on outdated tools and methodologies do not offer the best visibility into finding errors, errors with intent, errors that are considered fraud, and general mismanagement of the financial dataset in their organizations. The world is already witnessing a major trend toward moving to the cloud and becoming digital native and these must be vigorously pursued by organizations that want to be of the forefront of growth post the crisis.

Becoming digital native enables companies to move towards a near real-time view of their financial data and, coupled with AI in finance functions, the ability to fully analyze 100% of transactions. This ensures transparency to key stakeholders such as board members and auditors and aids in the identification of any anomalies in their financial records.

Currently, a company’s financial records are examined by external auditors on a yearly basis and evaluated using a sampling method that leaves the bulk of the dataset untouched. This method of rear view-mirror assessment provides C-suite executives with a hindsight perspective and the fear that decisions are made based on inaccurate and untimely information. Using AI-based tools to review 100% of the financial records in near real-time offers C-level executives with insights into data and, by using the appropriate analytics built into the AI applications, offers foresights into the operations of the company.

The two most important behaviors that companies must have to thrive post COVID-19 are resilience and adaptability. Resilience is defined as the ability to withstand or recover quickly from difficult conditions whereas adaptability is defined as the quality of being able to adjust to new conditions. Companies must build their operations and culture around resilience and adaptability so they can work efficiently during the “new normal” when we emerge out of this dark tunnel will become stronger and better off.

An article published by the Boston Consulting Group titled “The Rise of the AI-Powered Company in the Postcrisis World” highlights the tremendous opportunity for companies that are going to digital native, moving to the cloud, and adopting AI in finance applications to supercharge their operations. Arvind Krishna, in his inaugural speech as IBM’s new CEO, said, “I am predicting today that every company will become an AI company – not because they can, but because they must. Digital transformation means putting artificial intelligence at the center of workflows, and using the insights generated from that process to constantly improve products and services.”

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.

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

Our tips for artificial intelligence in accounting

December 18, 2020December 20, 2019 by Roy Sarkar

A common thread among the advice given by Accounting Today’s Top 100 Most Influential people was exemplified by Alan Anderson of Accountability Plus:

“Become technology sponges. Embrace technology and explore avenues that can positively impact what accountants do on a daily basis to help their clients.”

With our unique experience in delivering AI-enabled auditing solutions to accountants around the world, we asked our experts what advice they would give to firms thinking about, or already engaged in, their journey towards artificial intelligence.

Solon Angel, Founder & Accounting Today Top 100 Most Influential People

“Technologies that bring new efficiencies are the enablers of innovative process designs. It’s easy to get carried away by the hype in new technology cycles but keeping an eye on the fundamentals and measuring them against their utilities enables you to swiftly create value for all stakeholders.”

Scott Rockefeller, Director, Sales

“With artificial intelligence, incremental improvement is much better than postponed perfection. Waiting to see how the market evolves means you’re much further behind whenever your arbitrary inflection point comes up. The prudent strategy is to test the waters now and to get ahead of the competition.”

Azalia Shamsaei, Product Manager

“Artificial Intelligence is not hype, it’s real, and it’s here! AI has huge potential to transform our lives, and it’s already impacting industries worldwide. Audit must embrace technology and change – as auditors of the future, you should leverage tools that incorporate new technologies and explore new ways to audit beyond today’s and yesterday’s ideas.

There is no point in delaying the change, it’s just a matter of time to adapt and know more about it. Being an early adopter not only gives you a competitive edge in hiring the right people, it also provides your clients with more effective and efficient audits that exceeds their expectations.”

John Colthart, Senior VP, Sales

“Since the beginning, MindBridge has worked to help our clients change their business models, recruit new talent, and most importantly, enhance their existing processes. You can’t wait for standard setters, regulations, and “sign off.”

Technology is a fundamental part of our social and business fabrics and its use allows you to grow, retain, and encourage evolution in the profession. As leaders and individuals, you can be part of the change by moving into the 21st century to be technologically enabled.”

James Moffatt, CPA, Director of Sales

“Everyone finds themselves so busy with client commitments that they forget to plan for next year. And the year after that. Taking the time to make time to evolve your audit practice with enabling technology means you’re better prepared for the future.”

Gillian Fischer, Global Manager, Customer Advocacy

“With the flurry of new opportunities around us, the pace of change can feel overwhelming. The truth is, not everything is changing. It’s your core values that stay constant. Being a trusted advisor, remaining relevant, committing to your clients, empowering your people, and maintaining integrity — these are things we typically cannot compromise.

Return to your core values for the courage to make a change in your organization and have the confidence in knowing that where you’re going is part of the story of who you are.”

Gordon Roxon, Account Executive

“Overnight success takes time. Get on with it!”

Kevin Smiley, Account Executive

“AI is a blue ocean opportunity, providing firms the unique ability to redefine their market boundaries and make their competition irrelevant. CPAs went through digitization using tools to automate and improve their existing way of working without really altering it fundamentally.

CPAs now are going through digital transformation by moving from one way of working to entirely new ones, capturing far more value than was possible using low-scale, low-leverage legacy business.

Firm leaders need to ask themselves: Do I want to invest in AI to create demand for my firm now or wait until I’m compelled to in the fight for my firm’s life for market share?”

To learn about MindBridge Ai Auditor in 10 minutes, watch this video.

Democratizing financial and audit analytics with AI

December 18, 2020February 20, 2019 by Andrew Popp

PwC recently shared that in 2018 alone, 12 zettabytes of financial services industry (FSI) information was generated, but less than 0.5% was actually leveraged by businesses. This financial data explosion comes from an ever-increasing number of ERP and CRM systems employed by businesses and their partners, gathering and consolidating different payment, expense, inventory, and maintenance cost ledgers across the organizational landscape.

Contributing to this low rate of analytic engagement is the fact that current methods for analyzing financial data are slow; limited by time, capabilities and skill sets of the people and the software available to support the auditors and financial analysts.

Current analytics tools can’t keep pace

Let me illustrate this problem with a reasonably routine example. An internal auditor is asked to perform a risk analysis on a general ledger with five million rows. The Microsoft Excel limit for analysis is one million rows, preventing the auditor from using what is considered the world’s most popular analytical software. This means the auditor must engage their internal data analytics team, made up of specialized resources trained in the use of one the popular Computer Assisted Audit Tools (CAATs). This team in turn will write a script to perform the required analysis of the entire general ledger, or to save time, sample the data and attempt to extrapolate the risk.

The problem is, once the script work is scheduled and completed, the analysis might be too late for the audit process, or the priorities of the organization may have shifted to the point where the analysis is moot. If the team decided to use a sample and extrapolate the risk, they may have significantly less than a 100% risk analysis, putting the organization at risk.

It’s therefore no wonder that businesses are not making use of analytics; a world running on machine generated data, human-dependent analysis cannot keep pace.

AI enables analytics for all

This puts artificial intelligence (AI) front and center as the means of breaking this dependency on specialized resources and human-speed analysis. MindBridge is focused on developing the MindBridge Ai Auditor for auditors and finance managers to load financial data and analyze it on their own.

This allows auditors to bypass these specialized data science resources and tap directly into the power of their own data, effectively democratizing the access to financial analytics.

Microsoft CEO Satya Nadella shares that,

The core currency of any business going forward will be the ability to convert their data into AI that drives competitive advantage.

MindBridge is in complete agreement with Satya’s view, and I look forward to sharing more with you on this.

The human-AI relationship for CPAs: More, better, and faster

December 18, 2020February 7, 2019 by Roy Sarkar

Every week I interview entrepreneurs and experts from around the world to share their big idea about new forms of value creation and the potential we can unlock when technology augments the unique strengths of people to deliver remarkable impact.

Transforming financial auditing

I got inspired by the big idea behind MindBridge Ai, hence I invited CTO, Robin Grosset to my podcast. We explored the challenges in the financial auditing practice, and how, even after decades of automation, much of the practice is still very manual and sample based, leaving huge opportunities for fraud. Beyond that, we discussed why a human/machine approach will always provide the optimal combination to create exponential impact.

The thing that triggered me most from my interview with Robin

“The existing ways that we are analyzing or auditing financial transactions are inadequate with the rules based system, you’re only going to find something that you anticipate.

What’s the bigger value here?

If we only find what we anticipate, i.e., the cases that are highlighted based on the rules we have set, then what is the magnitude of what we are NOT catching? Robin addressed this by highlighting recent research from the Association of Certified Fraud Examiners. The number appears to be a number beyond imagination — but to put it in perspective — For every $1 we label as ‘fraud, misconduct, or irregularity,’ we’re missing out on $15. So, with current systems we’re only tracing 6.6%, and missing out on 93.4%! Translated, this is $500 for every person on the planet – every single year. And apparently (until recently) nobody was making a big issue about this, arguing ‘it’s not necessary to do anything different, this is the way we work’. This is a typical example of complacency and inertia in the workplace.

It’s about time the rules-based systems are going to be replaced by self-learning systems that are 24/7 active on finding new patterns, i.e., the +93% we’re missing out on. It’s the only way to win the fight against fraud. Doing nothing is not an option as data volumes and the number of channels we operate in keep increasing with extreme pace.

What significant fraud detection opportunity is raised?

What would be the impact on the economy and on society as a whole if this was solved? From my perspective, this is not only about finding the leaks in our systems, but very much about what we could do with the difference. Just look at the challenges we’re facing in health, education, or for example, public safety, simply because budgets are cut every single year. If these organizations would be able to 10x their ability to find fraud, misconduct, and irregularity — what could they do with that difference?

I would assume that there are many more areas like this to be uncovered — an opportunity and obligation for all of us to be sensitive about. I concur with Robin’s advice to look beyond the established conventions and existing standards. Only then will we be able to disrupt the status quo and increase (competitive) advantage.

On that notion — I concur 100% with Robin’s vision that the way to go about this is human-centric AI. In many industries, ‘black-box’ automation won’t work. Just think about how to explain black-box decisions in court? You’ll always need a person with a high-level understanding of the business context. Therefore, it’s about augmentation, not automation. Augmentation will allow human auditors to take their game to the next level, perform a better service to their clients, and be able to back their decisions up with clearly articulated evidence.

In other words: don’t be afraid that AI will take our jobs. It will not.

That said, doing nothing is not an option either: human auditors using AI will replace auditors who don’t. That’s an idea worth thinking about — also if you’re not an auditor.

Listen to the big idea behind MindBridge Ai, and why it has the potential to transform the way financial auditors deliver value.

Our year in stories: 2018

April 11, 2024December 28, 2018 by Roy Sarkar

We’re grateful to be a part of the world’s journey towards AI. Far more than an academic abstraction, 2018 was a leap forward in the practicalities of AI and machine learning across many different applications, with our own vision for the transformation of audit and financial analysis gaining momentum across the globe.

Here are some of the best and brightest spots of our year together in AI.

“ must be developed and used while respecting people’s autonomy, and with the goal of increasing people’s control over their lives and their surroundings.” – Montreal Declaration for a Responsible Development of AI

The social and ethical challenges of AI are just beginning to be realized, and the recent signing of the Montreal Declaration for a Responsible Development of AI is a big step forward in providing the framework for responsible technology development. As the first private sector signatory to the Declaration, we reinforced our commitment to responsible, human-centric AI systems.

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Through a passion for enabling technology, Samantha Bowling, CPA, CGMA, was named a 2018 Innovative Practitioner by CPA.com. As the first to successfully use AI in auditing for small businesses, non-profits, and local government, Samantha’s firm, Garbelman Winslow, leads the pack in improving processes and reducing the risk of material misstatements.

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“We need to figure how to free up more data so that AI can thrive.” – Leon Katsnelson, Director & CTO, Strategic Partnerships and Data Science Ecosystem, IBM, speaking at IMPACT AI

The inaugural IMPACT AI conference was held on May 24th, bringing industry thought leaders and technology experts to an audience of over 550 people. In addition to promoting AI education, the goal of the event was to increase and elevate more women in technology. Watch Navdeep Bains, Canadian Minister of Innovation, Science and Economic Development discuss the influence of AI and stay tuned for details on next year’s conference.

Industry reform was a big theme in accounting this year, with scandals for the Big Four and the UK Competition and Markets Authority recommending major shake-ups. Our CEO, Eli Fathi, reminded us how technology can play a critical role in reform.

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The first known case of AI helping to investigate a human CPA committing over $2.8M in embezzlement fraud was documented on the ACFE Insights blog.

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“AI is transforming the way auditors do business and the exponential pace of change is requiring CPAs to get up to speed quickly.” – Tom Hood, CPA, President & CEO, Maryland Association of CPAs

With dozens of events, webinars, seminars, and forums under our belts in 2018, two notable ones were our AI & the Future of Accounting roadshow, in partnership with the Canadian Trade Commissioner Service, and our expert CPA panel in December. While the roadshow introduced AI to audiences across eight cities, the expert panel delivered practical advice and recommendations tailored directly for auditors. We were also recognized by industry associations and media this year, including being selected as the Top New Product of 2018 by Accounting Today and the Best Machine Learning Solution for Regulatory Compliance by Central Banking.

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After a successful pilot with universities across North America, we launched our University Alliance Program in July to educate and train accounting students on the use of AI in auditing. As this year ends, the momentum will continue into 2019 with more than double the amount of institutions on board, over 1300 students completing the program, and a wealth of new curriculum materials and case studies being generated.

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Our partnerships with accounting firms around the world exploded, growing our user base to well over 200 organizations. Relationships such as with Garbelman Winslow, KNAV P.A., and Kreston Reeves, solidify the value that AI brings to auditing and help us continually improve the MindBridge platform.

For our development team, 2018 was a year of transition as we went from launching the first release of MindBridge Ai Auditor to continuous delivery of major new features for users. February saw new functionality such as Natural Language Processing (NLP) and accounts payable launched at a marquee event in partnership with the Canadian Trade Commissioner Service at Canada House in London, UK, while the rest of the year saw delivery of discrete pieces of value for users, such as interim audit reviews, the data ingestion wizard, and the amazing Filter Builder used by auditors to create their own AI-enabled tests and logic.

What will 2019 bring? We firmly believe that AI is still in its revolution stage for many, bringing aboard new players all the time, while others continue to work with AI-based audits every day. We’ll continue to share and educate along the way, and hope that you’ll let us know how we’re doing.