Stefano is a professional accountant with an extensive public accounting background across multiple industries (technology, real estate, manufacturing) and reporting frameworks, as well as experience in leading and evolving the Finance function for a technology scale-up. Prior to working at MindBridge where he helps create products for accountants by accountants, he worked as a Senior Audit Manager and Deloitte.
Artificial intelligence (AI) and machine learning (ML) technologies can streamline traditional audit procedures for Accounts Receivable (AR) and Accounts Payable (AP) in audits of financial statements.
This blog will consider applications of AI and ML technologies using the MindBridge platform for both substantive analytical procedures as well as detailed testing of specific items.
What does the MindBridge platform do?
MindBridge Ai Auditor, in addition to core general ledger analysis, includes dedicated AR and AP modules that automatically analyze subledger data and, without any scripting, provide high-value visualizations and transaction-level analysis of data.
These capabilities allow you to leverage subledger-level insights and anomalies as critical inputs to your audit procedures and identify risks of material misstatement.
How MindBridge empowers you to perform effective and substantive analytical procedures for AR and AP
Substantive analytical procedures can be a powerful complement to traditional sampling and external confirmations. That is, provided that the auditor is comfortable with the internal controls in place regarding purchasing and sales cycles and has validated the accuracy and completeness of the subledger data.
Trends and patterns
Ai Auditor allows you to visualize how monthly AR and AP balances or net monthly activity track over multiple years at customer vendor levels, and in aggregate. Consistent patterns in these trends in the face of consistent sales and purchasing patterns (respectively) may provide audit evidence that subledger information is not materially misstated.
Vendors and customers related to the entity subject to audit are flagged directly in the summary detail as well.
Key performance indicators
Days Outstanding and Turnover Ratios are calculated at the customer and vendor level and are visualized on a monthly basis, allowing you to identify where there are periods of potential distress or deteriorating quality (e.g. is the volume of cash receipts slowing?). Similar to ending balances and activity, you are also able to compare certain customers or vendors against each other along the lines of these metrics to expose patterns of interest.
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.). Consistent breakdown in the relative proportion of these aging buckets across multiple years of subledgers may provide audit evidence that subledger information is not materially misstated at the balance sheet date.
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 and whether they could be at risk of bad debt.
How MindBridge streamlines detailed testing of AR & AP subledger data
Navigating and querying transactional level data via the Data Table in Ai Auditor is a powerful and effective way to explore and validate subledger activity.
Control Points, which are various statistical, rules-based, and machine learning tests, are run against every transaction. 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 Control Point tests, you can efficiently identify relevant populations for sampling and have selections for external confirmation requests or alternative procedures testing (like subsequent receipts, for example) automatically identified on a risk-stratified basis. These selections can then be exported to Excel in one click to populate confirmation requests and/or to be included in supporting documentation.
The results of the transactional risk analysis may also be of particular interest to large entities and small businesses alike to provide insight into where there may be process improvements or gaps to consider in internal controls.
Take the first step towards AI-driven audit procedures on the AR and AP subledgers
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.
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.
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.
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.
COVID-19 — the coronavirus — has businesses around the world facing an unprecedented situation. First and foremost, we sincerely hope that you and your loved ones are safe and secure. Our current, shared situation certainly gives us pause and we recognize the importance of pulling through this together.
This new working environment most likely means that going into the office may not be viable for your audit teams, which means you may need to support remote talent.
We’re here to help you bring those audit engagements home.
We’re here for you
MindBridge continues to operate as usual and our teams are here to help you navigate any changes to your working model. As Ai Auditor is cloud-based and designed to help people in multiple locations work together, you’re already on your way to working remotely and having continuity in your processes.
We will update you with news and best practices to help you work productively. Keep this blog starred and check back for updates.
If you need to get in touch with support, contact your Customer Success Manager.
How to work remotely with Ai Auditor
Ai Auditor is built to support geographically diverse teams, meaning your audit team should be able to work together as they would normally. But as more team members start working from home, you may need to support additional geographically diverse users.
Considerations for expanding your infrastructure
Ensure that remote employees have the right equipment to log in to Ai Auditor, usually a modern laptop with an internet connection is enough.
Check with IT or your team on VPN requirements. While Ai Auditor does not require a VPN to log in, other files, applications, and communications tools to support your engagements might.
Best practices for engagements
No matter where your team is located, all the capabilities that you’re used to in Ai Auditor are available.
With the move to decentralized working arrangements, Ai Auditor provides the ability to manage your workflow transparently and for tasks to be allocated to team members. You can also use the tool to track responses and the real-time status of your testing plan at any point in time.
Has your client decided to postpone fieldwork or extend reporting timelines? Now could be the opportunity to think about the relationship between your methodologies and the appropriate approaches in Ai Auditor, including analysis and reporting. Your Customer Success Manager can help out here, and call upon one of our resident CPAs or CAs to find the best fit between methodology and technology.
Talk to your clients
Open and honest communication is critical during challenging times and your clients might be looking for support or wondering how the audit process may be impacted. This is an opportunity to connect with them on how they’re managing in these circumstances, reassuring them that you have state-of-the-art, cloud-based tools that maintain quality, support, and security in your service delivery.
Tips for maintaining productivity at home
Our very own Director, Transformation & Strategic Major Accounts (and fitness enthusiast), Gillian Fischer, created this guide for managing tasks, communication, and staying healthy while at home. We encourage you to take advantage of Gillian’s advice and let us know how you’re doing!
Rather than focus on uncertainty, now is the time to embrace change and innovate. By working through the unique challenges presented before us, we’ll find ourselves responsive, ready, and well-positioned for the time when this storm has passed (and it will).
While the nature of markets, organizations, and your clients themselves could be very different from what they look like today, as history has shown, a sustained focus and a real commitment to the future, prevails. It is this focus and commitment that will help organizations deliver differentiated value and relevance.
We’re here to help you deliver that value to your team and your clients.
