Audit Archives

Streamlining your going concern audit procedures

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

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

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

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

How can MindBridge add value and assist in this assessment?

A going concern refresher

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

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

Evaluating the going concern assumption under a new lens

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

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

How MindBridge helps evaluate going concern

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

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

Let’s review each of these capabilities.

Trends and patterns

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

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

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

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

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

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

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

Ratios

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

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

Some examples:

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

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

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

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

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

Interrogation of transaction-level data

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

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

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

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

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

Baldwin CPAs enhances technology-first services with MindBridge

September 29, 2024May 5, 2020 by Gillian Fischer

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

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

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

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

Core values

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

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

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

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

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

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

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

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

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

Internal discussions

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

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

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

External with clients

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

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

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

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

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

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

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

Myron D. Fisher, CPA, CGMA

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

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

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

May 26, 2022April 29, 2020 by Andrew Popp

Handshake illustration between government and public

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

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

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

The financial data explosion in government

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

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

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

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

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

Fraud hotlines & open data: First steps

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

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

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

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

Artificial intelligence: Creating government efficiencies

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

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

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

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

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

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

Artificial intelligence: Finding financial anomalies

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

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

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

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

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

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

Artificial intelligence: Restoring public trust

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

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

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

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

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

Quality audit vs. efficiency: How technology bridges the gap

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

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

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

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

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

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

The push-pull between audit quality and efficiency

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

Materiality

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

Focus on fraud

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

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

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

Big picture mindset

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

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

Normal Marks, author of Auditing That Matters

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

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

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

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

How auditors use AI-driven ratios to understand risk

Effective audit execution in remote work environments

Getting ready for AI-powered audit in 2020

Why the ICAEW Technology Accreditation Scheme matters to you

May 26, 2022March 31, 2020 by Roy Sarkar

ICAEW Technology Accreditation for MindBridge

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

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

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

The ICAEW methodology

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

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

Why it matters

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

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

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

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

Learn more about MindBridge Ai Auditor here.

How auditors use AI-driven financial ratios to understand risk

December 18, 2020March 23, 2020 by Kevin Smiley

In times of great uncertainty, we all look for a crystal ball.

Also known as an orbuculum or crystal sphere, legend has it that a crystal ball is a fortune-telling object. But the use of crystal balls to predict the future is pseudoscience and there’s no evidence that they can validly predict the future.

Time and again, businesses and their advisors have proven that monitoring key performance indicators and ratios can be helpful to understand current business health and, some might say, predict future events. With the advent of artificial intelligence and machine learning, we now have the ability to augment this work with large amounts of data and perform complex calculations using an unprecedented number of variables to increase its accuracy.

MindBridge Ai Auditor gives this power to auditors, helping to evaluate financial health, discover trends in risk, and enabling better decision making.

Here’s a quick rundown on some ratios within Ai Auditor and how they can help.

Current ratio

The current ratio is a liquidity ratio used to evaluate a company’s ability to meet its short-term debt obligations by measuring the adequacy of the company’s current resources to cover its debt. To calculate, you divide current assets by current liabilities.

Companies in crisis will likely see their current ratio decrease as they draw down lines of credit to stockpile cash and use cash to maintain operations while revenue and accounts receivable decline. An example is Boeing drawing down its full $13.8B line of credit to stockpile cash to maintain operations and deal with the damage the airline industry is experiencing.

A healthy company has a current ratio of more than 2, whereas a company who is in trouble has a current ratio of less than 1.

Operating cash flow to sales ratio

Even with a healthy current ratio, cash and cash flow must be monitored because of uncertainty on accounts receivable and cash is key to the success and survival of any business. The operating cash flow to sales ratio indicates a company’s ability to generate cash from its sales.

Ideally, as sales increase, operating cash flow should increase by the same. However, in a time of crisis, accounts receivable may take unusually longer to collect as the market manages cash more carefully and takes longer to pay. An example of this ratio decreasing is the difficulty that oil producers across the world are facing as demand for oil plummets, supply increases, and oil companies have a more difficult time generating cash from their sales.

Though it is normal to see change in a period of change, the higher the ratio the better, and it should find a level of consistency over time.

Debt to equity ratio

The debt to equity ratio is an indicator of a company’s financial health. This ratio is indicative of the company’s ability to meet financing obligations as well as its financing structure.

It will be normal in a crisis to see this ratio increase as companies borrow heavily against their lines of credit and other debt. Investors will also be hesitant to provide more equity in a crisis especially as the markets are in decline. Further, raising money via equity offerings at a time of depressed markets is expensive to businesses. This causes companies to rely on debt and since increasing debt brings an increasing ratio, lenders will eventually consider it unhealthy.

This is part of the reason that the Small Business Administration announced additional small business support of up to $2M loans to small businesses who qualify during the coronavirus (COVID-19) epidemic.

A ratio of about 1 is optimal where a ratio higher than 2 is considered to be unhealthy.

Cash flow to debt ratio

Cash flow is king to any business as no business can operate without an ability to pay their bills. The cash flow to debt ratio is often considered the best predictor of financial business failure. This ratio is calculated by dividing cash flow from operations by total debt. A higher ratio indicates a company is more able to cover its debt.

Often free cash flow is used rather than operating cash flow because this takes into account capital expenditures. With COVID-19 essentially grounding international air travel, airlines are seeing a huge decrease in cash flow to debt ratio, so much so that the airlines are seeking a $50B aid package from the US government.

A ratio higher than 1 is healthy but any value below 1 is indicative of an impending bankruptcy within a few years unless the company takes steps to improve its situation.

Another metric often used to predict potential bankruptcy is the Z-score, which is a combination of several financial ratios used to produce a single composite score.

What do these ratios have in common?

Other than the fact that they are in no way associated with crystal balls, they are very important to a business of any size in a time like this and they can be augmented and presented using MindBridge Ai Auditor.

As businesses create plans and seek advice from their advisors, Ai Auditor can present intuitive dashboards of ratios such as these (and more) by using a 100% complete set of general ledger data. In addition, by leveraging machine learning and AI, Ai Auditor can provide analytics of these ratios to evaluate deviations from expected ranges across 12 months of data. Additional analysis is also possible on more detailed ledger data such as accounts payable and accounts receivable ledgers.

So what’s with the crystal balls?

Whereas we have all been led to believe that the future-telling effect of crystal balls is pseudoscience—which very well may be true—there do exist approaches and high-tech tools that enable the use of massive data sets to help gain incremental clarity about the future.

Ai Auditor isn’t pseudoscience, it’s right here, right now.

COVID-19 update: Effective audits & remote work

May 26, 2022March 19, 2020 by Stefano Colaiacovo, CPA, CA

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!

Read Gillian’s productivity tips >

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.

AI for government finance: Understanding value & barriers

May 26, 2022January 23, 2020 by Andrew Popp

Klaus Schwab, the Founder and Chairman of the World Economic Forum, shares in his book, “The Fourth Industrial Revolution,” that artificial intelligence (AI) will perform 30% of corporate audits by 2025. While any estimate of change is just that, an estimate, the pace of change is governed by the benefits that result from the application of any given technology, weighed against the forces opposing it.

In the case of government finance and accounting, AI is being embraced at an astounding rate, and may even accelerate if we are able to overcome some of the forces opposing the change.

The benefits of AI to government are being proven out in its early usage today, namely employee efficiency, risk mitigation, and operational insights. These three value propositions are driving the rapid adoption of AI as a financial control, an audit tool, and a forecasting function, and will help ensure that governments at all levels are better managing the public’s finances.

What AI brings to government finance and accounting

Take the case of a large Canadian federal government department. Financial analysts, by policy, are asked to manually review every travel expense that exceeds $1000 CDN. This is the type of task that is ripe for automation via AI, as AI can rapidly analyze all the expenses at once and determine those that are the riskiest to review. AI allows the department to be more efficient, as it can prioritize its resources to review the riskiest expenses, the ones most likely to contain an error, omission, or a violation of the expense rules, and automatically approve the vanilla claims.

While operational efficiency has the greatest monetary value to government organizations, the value of risk mitigation centers around the trust placed in government financial operations. Whether a financial error, omission, or fraud is found to be above or below the material threshold of the organization, the impact on the public’s perception of the competence of its management and staff is always put into question.

With financial data growing at an exponential rate, (PwC estimated that 18 Zettabytes of financial services information was created worldwide in 2018), current audit and control techniques, including random sampling, are constantly failing to detect mistakes and fraud. AI provides a means of reviewing 100% of the data, allowing governments to find risky transactions and the associated parties, before it hits the press.

The operational insights provided by AI offer value to both the controller and the budget analyst. Controllers can use the risk ranking of transactions in a given year and visualize that risk against past years to spot areas of weakness in the control systems. Budget analysts can customize and visualize key performance indicators for the organization and use multiple years of data to predict how those ratios should evolve, and how they are tracking against them in any given quarter. Exceptions are highlighted so that action can be taken to apply additional budget or distribute resources to meet shortfalls.

Overcoming the human barriers to successful AI deployment

While these value propositions are helping speed the deployment of AI in government finance and audit, there are a number of human-centered forces that are putting a brake on wider adoption. Trust and transparency in the deployment of AI is one force against its adoption. How organizations change their processes to integrate AI is another. Lastly, the development of employee skills will ultimately predict the speed at which AI is adopted in organizations.

The Canadian government has proven itself a world leader in its adoption of the Algorithmic Impact Assessment (AIA) as a means of mandating transparency in the AI algorithms and how they are applied in any automated government service deployment. This move lays the basis for government departments to take advantage of AI automation that is explainable to the public, ensuring AI use can grow with appropriate oversight, and allowing trust to develop as a matter of process and not accident. Other countries have taken note of what Canada has done and are either adopting the Canadian AIA or are creating their own similar framework.

While building trust, it is also critical that government processes adapt to integrate AI. In the case of applying AI to reviewing $1000 expenses above, the policy governing the expense review process will have to be adapted to capture AI’s role. Policy, as we all know, doesn’t change overnight. The appropriate groups have to gather and review policy changes in the face of AI. There is also the issue of global and national regulatory standards that govern finance and accounting, particularly how and where AI-driven analysis can play a role. These conversations for change have already started, with the first major AI-driven changes to the audit standards process starting in 2020.

Skills are a huge part of any technology change. Just as blacksmiths evolved into being mechanics with the advent of the motor vehicle in the 1900s, financial officers and accountants are going to evolve into data analytics experts in the world of AI. One critical skill set is going to include the appreciation of numerical algorithms and analytical techniques and how they apply to the financial situation they are assessing. This doesn’t mean they have to become an algorithmic expert, or know how an algorithm is coded, any more than a mechanic needs to know how a motor vehicle is built. However, they need to know when their vehicle is good for driving on a paved road, and when it’s good for going off-road.

Data is the fuel of the future, and algorithms are the engines that will consume it.

It’s not a question of “if” AI will transform government finance and accounting, but “when”. With a strong set of value propositions driving the change, and the barriers of trust, adoption, and skills being diminished with increased awareness, leadership, and training, AI will be well enshrined in government before Schwab’s predicted date of 2025.

For a deeper dive into how AI helps government audits and financial management, watch my on-demand webinar now.

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

Getting ready for AI-powered audit in 2020

December 18, 2020January 14, 2020 by Roy Sarkar

You’re in the minority if you haven’t heard of artificial intelligence (AI). Yet the accounting profession has a long way to go in terms of adoption. AI is a popular conversation piece for industry bodies such as the AICPA, CPA Australia, ICAEW, and PCAOB, and more firms are deploying the technology today than ever before. But most firm leaders still struggle to understand the impacts of AI on their staff, processes, and clients.

What are the implications of AI for your audit practice in 2020?

We’ll break down the answer for two types of firms: Those that are thinking about adopting AI this year and those that are already using MindBridge Ai Auditor.

Thinking about adopting AI in 2020

Based on interviews with our clients, firms consider making the shift towards AI for the following reasons:

We’ve heard about the value of AI from others
We hope AI will create new opportunities to attract and grow clients
We don’t want to be left behind

Firms are less clear on how AI transforms their client engagement process and may not understand that it’s about the people as much as the technology. Firms that are thinking about making the shift to AI need to:

Raise their awareness and understanding of AI for audit
Align their strategic goals on providing more value to clients through AI
Build up their data skills and capacity to get the most out of AI

In other words, as AI and machine learning can extract anomalies in client data (i.e., potentially risky transactions in the general ledger and subledgers) that were previously unheard of, auditors need to build up their data analytics skills and consider new ways of working with clients. With AI, the focus is more on risk-based analysis and audit planning than traditional rules and statistical sampling.

This means that more data leads to more effective results. It’s wise to think about exporting samples of client financial data as early as possible. The level of detail that can be analyzed with AI is likely beyond what was included in your previous PBC requests and it may take your client a few iterations to get the exports required. We recommend getting the sample exports in advance of your fieldwork so your engagement teams can run an interim AI analysis and provide immediate value to clients as fieldwork begins. The up-front information gathered here will be useful throughout the engagement.

It’s also prudent to set realistic expectations for your firm and engagement teams if you’re starting your AI journey during busy season. Focus your first few engagements on clients that are using common ERP systems, such as QuickBooks or Dynamics, to minimize time spent on generating data exports. This enables your engagement team to spend more time interpreting and understanding the AI analysis results and delivering value to your client with AI-expanded insights.

Using AI for audit now

To best prepare for the upcoming busy season using MindBridge Ai Auditor, it’s important to consider these three actions:

Prepare your client and their data. When obtaining client data, know what you need, why you need it, and understand that more data is better. To help you prepare, our knowledge base has an overview of client data requirements, data checklists, and ERP export guides. Remember that the earlier you can get data, the better. Even if year-end data isn’t available, you can load previous year, interim data, and complete accounting mapping ahead of time.

Perform risk assessment and planning. We recommend the following steps:

Once client data is loaded, prepare the audit plan, create the necessary tests, and save them all using the Filter Builder.
Performing a risk assessment of your client’s data will identify the areas to test and using the dynamic audit plan will help assign tasks and facilitate testing procedures during fieldwork.
Reviewing the analytics, ratios, and graphs with current and past data will call out any items that need to be addressed during the audit.
Leverage the trending reports and ratios to enhance your working papers and provide additional value back to your client.

Engage our customer success team as early as possible. When interacting with your Customer Success Manager (CSM), it’s important to set clear timing expectations, including fieldwork dates. Your CSM acts like another member of your engagement team: Your busy season is their busy season. Setting them up for success early helps them be more efficient and effective in treating requests.

Need help? At any time, you can check out our knowledge base or join a live chat with a CSM using MindBridge Assist.

Remember that AI is as much about the people as it is the technology. Whether it’s your own staff, your client, or by working with our CSMs, the successful delivery of AI-based value depends entirely on putting the human at the center of the audit.

As MindBridge founder Solon Angel states:

“The purpose of AI or any new technology is to save time, headaches, and unnecessary effort on humans. Be mindful to invest these savings on your well being as the menial work becomes less burdensome—having a healthy body and mental state allows you to think with higher quality.”

Learn more about MindBridge Ai Auditor here.

The auditor’s fallacy: The law of small numbers

December 18, 2020May 13, 2019 by Erik McBain, CFA

Humans have used simple statistical sampling for millennia to make generalized sense of the world around us. Living in a resource-constrained world, statisticians gave emperors, surveyors, and accountants a simple workaround to the prohibitively intensive process of counting, checking, and validating everything. Sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of a much larger population.

Random sampling is an old idea, mentioned several times in the Bible with the word “census,” derived from the Latin word censere – “to estimate”. One of the world’s earliest preserved censuses was held in China in 2 AD during the Han Dynasty and appeared later in Ancient Egypt and Greece as a means of tallying or estimating population characteristics and demographics. Historically, the immense benefits of sampling’s simplicity outweighed any cost to accuracy. “Close enough” was good enough.

Fast forward to 2019 and we’re living in a tremendously different world with exploding data volumes and complexity. One domain where this is particularly problematic is the world of audit and assurance, where achieving a passable level of reasonable assurance is increasingly challenging.

For MindBridge Ai, the most obvious place to apply our advanced analytics and breakthroughs in machine learning is the audit world. To help everyone move toward a more wholesome and comprehensive risk analysis, enabling more informed decisions.

Simply, MindBridge Ai Auditor can be thought of as an advanced transaction analysis platform and decision-making tool that amplifies our ability to make sense of the complex and data-saturated world around us. Within our digital world, it’s now possible to pivot from reliance on sampling to algorithmically analyzing everything in a population.

Why is this evolution a good idea?

Why audit sampling doesn’t work

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

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

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

Humans are not good intuitive statisticians. For an audit professional, sampling variation is not a curiosity, but rather it’s a nuisance and a costly obstacle that turns the undertaking of every audit engagement into a risky gamble.
There’s a strong natural bias towards believing that small samples closely resemble the population from which they are drawn. As humans, we are prone to exaggerate the consistency and coherence of what we see. The exaggerated faith of auditors in what can be learned from a few observations is closely related to the halo effect. The sense we often get is that we understand a problem or person or situation when we actually know very little.

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

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

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

Minimizing risk exposure

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

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

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

Exaggerated faith in small numbers is only one example of a more general illusion – we pay more attention to the content of messages than to information about their reliability. As a result, we end up with a view of the world around us that is simpler and more coherent than the data justifies. Jumping to conclusions is a safer sport in the world of our imaginations than it is in reality.
Statistics produce many observations that appear to beg for a causal explanation but do not lend themselves to such an explanation. Many facts of the world are due to chance including accidents of sampling. Causal explanations of chance events are inevitably wrong.

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

Top 3 actions to take before the 2019 busy season

December 18, 2020January 14, 2019 by Roy Sarkar

To best prepare for the upcoming busy season using MindBridge Ai Auditor, we’ve prepared this list of key actions to take, based on feedback received from users and their clients. Take note, plan your actions, and if you have any questions, please don’t hesitate to contact us for help.

Key actions

Prepare your client and their data
Risk assessment and planning
Engage the MindBridge customer success team as early as possible

Prepare your client for AI-based audit

Planning and communicating with your client during busy season is always a good practice and it’s even more critical for firms going through their first artificial intelligence-based audit. Most of the steps will be familiar, some are new, so you want to make sure that everyone’s on the same page as far as activities, expectations, and goals.

Key to working with Ai Auditor is letting your client know that you’re moving to a data-driven audit approach this year and that the earlier their data is submitted, the more effective the audit. Often, clients aren’t as prepared as auditors would like them to be, so consider these initial activities to get them up to speed:

Have the conversation that this audit makes use of artificial intelligence technology to provide 100% transaction coverage and to identify anomalies and risk areas in their data that may have been missed by their accountant or controller. Not only is this better for your firm’s brand, it boosts your client’s credibility.
Obtain and ingest your client’s data into Ai Auditor as soon as possible. Often it takes a few times to get the right data from your client’s IT team (such as knowing what fields to export) and different enterprise resource planning (ERP) systems require different amounts of effort to extract the information. Our Customer Success Managers (CSM) are always available to help and by engaging their expertise early, any issues in getting the data from client systems into Ai Auditor can be resolved before it’s too late, better preparing all teams for a more efficient audit later.
To support the analytics/graphs/ratio and forecast and trending data in Ai Auditor, we recommend that you obtain the current year plus four prior years worth of client data. These insights will be something new for your client and provide much-needed value.

These critical early efforts during busy season will ensure that the transition to a data-driven audit will be smooth for both you and your client.

Risk assessment and planning

Planning for an audit is often performed in a black box, where the auditor has very little insight into client operations until the data is received and even then, assessment can be a difficult process. Using Ai Auditor gives you a deeper level of insights into client data than traditional methods and makes planning more effective, so consider these actions:

Prepare for your initial discussions with the senior finance official by running their data through Ai Auditor to better understand their profile and identify areas of interest to have conversations about. Not only does this demonstrate your knowledge of the client’s operations, it helps to have any difficult conversations early rather than waiting until the rush of the audit process.
Once client data is loaded, prepare the audit plan, create the necessary tests, and save them all in Ai Auditor using the Filter Builder feature. Performing a risk assessment of your client’s data will identify areas to test during the audit and helps create the test plans to execute. Reviewing the analytics, ratios, and graphs with current and past data will call out any items that need to be addressed during the audit. Using the Filter Builder feature allows you to create any standard tests, such as Journal Entry testing, selection of AP and AR confirmations, etc., and save them to be used once the final data is ingested – saving a tremendous amount of time. It’s also good to know that any sample selected for existence (also known as selecting from the system) can be chosen within Ai Auditor.
From a fieldwork perspective, having client data within Ai Auditor allows you to do all your audit tracing through to the platform, saving you time and the need to go back to your client for additional clarifications.

Engage our customer success team early

A common theme here is to contact your assigned CSM as early as possible, to ensure a smooth data export and import, understand the features available to you in Ai Auditor, and to best prepare for client conversations and reporting. The first step is to let your CSM know when to expect your client’s data, to help with planning during busy season.

We’re here to help and we have plenty of experience across different ERP systems, environments, and types of clients, so to avoid any pain down the road, engaging earlier helps us all.Contact us now to get started with your busy season planning.

Changing the World with Small Teams

December 18, 2020June 27, 2018 by Robin Grosset

I have had an email signature for many years which has a cheesy quote at the end. It reads “never doubt that a small group of thoughtful committed people can change the world.” The actual quote is longer than this, it is attributed to Margaret Mead who was an anthropologist, the full version is “Never doubt that a small group of thoughtful, committed citizens can change the world; indeed, it’s the only thing that ever has. ”

A colleague of mine recently asked me if larger teams was the key to success in a large company. I wondered if this colleague had ever read to the end of one my emails. Were they trolling me?

The core sentiment of the quote is that only small, thoughtful and committed groups of people succeed in making significant change. If you work in a tech company this is important because it applies most of all to the technology disruption around us today. Cloud computing and Artificial Intelligence are changing the face of many industries. Its not the older, larger and established companies who are necessarily leading this change, its often the smaller nimble organizations who have the focus to figure out and lead this disruption.

Quite a few years ago now I founded a small high tech startup that was fairly quickly acquired by Cognos who themselves were acquired a year or so later by IBM. Code I wrote in my basement in West London ended up 10 years later being a core piece of technology in tens of thousands of installations. Large scale tech companies are great for scaling ideas but my most important lesson working in small startups and big corporations was that ideas themselves and solving hard problems is not necessarily about big teams. In fact, its almost never about big teams.

Why is this so?

The first reason is quality over quantity. The adage in the industry is a great developer is three times faster at delivering software than an average developer. While this is true in my experience there is a little more to it. In small teams it is possible to handpick team members with the right mix of talents. With the right people with complimentary skill sets and respectful of each other’s expertise you can create collaborative teams that can easily out pace much larger groups.

Small teams with diverse and complimentary skill sets also foster something called the Medici effect. It relates back to team collaboration. Diversity in thinking and the connection of ideas through close knit face to face communication is often what leads to new innovation.

As teams grow they can impede themselves as a result of having too much overhead in communication. Its very hard to effectively have a discussion with 25 people, let alone 100. This is why effective software teams rarely are this big, and instead are divided into smaller mission focused groups.

The core point is, if you think you need a bigger team to solve a difficult problem, you are most likely wrong. Think again. This type of thought process leads to inaction and if you are in a startup this may result in failure. Sometimes constraints create the best solutions, so keep working at it. Time and again I have seen hard problems solved by small groups, often with simple approaches. My hopeful message to entrepreneurs and startups is not only can you solve hard problems that big companies may not be able to solve but you have the capacity and ability to disrupt entire industries.

Keep thinking you can change the world. Remember *only* small teams can do this.