Audit Archives

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.

Interview with Ryan Teeter, University of Pittsburgh

December 18, 2020June 4, 2018 by Stefan Celeski

What is your position and what do you teach at your University?

I am Ryan Teeter, I teach Accounting Information Systems in particular, as well as Auditing and Data Analytic at the University of Pittsburg. I am a Clinical Assistance Professor, that just means I am teaching a lot of courses and I am always looking for ways to incorporate technology into my class room and into the projects which I have my students do. A lot of the work we do are very hands on, most of it is what we call experience-based learning. It is focused on getting the students hands on using various accounting and auditing tools, overcoming any challenges with learning a particular tool, and gaining from that experience, to improve their understanding of accounting and auditing.

Which course did you pilot MindBridge Ai Auditor in, and how many students did you have in the class?

I piloted MindBridge in a graduate course on data analytics for accounting. The course is titled Accounting Data Analytics and is part of our Master of Accountancy program at the University of Pittsburgh. We had 28 students this semester and next semester we will be doubling the capacity, so we will have about 50 students participating next time.

It sounds like there is a lot of demand for this course, is it a competitive process to be accepted?

There is cut-off for this program, it is an elective course, there is a lot of demand for it. So that’s why we’ll be increasing capacity in the future forward.

What was your motivation to pilot MindBridge Ai Auditor?

In the Data Analytics course we spend about half of the course teaching fundamental data analytics topics, terminology and foundation. We’re talking about asking the right questions, going through and cleaning up data, data quality issues, particularly how it relates to an audit. We spend a few weeks on different types of models, from classifications to regression to clustering and profiling data and so forth. Next, we move to interpreting the results and generating visualizations for communicating the results of the data analysis to decision makers, management and leadership positions within organizations.

By the time we’ve moved through those fundamentals we have talked about topics like machine learning, different types of risk scores, we have talked about expert models and artificial intelligence. And then, the second half of the course we move into more domain specific topics. We spend a couple of weeks on audit analytics, management accounting analytics, financial statement analysis, and then in the auditing section we’re looking for something more than just the traditional CAATs, computer assisted audit techniques. So, we introduce students to things like double payment checking and fuzzy matching and some of the probabilistic models for outlier detection. By this point however I am really looking for ways to take that to the next level and find a convergence of those different technologies into one place.

I thought that MindBridge was particularly useful for illustrating the different topics we were talking about like Benford’s testing and outlier detection, but also for the concept of discovering the really risky items. So for the platform to set those risk scores, and make it apparent to the auditor as they go through and evaluate ledgers and journals, was an important discovery concept.

After having used MindBridge Ai Auditor in your curriculum, how was your overall experience?

The experience was really good. The software is pretty straightforward aside from some minor issues with importing and running the analytics, meaning just the time that it took to re-evaluate the ledger once we changed some of the risk score items. The students were very satisfied with the program, they liked that they were able to drill into the risky transactions and see exactly what caused some items to be flagged as a high or medium risk. The interface was fairly intuitive.

I would say the only negative is that it’s almost too simple in a sense, because it is so user friendly. You can see the risk scores and see what triggered the scores and then you’re a kind of done. I would like, from an illustrative perspective, to be able to go into a little more depth into the different analysis that are being performed, popping open the hood a little bit to see how this is all working. But otherwise, the students were very satisfied with it and they could see the applicable use of data analytics for the ledger in that particular case.

How was the feedback from your students?

Overwhelmingly the students found it to be eye-opening that they could examine what went into the risk scoring. They liked that they had the control to explore different aspects of the data if they wanted to, so if they wanted to focus more on outlier detection or zero in on individuals or keywords, that the platform enabled them to do so. They liked the flexibility that the platform offered. I think with the cases that were provided they had some clear-cut examples to examine, it would be really interesting to see what they could do with exploring data that was a little more ambitious.

What’s next for you and MindBridge Ai? Will you use Ai Auditor as part of your curriculum again?

I was very pleased with the MindBridge Ai presentations and the illustrative applications of the platform in my Data Analytics course. I really would like to extend it into my undergraduate Accounting Information Systems course as well. We talk about auditing, and audit analytics and risk a bit more in that course, at a basic level. Being able to have something that is straight forward and shows the different techniques while also piquing the undergraduates interest toward data analysis, risk scoring and applied statistics area that would be very useful.

I have a text book written with McGraw Hill on Data Analytics for Accounting which comes out in May. My expectation currently is to add supplement material that I would like to develop for future editions of the text book that may incorporate MindBridge Ai Auditor. It’s all still very preliminary, but for illustrative purposes it’s an intuitive and wonderful example of applying data analytics in accounting.

CPA Firm Taps MindBridge Ai’s technology in Audit as a Competitive Advantage

June 2, 2022April 13, 2018 by John Colthart

An interview with Lisa Zimeskal, CPA, Partner, Hoffman & Brobst, PLLP

According to a survey from the International Federation of Accountants (IFAC), smaller accounting firms are facing significant challenges. Attracting new clients, keeping up with new regulations and standards, and cost pressures versus competitors, were among the top concerns of these firms.

To combat these challenges, Hoffman & Brobst, PLLP, a firm of five partners, decided to embrace artificial intelligence (AI) in their audit services, as a differentiating advantage for their clients, and the firm now use the extensible MindBridge Ai Auditor platform in their audit process.

Ai Auditor is an award winning platform that empowers auditors to detect anomalies in financial data, with speed, efficiency and completeness. The platform leverages expert taught machine learning and AI to ingest and analyze 100% of financial data, as opposed to traditional sampling techniques, to provide higher assurance along with cost savings. Armed with greater insights and boosted efficiency, auditors can focus on what matters most – providing higher value-added services and guidance to their clients.

John Colthart, VP of Growth at MindBridge Ai, recently spoke with Lisa Zimeskal, CPA, Partner, Hoffman & Brobst, PLLP about how AI tools can benefit small firms. Here’s what she had to say.

John Colthart: Tell us about Hoffman & Brobst, PLLP.

Lisa: Hoffman & Brobst, PLLP is a full-service accounting firm in Southwest Minnesota. We provide audit, tax preparation, compilation and review services, in addition to payroll processing and third-party retirement plan administration services.
John Colthart: What do you see as your biggest opportunity?

Lisa: Our biggest opportunity is the continued growth in our industry. We are embracing growth in our firm and we are looking to expand our services when the opportunities arise.

John Colthart: What do you see as the biggest threat or challenge?

Lisa: Our biggest challenge is attracting qualified staff to our practice because of our rural location.

John Colthart: How do you plan to address it?

Lisa: We are currently looking into more options with technology for a remote work environment.

John Colthart: What made you choose MindBridge Ai Auditor? What are the features that you plan to use?

Lisa: We chose MindBridge because we are excited about offering a new value-added service to our clients. This is cutting-edge technology, and it is not something in which others in our area are participating. The entire concept is new to us, but initially, we are planning to leverage the risk-based assessment of transactions. This approach will be enable us to review by-transaction risk in a much more effective and efficient approach than we currently utilize.

AI Will Not Replace Auditors, but Auditors Using AI Will Replace Those Not Using AI

December 18, 2020March 2, 2018 by John Bednarek

The more things change, the more they stay the same (at least that is what my mom would say). This wisdom can only be partially applied to the world of auditing. With the explosive growth of (big) data, and with an ever more connected, globalized world, the manner in which we approach auditing must change, and it must change significantly. The roles and processes of auditing, and the people working in that area will remain the same, but the way we conduct audits will change to address these new realities.

The good news is that, armed with new technologies such as Machine Learning and Artificial Intelligence, auditors can be empowered to face these new challenges — while potentially delivering better assurances on the state of their client’s business, and providing more value-added services than ever before.

The purpose of this blog post is to look at the role of auditors and how it is changing in today’s new landscape.

Auditing is a process in which one party examines another party’s information to ensure that it is fairly stated (Loughran).

One of the core methodologies of the modern audit process is sampling, which draws conclusions about a data population by examining a subset of the data. The reason that sampling is relied upon is one of cost and time, it would simply be too expensive, or too time consuming to audit all of the data manually. There are inherent weaknesses to sampling however, as human bias and the possibility of erroneous decisions based on the conclusions resulting from the examination of only a sample of data, present real problems.

As my colleague Robin Grosset, CTO of MindBridge Ai once said, “Sampling is a coping mechanism for dealing with large data, because it is humanly not possible to examine each and every transaction.” The alternative is to examine all of the data, which can be onerous and not very practical.

The situation is further compounded by the explosive surge of big data. The International Data Corporation (IDC) says that the amount of information stored in the world’s IT systems is doubling about every two years. By the year 2020, the total amount of data will be enough to fill a stack of tablets that reaches from the earth to the moon 6.6 times.

How can I, as an auditor, possibly provide reasonable assurance that financial data is free from material irregularities, when faced with these new challenges introduced by big data?

You can fight fire with fire in this scenario. The ability to process big data is bolstered by recent technological advancements (i.e. microprocessors, internet, software) and now we can begin to leverage cutting edge technologies such as AI, to address these challenges. According to a report by Forbes, an auditor’s key role is to determine a company’s most significant threats, however traditionally auditors have devoted less than 25 percent of their time to risk analysis.

At MindBridge Ai, we recognized this challenge early on, and have developed a purpose-built platform for auditors to utilize machine learning and AI, along with more traditional methodologies such as business rules and statistical methodologies. Our Ai Auditor platform can ingest 100% of financial data and identify any anomalies. Thus, we improve the audit process by not just making it more complete, but by making it faster and more effective.

What does this mean to an auditor? It means an auditor can be more effective and efficient, while reducing their costs and providing greater insight and assurance to their clients. If any regulators have questions about how the audit sampling was conducted, you can simply point to the MindBridge Ai Auditor platform and show them the algorithms that were applied. An auditor in this scenario can now move up the value chain and become a true strategic business advisor to their clients.

For those auditors who do not embrace AI, their future will be more burdensome, and they will certainly be surpassed by their colleagues who are using AI to their advantage. Such is the cycle of innovation, where relics of the video rental and retail industries serve as a testament to the velocity at which antiquated approaches are being replaced by some more technologically sophisticated alternatives.

As I stated at the beginning, “The more things change, the more they stay the same.” In the case of auditors, we can see that role is more crucial today and the future than ever before. Due to the rapidly changing landscape however, of big data and a connected world, the only way to manage that complexity is with technologies such as AI. For those who do not embrace AI, they will be eventually replaced by those auditors that have embraced it. AI in auditing is here today and is making inroads faster than you may know!

Coffee With a Professional in Accounting: Taryn Abate

June 2, 2022January 16, 2018 by Solon Angel

An interview with Taryn Abate, Director, Audit & Assurance — Research, Guidance and Support, Chartered Professional Accountants of Canada

I recently met with Taryn Abate, Director, Audit & Assurance – Research, Guidance and Support at Chartered Professional Accountants of Canada (CPA Canada) at the Digital CPA conference.

This conference is hosted by CPA.com and the AICPA and attracts leaders within the industry who truly embrace the digital CPA era, so no doubt it made sense for MindBridge to attend.

Taryn is a thought leader in the CPA space and after completing her CA designation in 2009, has held key positions in firms like MNP LLP, the Institute of Chartered Professional Accountants (CPA) of Ontario, and now with CPA Canada.

I chatted with Taryn about her thoughts on the massive disruption occurring in the audit profession and how innovative technologies like Artificial Intelligence (AI) and blockchain can be leveraged for auditors to graduate towards more of an advisory role. Below is an excerpt from our discussion.

Solon: What did you like the most out of the last two days, out of all the conversations, vendors’ booths, and the work groups? If you had to come back home and say, “I spent two days with hundreds of CPAs and here’s how I feel about the profession,” what would you say?

Taryn: The keynotes from both days were a fantastic frame of reference for the conference and the profession.

Mark Randolph, Netflix co-founder, on the first day talked about innovation; the importance of having an idea, having a tolerance for risk, and having confidence and optimism in everything you do.

Barry Melancon, CEO of the American Institute of CPAs (AICPA), started off the second day by discussing the state of the profession. Technology’s impact on the auditing profession was a trend/theme throughout the conference, focusing on blockchain, AI and data analytics. It was pretty much in every session that I went to. I heard discussions on how practitioners are going to start to use these technologies. It was amazing to hear everyone embrace the idea that these technologies are here, and the fact that it is going to change how we audit and what we are auditing.

Solon: Of the discussions you had with people in the room, do you agree that everyone is more or less ready for innovation? Do you think they know where to start?

Taryn: We have a tiny segment of the bigger population here. These practitioners have accepted the idea of digital transformation and understand the benefits of the new technologies. But, not everyone in the business community has reached this stage. We’re on a journey here, and that’s why CPA Canada has identified audit as a strategic priority. We are working with stakeholders around the globe to understand current challenges facing the profession, identify future needs and explore how the profession must evolve to meet these challenges and opportunities.

CPA Canada and other organizations such as the AICPA are preparing guidance to help members and other stakeholders adapt to an ever-evolving operating environment. Stay tuned for upcoming publications on blockchain and AI and the implication of these technologies on the audit and assurance profession.

Solon: I have to say I’m very impressed with CPA Canada.

Taryn: Me too, and thank you. We are committed to helping our profession and members navigate this changing environment. Our goal is to ensure that our profession and members are well-positioned to take advantage of opportunities associated the evolving technologies. Stay tuned.

Solon: CPA Canada has a TV ad — the one about driving change with the CD player guy, and I loved it. I also saw a poster at the Billy Bishop Airport that asks, “Will machine learning replace human know-how?” and then says, “Ask a CPA.” I have to ask you about that one. Who had that idea?

Taryn: That’s from our current national brand campaign that asks tough and timely questions about the changing business landscape. I think it’s great. It catches your interest and makes you think about innovation and the future and what organizations need to do to stay relevant.

Canadian CPAs have a solid foundation of technical and enabling skills, and we see those skills becoming even more relevant in a world that is increasingly volatile, uncertain and complex.

That’s why preparing the profession for the future is a key focus at CPA Canada. It’s about understanding emerging technologies and utilizing the data, identifying trends and bringing insight into strategy development to help organizations achieve long-term success. As the pace of innovation increases and new technologies continue to spread, the disruption of business models and processes will require rapid adaptation.

Solon: That’s for sure. I was just flying back from a conference reassuring people that AI is not going to take their job away, and then I see that ad…

Taryn: You see an article one day that says AI or blockchain is going to replace audit, completely replace 88% of jobs. Then last week, Forbes produces an article saying in four out of five companies, AI will create new jobs.

We need to reinforce that with technology comes change but that can include opportunities. This is again why helping our members and other stakeholders prepare for the future is so important to CPA Canada.

Solon: I think there are a lot more job opportunities. That is my company MindBridge’s view and vision too. We believe there is going to be a surge of demand that will be for more digitalized solutions that will be easier to access. It is also going to generate new capacities within the marketplace. That’s my personal view too, so I’m glad that you believe in it as well…but is it fair to say that there is fear anytime there’s innovation?

Taryn: Yes. People were scared when computers came in, but they made us smarter and more efficient. And, while computers may have led to the elimination of some jobs, they are responsible for the creation of many others.

Solon: People here at DCPA ‘17 are embracing innovation, but they’re not sure exactly where to start all the time. They rely a lot on the product community here and their peers to guide them. You live and breathe the profession, go to many industry events and know the mandate of CPA Canada. How would you explain the benefits of AI and blockchain technologies to a senior partner that is in the grind, has a medium-sized practice and doesn’t have time to come to these conferences? What advice would you give him or her to understand what the profession is going through right now and how it may change?

Taryn: My advice to all would be to prepare yourself for challenges posed by globalization and technology. Stay abreast of changes in the business world, get familiar with new technologies and proactively assess the implications.

For small to medium-sized practices that may be resource-constrained, I encourage them to check out our available resources and be aware that more are on the way.

The most important call to action is to start now when it comes to assessing what the operating landscape is going to look like for your organization. Identify the challenges and meet them head-on.

Solon: To your point, some of the more prominent partners who are engaging with our vision are moving pretty fast on that.

Taryn: In general terms, it’s important to be aware of everything that’s happening out there. Be honest to yourself and don’t pretend that change hasn’t arrived. More specifically, as an auditor, you look at the problems in a company and help your clients address any pain points, and for that, you must use the appropriate tools. If you don’t have the proper tools, guidance or information, help is available. Taking action today can pave the way for long-term success.

Solon: I recently met a very cool CPA, Natalie Quan, CFO of the CalCPA Association, who was also the controller for the San Francisco Ballet. Who is the coolest CPA you have ever met?

Taryn: We meet a lot of interesting and inspiring people through the work we do at CPA Canada. We work with subject matter experts in Canada and globally, to help the profession and for that reason, I couldn’t pick just one.

Solon: What is the most exciting story you heard?

Taryn: Technology today moves rapidly, resulting in many exciting stories.

Of particular note, I would reference Alan Wunsche, a blockchain expert CPA Canada works with, and he launched Token Funder on November 1st with the Ontario Securities Commission; the province’s first regulated token offering.

My job is a blessing because I get to work with many respected thought leaders and hear how they are moving forward in today’s global economy. It’s amazing to see the innovation that is happening all around us.

Solon: You seem to be doing a great job, and you seem to enjoy it so, congratulations.

Taryn: I certainly do!

Solon: Thank you for your time. The readers are going to enjoy this blog post.

Originally posted on Solon Angel’s Linkedin and can be accessed here.

How Gilbert Associates is Leveraging AI to Provide More Value to their Audit Clients

June 2, 2022January 8, 2018 by John Colthart

Recent advancements in Artificial Intelligence (AI) and machine learning are ushering in a new era of possibility for the audit function globally. By touching all transactions in a data set, audit firms around the world are using this cutting-edge technology to drive tremendous efficiency and effectiveness gains.

Gilbert Associates Inc. recently adopted MindBridge Ai Auditor™, which utilizes a hybrid of tests including machine learning based algorithms, rules-based tests, and statistical models, against each transaction in the complete financial dataset. Through the analysis of all the records, the results are presented to the user in an intuitive, visual interface which augments the capabilities of audit and investigative professionals by allowing them to focus their analysis on the most relevant activities. Our VP of Growth, John Colthart had a conversation with David Ljung, CPA, President and CEO, Gilbert Associates Inc. and Kevin Wong, CPA, Director of Audit Practice about how integrating AI technology into their audit practice is creating value for their clients.

Here’s what they had to say!

John Colthart: Tell us about Gilbert Associates Inc.

David: Our clients are a mix of closely-held companies, nonprofit organizations, and governmental agencies. Our emphasis is on delivering high-touch customer service and adding significant value to their organization.

John Colthart: What do you see as your biggest opportunity?

David: Our biggest opportunity lies in our ability to capitalize on technology to improve the efficiency of our work and produce greater insight into our clients’ organizations to increase both their effectiveness and profitability.

John Colthart: What do you see as the biggest threat or challenge?

David: Being unaware of or ineffectively utilizing current technology to improve our service to clients.

John Colthart: How do you plan to address it?

David: We plan to approach technological advances openly, and challenge ourselves to push the envelope to identify those which can effectively improve and expand services to clients while maintaining an appropriate balance with our costs of delivering those services.

John Colthart: What made you choose MindBridge Ai Auditor™? What are the features that you plan to use?

Kevin: MindBridge Ai Auditor™ gives us a scalable opportunity to tap into Artificial Intelligence(AI) technology without the up-front software and data science development costs. As the first product of this kind in the market, we hope to leverage Ai Auditor™ to make our audits more efficient, reduce audit risk, and as a differentiator in the industries we practice in. Beyond the risk analysis of 100% of general ledger transactions, we are in the exploratory stages of determining how we can most effectively incorporate this technology into our processes, provide deeper value-added insight into our clients’ operations, and open new service lines.