2018 - MindBridge

Our year in stories: 2018

April 11, 2024December 28, 2018 by Roy Sarkar

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Why we signed the Montreal Declaration for a Responsible Development of Artificial Intelligence

December 18, 2020December 24, 2018 by Roy Sarkar

With artificial intelligence (AI) influencing every aspect of our lives, and its continued growth in research and commercialization opportunities, the question isn’t whether we should develop it responsibly, it’s a question of how.Last year, over 400 participants came together at the Forum on the Socially Responsible Development of Artificial Intelligence to discuss themes of cybersecurity, legal liability, moral psychology, jobs, and other areas to begin the conversation around the impact of artificial intelligence systems (AIS) on humans. Given that it’s now possible to create autonomous systems capable of performing tasks that were once the sole domain of human intelligence and have strong influence on data-driven decisions, it’s imperative to consider the potential effects of AI on ethical and social concerns. How will AI impact security and privacy? What is the impact on social equality and cultural diversity? Will AI disrupt careers and upend the job market?

These are tough questions and the result of the 2017 forum was a draft declaration setting out a framework of ethical guidelines for the development of AI. After a months-long consultation process with the public, experts, and government decision makers, the final Montreal Declaration for a Responsible Development of Artificial Intelligence was signed on December 4, 2018 at the Society for Arts and Technology.

As of today, we are the first private sector signatory to the Declaration, reinforcing our commitment towards an ethical framework for AIS technology development. The Declaration has three main objectives:

Develop an ethical framework for the development and deployment of AI
Guide the digital transition so everyone beneﬁts from this technological revolution
Open a national and international forum for discussion to collectively achieve equitable, inclusive, and ecologically sustainable AI development

How the Montreal Declaration applies to us

As MindBridge is building an AI platform to help people analyze and understand vast amounts of their data in ways never thought of before, it’s critical to follow a development philosophy that keeps our users at the center of the loop. Because we’re building it for you.

We firmly believe that AI is not meant to replace humans, rather its greatest benefit is to empower people to make better decisions for themselves and society without imposing constraints based on any specific beliefs. As the Declaration’s “Respect for autonomy” principle guides:

AIS must be developed and used while respecting people’s autonomy, and with the goal of increasing people’s control over their lives and their surroundings.

Another principle is democratic participation, where “AIS processes that make decisions affecting a person’s life, quality of life, or reputation must be intelligible to their creators.” Our human-centric approach to the MindBridge platform embodies this philosophy within every aspect of the system. Our CTO, Robin Grosset, explains the details and provides concrete examples in his recent blog.

We embraced these and other principles long before the Declaration was signed, so it required little thought to join and become the first private company to get on board. Now that it’s official, we look forward to working with industry, government, and other parties to ensure a responsibly-developed AI future for all of us.

Our approach to human-centric artificial intelligence

December 18, 2020December 11, 2018 by Robin Grosset

Where is the AI?

Artificial intelligence (AI) is all around us, it powers the helpful voice on my phone and it’s in the digital assistant on my kitchen counter. Actually, I have to admit liking to say “Alexa turn on Christmas” to turn my Christmas lights on and off. It’s just a simple end-point computer, like a terminal, communicating with a cloud-based service which does all the hard work of interpreting what I say and figuring out what to do.

Many AI systems are not as obvious as Alexa, they surround us, yet we don’t see them. Take the ads on my Facebook feed, for example, an algorithm is figuring out what it knows about me and then what ads will likely work best. Even with Google, what appears to be just a search box is much smarter. If you ask the question “What is the population of Canada,” Google is not just searching documents using its famous PageRank algorithm, it’s doing much more. It’s figuring out that an infographic is the best way to communicate the population of Canada to me and showing this alongside its other insights. It also knows flight numbers and does different things depending on context.

What we think is a simple search is much more. AI is sometimes quite subtle and helping us in ways we may not realize.

Good experience design often makes our little AI helpers invisible to us. Two of the ten Dieter Rams principles of good design are, “Good design is unobtrusive” and “Good design is as little design as possible.” We can see why subtle or invisible AI happens; it is considered good design.

Does MindBridge hide its AI?

We have a philosophy that when our AI provides insight or direction to users, we give them the feedback they need to both see it and understand it. We believe in human-centric AI, which means the human is the central part of the system and they should be able to understand what the AI is telling them and have explanations at each stage. The AI needs to communicate and therefore, being visible is an essential element in the trust relationship we are endeavoring to create.

Having said that, sometimes we can’t help ourselves and occasionally we make the experience seamless and require users to click on little information tabs to find out more. This is a design principle called ‘progressive disclosure’ and allows a user to select the level of detail they want.

So where is our AI? How do you know it’s there and working? Let’s take three examples from our AI Auditor product and walk through the techniques and the design considerations.

#1 Unobtrusive but verifiable

Auditors often have to classify items in audit tools manually. They may need to say what kind of money is held in a certain type of account, whether it’s a cash asset, a liability, or maybe a non-capital expense. This process of instructing a software tool in what something means is laborious and repetitive. I think it’s fair to say nobody wants to do it but it’s required to get an accurate view of the finances. This is a great candidate for automation with AI.

MindBridge has a built-in account classifier that uses the human-readable label on financial accounts to determine what kind of account it actually is. This is a form of language processing and we use two methods, the first is a simple search which works well for well-labelled accounts, the second is a Neural Network Classifier which learns how people classify accounts. The net effect (excuse the pun ☺) is that most users of MindBridge spend little to no time telling our system what an account is. It just knows. We do recommend, however, that users review its findings to confirm or correct them. Our AI also learns from these interactions.

This is what it looks like as its working: It appears to be loading data, pretty unobtrusive and just doing its thing.This is what it looks like when the user verifies the outcome. The user has the option to change the classification of the account. This is the only real clue that something smart has just happened.You could be forgiven for not noticing that a lot of work is happening but there are some real time savings here. Below are some charts of simple text search methods vs. a hybrid of text search and AI together. On simple and well-labelled accounting structures, the accuracy of a text search is indistinguishable from an AI. But as we get a little more complex, we see big wins. Further, as the complexity grows to involve a massive organization’s accounts, you see that the simple text search accuracy breaks down and doesn’t cope at all. Conversely, the AI method keeps on punching through the problem and gets it done. The time savings at the complex level is huge; we are talking hours, if not days, of human time saved in laborious activities.

#2 Search that tells you what it understands and gives you options

The MindBridge search interface is a little different than what you’re used to, as we want everything to be understandable and explicable even at the level of a search box. Have you ever typed a search into Google and not got the results you wanted? Chances are you ended up not scrolling to page 2, typed in a slightly different question, and got what you wanted by trial and error.

At MindBridge, we value the AI being visible and explaining itself so that our users can figure out what part of the question is driving the view of data. Here we see a search user interface where the user types their query. There is no AI yet.The user hits go! The AI system parses the language and uses natural language processing (NLP) techniques to unpack what is being requested. Our NLP AI understands language in general but also common accounting terminology. It highlights the important terms in the query and filters the transaction list accordingly.Note that the highlights are clickable so that a user can determine other possible paths and verify that the AI has understood the question. It also understands complex semantics like conjunctions, which are combinations of terms such as AND, OR, or NOT logical expressions. This allows more complex questions to be posed and answered.

In this way, MindBridge users can not only search vast amounts of transaction data for specific scenarios, they can do this without writing an SQL query or using similar technical languages. The AI is effectively reading back their query to them to help in the understanding of what’s driving the results and showing other possibilities. This user interface is very artful as it provides both progressive disclosure and explainable AI, all in a search box.

For transparency, MindBridge has filed a patent for methods used in this search interface. We believe in ‘AI for Good’ and human-centric AI and we use patent protection to ensure the freedom to do the work we do.

#3 Ensemble AI

Ensemble AI is the main event at MindBridge and it guides much of our work. We consider its primary role to be a focusing function for people and, as we specialize in finding insights and irregularities in financial data, it allows us to do this in a robust and explainable way.

So how does Ensemble AI work?

First, we need to understand that the ensemble is not just one method or algorithm but many. It’s like having a panel of experts with different types of knowledge and asking each of them what they think about a given transaction or element of data. The system then combines all the insights from the individual algorithms together.

For example, AI Auditor includes standard audit checks, so some of these “experts” are following simple audit rules while others follow advanced AI techniques and algorithms. The point of the ensemble model is that they all work together like an orchestra and, as the user is the conductor of the orchestra, they can select what’s important to them and the combination of results from the ensemble is presented in an easy to follow way.

Here’s an example of one of the detailed views of the ensemble at work (click to enlarge). You see all the little rectangles which have the larger red or green highlights, these are the individual AI capabilities in the ensemble.Let’s dig deeper into two of these capabilities.

Expert score

One example of an AI method we use is an ‘Expert System.’ This is a classical AI method that draws on the knowledge of real-world accounting practice to identify unusual transactions.

How do we capture real-world knowledge? We work closely with audit professionals and quiz them with surveys and specific questions about risky transactions, allowing us to construct an expert system that knows hundreds of account interactions and their associated concerns. We can run this method very quickly on large amounts of data, allowing us to scale human knowledge and highlight issues that a human user looking at a small sample could easily miss.

Rare flows

Ensemble AI can also identify unusual things using empirical methods. This leverages the science of what is usual or unusual, such as another method we use called ‘Rare flows.’ This part of Ensemble AI is a method of unsupervised learning from a family of algorithms known as outlier detection. The nice thing about unsupervised learning algorithms is they bring no bias, they simply identify what’s in the data and thus let the data speak for itself.

The purpose of this method is to uncover unusual financial activity. We apply this method to all financial activity but the specific PCAOB guidance on material misstatements says:

“The auditor also should look to the requirements in paragraphs .66–.67A of AU sec. 316, Consideration of Fraud in a Financial Statement Audit, for … significant unusual transactions.”

This algorithm finds unusual activity and highlights them and we also perform this type of analysis with several different ensemble techniques. One of the nice things about the ensemble is that you’re not relying on one method, and these techniques can look at account interactions, dollar value amounts, and other outlier metrics to bring them all together.

Why human-centric AI is needed in auditing

Most audit standards today, including the international standards, were the result of years of experience in previous cases of accounting irregularities. As such, they are great at identifying the problems of the past. The limitation is that the typical rules-based system approach to finding irregularities can never identify a circumstance that is not anticipated, and this is why we should apply AI methods like those described above.

A future-looking audit practice needs to adapt to new circumstances. Every industry is changing as the result of AI adoption and the idea that we can uncover new and unusual activity, and explain why it is being flagged, is a key strength of AI systems used by forward -looking audit professionals.

This is why we need AI in auditing. In the words of John Bednarek, Executive Director of Sales Operations, Marketing & Strategic Business Development at MindBridge, “Auditors using AI will replace auditors who don’t”. The simple reason for this is auditors who leverage AI will be faster and more complete in their work, providing a better service to their clients.

Ethical AI goes beyond legal AI

December 18, 2020November 22, 2018 by Bill Brierley

The recent case of the Statistics Canada project to use personal financial data from banks to study the spending habits of Canadians provides a very clear lesson in the ethics of AI. In this case, Statistics Canada has clear legal authority to request and use this data and it’s very likely that the proposed project conforms with ethical standards for AI and analytics. There is also an excellent case that this project will provide significant public benefit. However, it’s also clear that the project failed to gain a moral license from Canadians and by failing in this regard, they have put the project and perhaps their freedom to operate at risk.

Shining a light on the project

At this point, the details about the project are difficult to come by and I have not seen evidence of any public consultation or public notice of the project. This project came to light through a news story published by Global News on Oct 26, 2018. Based on the news reports and a bias towards the general good intentions of government bureaucracy, we can infer that Statistics Canada finds its current survey-based approach to collecting data on Canadian spending habits deeply inadequate. I also expect that the bureaucrats involved saw the opportunity to provide a more accurate picture of Canadian spending habits, more efficiently, and with less burden on the members of the Canadian public. After consulting with Justice, they also determined that they have the legal authority to do so and they honestly believe that Canadians by and large trust Statistics Canada with their personal data. So they made the decision to use the legislation governing Statistics Canada and request data from the banks. I also expect that bureaucrats knew that this request could be misunderstood by the public so they decided to act out of the public eye, trusting that the banks would comply without fuss. Of course, this project will benefit the banks greatly.

What possibly could go wrong?

Application to analytics and AI

I want to stress that there was no malice in the bureaucratic intentions behind this project. To the contrary, I see the motivations as things we want to encourage: innovation, efficiency, improved quality, and Canadian competitiveness. Where things may have went wrong is a long-standing bureaucratic culture of secrecy. The causes and solutions to this problem with bureaucratic culture is a topic for another day.

No doubt there will be calls for changes to the Statistics Act but I think cries for wholesale changes are misguided. Overall, the Act provides a good example of a legal framework for analytics. I’m not saying that events such as this should be ignored, rather the justice department should be tasked with reviewing the act and regulations with the goal of improving the legislation — perhaps by making public consultation mandatory when Statistics Canada wants to collect personal data indirectly.

Legislative frameworks for analytics and AI must do a few things well:

They must protect privacy
They must ensure that the collection and use of personal data contributes to the general social welfare broadly defined
They must protect the ability to innovate

On this last point, legislative frameworks must be flexible and protect against egregious misuse while relying on social and market mechanisms to align activity with public expectations. Authority granted by legislation must protect against the right to innovate being blocked by a radical few. By these tests, the Statistics Act stands up well.

Having legal authority to do something is not the same as acting morally or ethically. In general, ethical use of personal data requires that the data subjects explicitly consent to the collection and use of their data. One can assume that the data subjects have given a license to the analytics organization to use their data for the intended purposes but, in practice, this is complicated and there are exceptions to this approach. One such exception is that the use serves the public good. From what I understand of the proposed use of data by Statistics Canada, this test is clearly met.

How we can do better

So what went wrong? The personal data in the possession of the banks was created as part of delivering banking services. The public expectation, perhaps naively, is that that is the only use they have consented to. The attempt by a third party to access and use this data to develop profiles of consumer spending habits goes well beyond their expectations. In this case, the legal authority to do this is irrelevant and disturbing. At the very least, a public education campaign describing why this is important to Canada and Canadians and how each individual will be protected in the process would have gone a long way to easing the public’s concern.

More fulsome consultation and offering individuals with the ability to opt out would likely have eliminated all barriers and created a positive opinion of the project. Each time an organization tries to fly under the radar when accessing large quantities of personal data, they create a risk of public backlash that will saddle the industry with stifling regulation.

The AI industry needs the right to ethically innovate and to do this, we need a regulatory environment that gives latitude to innovate. This requires the public to be confident that industry members will act ethically within the bounds of the legislation. Each time the AI industry goes against these expectations, the right to innovate is put at risk.

How accountancy can thrive in the age of AI

December 18, 2020October 31, 2018 by Roy Sarkar

The world is changing at a faster pace than ever, leading chief economist at the Bank of England, Andy Haldane, to state that the disruption caused by the ongoing fourth industrial revolution would be “on a much greater scale” than that experienced during the Victorian industrial revolution. Technology is evolving and infiltrating different industries each day and the era of artificial intelligence (AI) is very much upon us. But do employees risk becoming “technically unemployed” with this rise of technology? Or instead, could accountancy thrive thanks to the rise of AI?

Change is in the air

The adoption of new regulations around mandatory audit firm rotation has stimulated competition in the market and caused real drive for the accountancy industry. The most progressive firms have identified AI capabilities as an important differentiator, but still appreciate that the best practice is a collaborative approach, one that augments human and artificial intelligence.

In the same way that the human brain cannot compute hundreds of thousands of data points in a split second, a machine cannot always understand the and context of real-world accounting. In combination, an accountant fueled by AI is turbo-charged to make faster, more accurate decisions, while having more time to focus on providing guidance, value, and insights.

Enhancing the practice

Although proactive firms are deploying AI to help drive efficiency, reduce risk, and increase quality in their compliance processes, there still remains caution in some parts of the market. Implementing AI to augment and support the practitioners in the accountancy world has shown how this technology can benefit the industry, so why is there still hesitancy? It’s a caution that’s driven by myth, misunderstanding, and misconception regarding the perceived black-box nature of artificial intelligence. Each is an unnecessary barrier to the progress all companies need to make if they’re to compete in the modern marketplace.

Often the adoption of AI tools remains hamstrung by the idea that they cannot integrate with existing technology and are complex to use, and this comes down to a misunderstanding of what’s available. The most effective solutions are affordable and designed to work easily alongside people. They’re designed to demystify AI and make them intuitive to use. Moreover, as regulators take an increasingly tough stance on audit failures, AI solutions are a long-term investment that can reduce risk, increase efficiencies, and improve the quality of financial analysis.

Collaboration, not isolation

In the age of AI, each company must become a technology company in order to defend and grow their market, including the financial industry. It is no longer a question of if the role will change, but how can accountants equip themselves with the necessary skills to thrive in the changing world. It’s time to forge forward and recognize that accountancy actually benefits from the rise of artificial intelligence, unearthing more of the risk in financial data, and providing greater assurances than ever before.

AI is not something for accountancy to fear; it’s something for the industry to embrace in order to enhance auditing practice, increasing accuracy and efficiency.

Click here to find out more about the world’s first and only AI-powered auditor platform.

Answering questions about Ai Auditor

December 18, 2020October 22, 2018 by Roy Sarkar

As practical applications of artificial intelligence (AI) are new to the finance space, especially with regards to audit, it’s no surprise that the same questions come up across our expert-led webinars. To help you understand how AI is applied to audit, we’ve collected the most common questions and answers here, as provided by our V.P. Growth, John Colthart.

Q: What programming skills or training are needed to use Ai Auditor?

Our goal is to minimize training to make the platform easy to use – a different philosophy from some of the old audit tools you may have used in the past. We designed Ai Auditor to be as user friendly as possible to help you get to maximum value as quick as possible, which means you need no programming or scripting skills to get things done. It’s all drag and drop actions, mapping your data, running the analysis, and viewing results in as easy a manner as possible.

Of course, we do recommend and include training on using the platform itself. Typically, that’s a kickoff with our customer success team to show you around the platform and help you load in that first data set. We give you a few days to play around with the data and reports, then set up a more focused discussion to help you get the most out of the results, such as understanding what control points do and what the machine learning algorithms are hunting for.

Q: Will Ai Auditor replace our existing audit tools or is it in addition to what we use?

The honest answer is that it depends on what you want to accomplish. If you’re just using a working paper solution to gather data to do quick assessments of a trial balance, our platform would absolutely be an addition to what you’re already using. You would use it to go even deeper into the analysis of the data and bring all our reports back into your working papers to have a much higher level of confidence. On the other hand, if you’re using a data analytics tool, especially a visual tool that doesn’t have machine learning built into it, Ai Auditor could potentially be a more effective and easier to use replacement.

We never say it’s one way or the other because every firm we work with has a different view of how technology supports their people and engagements and how they look at things from a line of business perspective, for example M&A, or all the way through to assurance audit and taxation.

At the end of the day, it really depends on the use case but one thing is certain, Ai Auditor is a tool used to help people be more effective at understanding data and gathering evidence, in the capacity that best suits their needs.

Q: Where does all the data that’s being analyzed come from?

We provide a drag and drop interface to load your data and integrate with the most common ERP systems used today, things like CCH Engagement, QuickBooks, Thomson Reuters AdvanceFlow, NetSuite, Sage Intacct, and more, to pull the various types of data we need. For something like accounts payable, for example, we use information from the ledger itself, including the payables register at the end of the period so we can see what’s outstanding and things such as the vendor name and the user hierarchy.

We also eliminate the need to spend time or IT resources on data extraction, manipulation, and ingestion – we take care of all the data heavy lifting so you can focus on the analysis and results.

Q: Does Ai Auditor help with audit planning?

This one is critical to understand: Our platform isn’t just for performing year-end audits, rather it plays an important role throughout the year, including planning. Our interim analysis is always available, going back to whatever period is available from the data, to help you see and understand how the business is transitioning at various points in time.

We support planning in different ways, such as looking at the data to identify and prioritize where you should be spending more time. It could be potential risk in inventories or accounts payable, or really anything that could influence your thinking around how the business is performing. Additionally, we also give you all those control points to show exactly what’s going on in the business and we can help you derive insights from the available data.

We want you to see and drill down into where the risks are at any point in the year, all for the same price as doing a single engagement at the end of the year.

Q: How and where is your data store?

MindBridge Ai cloud services are hosted on a secure cloud infrastructure, with our primary and backup providers fully ISO 27001 and SSAE 16 compliant. Our software stack is designed for defence in depth, deploying redundant controls in the infrastructure, network, platform, and application to ensure no single point of failure.

Q: How secure is the data?

Customer data is always protected, using NIST-approved algorithms (AES 256) and the most secure protocols and implementations available. All network connections are encrypted and all data stores, including primary and backup, are encrypted at all times.

Q: How do you control who has access to what data?

MindBridge Ai has zero access to your client’s data. We maintain SOC 2 compliance and we build in very high security around who can see and perform operations on various types of data, with different levels of hierarchical security. Each Ai Auditor customer has their own dedicated database and storage and there’s no interaction between customers or mixing of data.

At the end of the day, securing your client’s data is paramount and being able to secure that internally – who gets access to what pieces – is also of paramount importance for us.

Q: How do we use the results we get from Ai Auditor and include them as part of our overall processes?

Every report is available in downloadable format, whether it’s images from a screen or some form of data tables. For example, our data can be exported to a Microsoft Excel file and attached as a supporting document to your audit report. In fact, we highly recommend taking all the data we provide and showing them to your client, where it won’t cross independence lines, so they see you as the expert, trusted partner you should be along with the evidence to back it up.

You can also produce reports to share with your end clients including income statements, financial trending analysis, financial analysis, and more.

For more information on Ai Auditor or to book a demo, visit mindbridge.ai.

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.