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Operational Risk Management: AI Tools and Best Practices for Finance and Audit

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Learn how to master operational risk management in finance and auditing. Discover AI tools and strategies to identify, mitigate, and monitor risks effectively.

Operational risk poses a significant challenge for organizations, threatening financial stability and reputations alike when internal processes, systems, or external events fail. For all organizations, operational risk management is essential to protect against costly disruptions. Risk identification, assessment, mitigation, and monitoring are the pillars of operational risk management. Many methods are used to conduct these … Read more

AI-driven audit automation: streamlining processes for scalable success 

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Discover how AI-driven audit automation revolutionizes the audit process with enhanced risk detection, continuous monitoring, and real-time compliance for scalable success.

As financial data grows exponentially, traditional audit methods struggle to keep pace with the sheer volume and complexity. In today’s fast-evolving regulatory environment, relying on periodic, manual audits leaves organizations vulnerable to risk. AI-driven audit automation offers a solution by allowing finance professionals and auditors to continuously monitor transactions, detect anomalies, and ensure compliance in … Read more

ISA 315 revised: What it means for risk assessment procedures, and data analytics

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Two characters discuss the benefits of data analytics in light of ISA 315 revisions.

ISA 315 (revised) and Data Analytics: Risk assessment procedures reimagined

The revised standard has been published as of December 2020, and you might be wondering what impact it has on your firm’s risk assessment procedures and how you can address the requirements. There are many useful sources of information on the changes, notably the IAASB’s Introduction to ISA 315. IFAC also published a helpful flowchart for ISA 315 during the work programme, which walks through the various steps required to assess risk of material misstatement.

There are a number of improvements to the standard, including an enhanced focus on controls (particularly IT controls), stronger requirements on exercising professional scepticism and documentation, and considerations around the use of data analytics for risk assessment. The new standard comes into effect from 15th December 2021, so now is the time to start planning how you will address the changes in your audit. Below we discuss some key considerations on how analytics can support a strong risk assessment.

A chart explaining risk assessment and data analytics as part of the ISA 315 revision by IFAC.

Credit: https://www.ifac.org/system/files/publications/files/IAASB-Introduction-to-ISA-315.pdf

So how can data analytics support your risk assessment according to ISA 315? The areas identified above in red show the different procedures that can be supported by the use of these techniques. A key element of the revised standard is that this should be an iterative process conducted throughout the audit. This means using data analytics tools that can be easily refreshed with the latest information will better support this requirement than more traditional approaches.

Identifying risks of material misstatement at the financial statement level

Data analytics can support the risk assessment procedures laid out in ISA 315 by analysing previous and current accounting data to the financial statement level. This allows the auditor to see the material balances in the accounts, and if machine learning is applied, where the concentration of risky transactions lies. This is where the knowledge gained in the blue boxes above can be brought to bear. Comparing understanding gained through observation to the data is a powerful way to sense check and identify areas for further investigation.

Identifying risks of material misstatement at the assertion level

Specific analyses can target assertion risks and show where there are particular problems with an assertion. To do so effectively, several different analytics tests can be applied and combined to develop a good indicator of an assertion risk, for example accuracy. These can then be applied in an automatic way to give the auditor the information needed for their risk assessment.

Determine significant classes of transactions, account balances or disclosures (COTABD)

Combining assertion analytics with the ability to profile similar transactions can help auditors identify significant classes of transactions or balances. Analytics can help to produce similarity scores, but also to identify sets of transactions that are unusual. This can indicate previously unknown business processes that may require a separate assessment of their control environment.

Assess inherent risk by assessing likelihood and magnitude

Following identification of risk, the audit can guide their assessment by understanding the level of unusualness. Data analytics can provide finer grain evaluations of risk rather than simply risky or not. This can help support assessments aligned with the spectrum of inherent risk as defined in the standard.

Assess control risk

Data analytics such as process mining or automated testing of segregation of duties can help to inform or test control risk. These analytics can provide more comfort around the controls risk assessment and help to identify deviations in the control environment that require further examination.

Material but not significant COTABD

Where COTABD has been determined as material but not significant, recurring analytics can ensure that this assessment remains valid. Anomaly detection methods can be particularly helpful here, allowing the auditor to regularly check that nothing unusual has occurred since the initial assessment was undertaken.

Next Steps: ISA 315 and Data Analytics

Audit methodologies will need to reflect the revised workflow, with particular emphasis on the iterative nature of the risk assessment and ensuring that auditors are prompted to exercise professional scepticism and document it at every stage. Data analytics can help to ensure that the information used to continuously conduct risk assessment is timely, appropriate and relevant.

These improvements to the standard will result in a stronger audit approach and an advancement towards industry adaption data and analytics technologies. With AI audit software, accountants and auditors can gain deeper insights into their client’s financial data, in less time. Overall, the audit software can increase the efficiency of their processes, so they can focus on delivering better results, in time for the ISA 315 (revised) December 15th, 2021 deadline. 

Want to learn more about the benefits of AI auditing software? Read our article on “Assessing audit risk during engagements” to learn more. 

Want to learn more about how auditors are using AI?

Watch the on-demand webinar now

Leveraging AI for your substantive procedures for Accounts Receivable and Accounts Payable

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abstract lines up showing ar-ap procedures

Artificial intelligence (AI) and machine learning (ML) technologies can streamline traditional audit procedures for Accounts Receivable (AR) and Accounts Payable (AP) in audits of financial statements.

This blog will consider applications of AI and ML technologies using the MindBridge platform for both substantive analytical procedures as well as detailed testing of specific items.

What does the MindBridge platform do?

MindBridge Ai Auditor, in addition to core general ledger analysis, includes dedicated AR and AP modules that automatically analyze subledger data and, without any scripting, provide high-value visualizations and transaction-level analysis of data.

These capabilities allow you to leverage subledger-level insights and anomalies as critical inputs to your audit procedures and identify risks of material misstatement.

How MindBridge empowers you to perform effective and substantive analytical procedures for AR and AP

Substantive analytical procedures can be a powerful complement to traditional sampling and external confirmations. That is, provided that the auditor is comfortable with the internal controls in place regarding purchasing and sales cycles and has validated the accuracy and completeness of the subledger data.

Trends and patterns

Ai Auditor allows you to visualize how monthly AR and AP balances or net monthly activity track over multiple years at customer vendor levels, and in aggregate. Consistent patterns in these trends in the face of consistent sales and purchasing patterns (respectively) may provide audit evidence that subledger information is not materially misstated.

Vendors and customers related to the entity subject to audit are flagged directly in the summary detail as well.

AP AR screenshot showing summary detail

Key performance indicators

Days Outstanding and Turnover Ratios are calculated at the customer and vendor level and are visualized on a monthly basis, allowing you to identify where there are periods of potential distress or deteriorating quality (e.g. is the volume of cash receipts slowing?). Similar to ending balances and activity, you are also able to compare certain customers or vendors against each other along the lines of these metrics to expose patterns of interest.

Key performance monthly indicator screenshot

Aging

Aging at the customer and vendor level is automatically calculated and captured across respective buckets of days outstanding (0-30 days, 31-60 days, etc.). Consistent breakdown in the relative proportion of these aging buckets across multiple years of subledgers may provide audit evidence that subledger information is not materially misstated at the balance sheet date.

For certain entries that are significantly aged or stale, you’re able to drill-in to all the transactions with a particular customer or vendor and ascertain which invoice(s) are contributing to those totals and whether they could be at risk of bad debt.

Risk of bad debt screenshot

How MindBridge streamlines detailed testing of AR & AP subledger data

Navigating and querying transactional level data via the Data Table in Ai Auditor is a powerful and effective way to explore and validate subledger activity.

Control Points, which are various statistical, rules-based, and machine learning tests, are run against every transaction. The results are summarized on a dashboard that supports interactions like filtering and drill-through.

Dashboard based on control point screenshot

Combining the query building capabilities of the Data Table with Control Point tests, you can efficiently identify relevant populations for sampling and have selections for external confirmation requests or alternative procedures testing (like subsequent receipts, for example) automatically identified on a risk-stratified basis. These selections can then be exported to Excel in one click  to populate confirmation requests and/or to be included in supporting documentation.

The results of the transactional risk analysis may also be of particular interest to large entities and small businesses alike to provide insight into where there may be process improvements or gaps to consider in internal controls.

Take the first step towards AI-driven audit procedures on the AR and AP subledgers

To learn more, contact sales@mindbridge.ai.

Want to learn how AI can empower finance leaders of the future?

Watch the on-demand webinar now

The auditor’s fallacy: The law of small numbers

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big data analytics in auditing

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

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auditors findings

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

  1.      Prepare your client and their data
  2.      Risk assessment and planning
  3.      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

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audit and auditor

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

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ai audit software

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

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internal auditing software

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

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information about auditor

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!