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MindBridge recognized as a top 20 AI firm in Canada

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We’re incredibly excited to announce that MindBridge has been included in the Vector Institute’s inaugural AI20 for 2023. Read more in the Press Release published here. With over 1,200 artificial intelligence-focused firms in Canada, receiving this recognition alongside a select group of leading Canadian AI organizations is a testament to the value we consistently deliver … Read more

AI for enterprise risk management (webinar recap)

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On February 23, 2022, MindBridge’s VP of Strategy and Industry Relations, Danielle Supkis Cheek, CPA, CFE, CVA, hosted a live webinar and Q&A on how to remove barriers to AI for your core ERM framework. Danielle shared some great insights during the webinar (video link shared below), and the attendees were very engaging by participating in polls and asking great questions in the Q&A.

Thank you to everyone that attended the live event, and for anyone that missed it, you can view a recording of the webinar here or keep reading for a recap of some of the most valuable key takeaways.

Introduction

Financial technology transformation is moving rapidly, making it hard for enterprises and their leadership to adapt their Enterprise Risk Management processes. The impact on informed judgment can be detrimental if risks are not appropriately managed. AI solves this challenge by helping financial professionals augment traditional risk management processes and quickly and more accurately identify anomalies and surface insights to mitigate risk. 

AI’s Place In The COSO ERM Framework

Nuggets of information are difficult to process anytime you have extreme amounts of data, ledgers, or sub-ledgers of other operational datasets. And while most of us have some data analytics programs in-house, it is incredibly challenging to build out complex programs that encompass the basis of outlier detection based on your norms or control points. 

That’s where AI can start fitting in.  

AI enables the ability to aggregate extreme amounts of data that would typically otherwise be highly cumbersome to aggregate and use for decision-useful information. Therefore, instead of going through a theoretical exercise, you’re able to use actual concepts and actual risks that are permeating through your data. 

Current Pressures Creates New Risks

The risk environment is constantly changing. With factors such as staffing shortages, new regulations, data volume issues, and budget pressures, organizations must be aware of how these pressures affect their risk profile.

When you have all those different kinds of changes in pressures, your risk profile also changes very rapidly and in ways that you may not necessarily be aware of. Sure, you probably have good guesses, you probably have really good insights and intel that’s coming in, but the speed at which that changes is tremendous. E.g., One of the most concerning pressures that organizations face is to do more with less. This burden pressures organizations to either skip a couple of steps or bypass a process which could ultimately lead to errors.

Detecting Behavior in Data  

Here at MindBridge, a lot of the work we’re doing related to risk stems from the question of ‘what are the risks created within organizations that are related to humans as part of it?’ What’s essential within the data, and what you see through the data, is behavior, the human behavior. Of course, there are external risks to consider; however, there are also things that may not necessarily be seen inside existing data and can only be discovered by looking within your organization’s environment. 

“When a measure becomes a target, it ceases to be a good measure.”

 – Charles Goodhart

This quote basically says that any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes. 

KPIs create accountability for an organization— hit your metrics. The problem is that many organizations either put hyper-focus on a singular metric or a series of metrics that can be manipulated the same way. 

Ensemble AI

Ensemble AI combines three different types of things (machine learning, statistical methods, and traditional rules), weighs them together, and presents them so that you (the human) may determine what doesn’t look ‘right.’ This trigger is designed to give you plenty of clues and indications as to what you should be paying attention to in your books that could potentially become, or already is, an issue. 

This process allows the analysis to identify the relative risk of unusual patterns by combining a human expert understanding of business processes and financial, monetary flows with outlier detection. 

“An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem”

-John Turkey.

Outlier vs. Anomaly

Many people use the terms outlier and anomaly synonymously. Outliers are distant observations from the mean or location of a distribution. However, they don’t necessarily represent abnormal behavior or behavior generated by a different process. On the other hand, anomalies are data patterns generated by different processes.

Control Points / Tests

 MindBridge control points are designed to compare client data against pre-defined areas of risk, providing visualizations and reports to understand levels of risk (risk scores), identify unusual transactions, and drill-down into the details. With Ensemble AI, these control points work together to provide results that couldn’t be achieved by running each capability separately. 

To give you an idea, MindBridge has one control point that looks at the pairing of transactions. And let’s say the pairing of accounts receivable and revenue is one of your ‘norms.’ If you look and see that you have a transaction that pairs cash to revenue, it would be flagged for review as it is not the standard pairing. Machine learning is needed in that iteration to determine “what is normal” in each uploaded file.

The same concept also applies to vendor analysis. For example, let’s say you pay $30,000 a month rent for a particular landlord, and then all of a sudden, you see a $60,000 amount. That transaction will get flagged as an “unusual” amount for you to review. 

This unusual amount may be justified as rent + deposit or a similar situation. However, if said outlier occurred in the last month of the fiscal year, you may have some other factors to consider. For instance, do you have a massive cut-off issue? 

All those different tests are run simultaneously, in real-time, on 100% of transactions. So instead of going down theoretical exercises of risk, you can start looking at actual concepts of risks and use that to shape your judgment related to what kind of risks and what other areas you need to spend some time on.

Use Case

During the webinar, Danielle presented many strong use cases concerning the utilization of AI in the ERM framework. We don’t want to spoil the entire show for you, so we’ll just cover one of the use cases in this recap.

One of the use cases presented was the DOJ’s effective compliance program.

The DOJ’s effective compliance program is the DOJ’s response to what compliance program they expect you have in place to address the risks of violating the Foreign Corrupt Practices Act, which comes with criminal penalties associated with it. 

Let’s face it; no one wants to go to jail, especially for something done without your knowledge. And due to the differences in international business practices, something that may be a standard practice in a foreign country (e.g., bribes) may be illegal at home. 

Suppose a foreign third party makes bribes without your knowledge and the DOJ sees that you have an effective compliance program. In that case, you may have an affirmative defense to not have criminal liability for an FCPA violation. You may likely have some civil liability for it, but you don’t have people going off to jail. 

The DOJ uses three significant components when evaluating the competence of your compliance programs.

1. Is it a well-designed program?

Here they’re determining if there are procedures in place for risk assessment. For example, is there risk-based training, are there appropriate controls and processes for third-party management, and what is your due diligence process in mergers and acquisitions?

2. Is the program being applied earnestly and in good faith? In other words, is the program adequately resourced and empowered to function effectively?

The DOJ is very interested in what kind of resources you provide to this program and how much funding is being allocated. Unfortunately, the attachment of funding as a factor poses a problem for some organizations because not all money is spent efficiently. So many people have spent a lot of money to build a program that ends up being too narrow scope when a more holistic concept is needed. 

3. Does the corporation’s compliance program work in practice?

For more holistic concepts of risk, the DOJ wants to see the internal audit, control, testing, and the iteration and constant evolution of the programs; but most importantly, does it work? This is very similar to enterprise risk management, where you’re constantly reassessing and fine-tuning and becoming more precise. This process can be challenging if you don’t have an inflow of real data that can be processed in real-time. 

Technology Ethics

In a new technology benchmarking report, the Association of Certified Fraud Examiners said, “The use of AI and Machine Learning in anti-fraud programs is expected to more than DOUBLE over the next two years.” This is scary because some people don’t know how to supervise AI properly. There are a lot of tools out there that will let you custom configure your own AI. The problem is that you don’t know if it’s actually free from bias or if you’re supervising it appropriately. 

IESBA Technology Ethics Project

“The use of technology is a specific circumstance that might create threats to compliance with the fundamental principles. Considerations that are relevant when identifying such threats when a professional accountant relies upon the output from technology include:
  • Whether information about how the technology functions is available to the accountant.
  • Whether the technology is appropriate for the purpose for which it is to be used.
  • Whether the accountant has the professional competence to understand, use and explain the output from the technology.
  • Whether the technology incorporates expertise or judgments of the accountant or the employing organization.
  • Whether the technology was designed or developed by the accountant or employing organization and therefore might create a self-interest or self-review threat.”

Source: https://www.ifac.org/system/files/publications/files/Proposed-Technology-related-Revisions-to-the-Code.pdf

The standards mentioned above are part of the new technology ethics project out of the International ethics group for CPAs. You may think this standard is related to public accountants or your auditors. But actually, this is the proposed standard for CPAs worldwide that are internal to an organization. That means your controllers, your CFOs, your internal audit team, and any CPAs you may have in your organization.

So, it is crucial to be cautious if your organization decides to take the “build your own” or “use a wizard” machine learning route where some people may not necessarily know exactly how the program works. This lack of transparency can create a risk for your organization and the individuals within your organization that carry a CPA license.

Click here to view a complete recording of the webinar.

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How accounting firms are driving growth with AI

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Commoditization has been a hot topic in the audit industry for some time now. We’ve heard from many prominent voices that commoditization is real – the invisible and somewhat mean-hand of the market driving prices down for audit as the only differentiator is price. But at the same time, we have been seeing average audit fees rise faster than inflation. For example, for a basket of US-listed entities, average audit fees increased from $6m in 2002 to over $15m by 2020, significantly outpacing inflation over the same period. 

We’ve also seen significant evidence that price is not the only factor when choosing an audit firm. Of course, the expertise of the engagement team comes out number one, but increasingly the technological capabilities that firms can bring to bear are playing a key role.

Generally, it seems that firms that are responding faster to society’s rising expectations for efficient audits are reaping the benefits. Being able to win larger, more important, and more profitable audit clients is a key strategic advantage for these firms. However, at the larger end of the market, firms that are now unable to talk to their next-generation or data-driven audit are left with price as a remaining primary lever they must use to differentiate.

Of course, this is not always the case. A significant portion of buyers in the audit is still looking purely for the lowest price. The additional trust and credibility that auditors bring to financial statements today does not seem to carry much weight for this lowest price-focused buyer. For many such stakeholders, it is not easy to differentiate between a high and low-quality audit product. It is up to the audit firm to demonstrate that differentiation, and if undercutting is still a key strategy for an audit firm, then there is still a way to use technology to display how the level of assurance is not compromised despite the low price.

Thinking about the value proposition for a data-driven audit throughout the customer lifecycle is key to demonstrating this value.

Clear outcomes

Before speaking to value, having a clear set of outcomes for a data-driven audit approach is essential. Understanding how the service you are providing will change helps you effectively sell the value of this approach internally and externally. Defining these outcomes and understanding the differentiated value proposition that your firm offers is key.

1. Market facing

Even before you start talking to a prospective client, the firm must be communicating its outcomes in implementing a data-driven approach. Whether this is a lessened focus and effort on low risk-areas, more informed conversations with clients, or direct value-add, it’s critical to emphasize these factors to your market. Creating case studies, dedicating a section of your website for innovation, providing examples in newsletters, and aligning to accounting technology standards such as ISA315, SAS142, and SAS145 are great ways to raise awareness.

2. Proposals and tendering

Allowing your innovation and data team to have input into the proposal process is a step above, both in the form of a dedicated section in the proposal template as well as a process that allows the engagement team to demo the analytics capabilities during meetings. This demonstration could be done with demo-designed data or real data, depending on the importance of the proposal. It also offers technical and data-savvy staff an opportunity to get involved in this discussion with the client.

3. Planning and fieldwork

This audit stage centers around evidence gathering and learning from the auditor. Using data to deepen the engagement team’s understanding of the client can help with a far more productive conversation earlier in the audit process, but it is key that you’re demonstrating how you came to the conclusions you came to. Using visualizations during these conversations is a fantastic way to achieve this, and even better if you can navigate an analytics tool on the fly to adapt to where the conversation is going during the planning meeting.

4. Completion

Ultimately, it’s at this stage that the client sees most of the outputs for the audit process. As a result, we’ve found that including descriptive analytics is a fantastic way to add context to audit findings and cement a perception of value with your client. 

Where to go from here? The pace of change in audit is accelerating, and there is a growing number of technologies that auditors can leverage in various ways. These are opening up strategic opportunities for firms to differentiate – but to do so means that they must be willing to take a different approach from their peers. So whether it is changing where the team is focusing on the audit, how they communicate with their client, or adding net new insights to the post-audit reporting, implementing technology is becoming mandatory as a differentiator and a means to deliver a more efficient audit.


For more information on how top accounting firms are driving growth with AI,
register now for our upcoming webinar with Cherry Bekaert.

This webinar will cover how Cherry Bekaert’s success with leveraging advanced data analytics for risk discovery has continued to offer more significant insights and more efficient audits.

Webinar: Improving audit efficiency by reducing sampling: The value of data-driven assurance

How AI is changing expectations for auditors

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CFO using the MindBridge API for auditing automation

There are some ways that AI is becoming obvious in our daily lives, be it in the driverless technology found in cars or in the tailored content selected for you by streaming services. Many of us have received a reassuring text message from our banks, verifying that the recent payment was you and not some fraudster. You can thank the watchful eye of anomaly detection algorithms that have been keeping our money and accounts safe.  

 Businesses are similarly coming to rely on machine learning to inform critical decision-making. Increasingly, machine learning is finding its place throughout organizations, from customer retention to marketing and finance. Assurance and audit are no different. As the value of these technologies becomes clear and society expects more, pressure builds on auditors to improve. 

 

Reasonable to ask for more assurance 

 The standards have required auditors to deliver a ‘reasonable’ level assurance, a level that is not absolute but rather a high level determined, really, by a shared sense of best practices. Over the last few years, we have seen auditors adapt to the way they are working, and the way they demonstrate their quality. This is largely in response to the market; buyers are becoming more sophisticated. “Audit committees require audit firms to provide extensive evidence to demonstrate their quality. It has become normal to test a firm’s technology, including its data analysis capabilities,” noted PwC in 2018. 

 This is a trend that we are seeing in multiple markets, with a top US firm commenting that “our client’s technology and data availability plays a role in drivers of change. The more clients are using technology, their expectation is elevated on our use of technology.” What constitutes a reasonable level of assurance is changing. 

 Regulators are aware of the positive impact that new technologies can deliver, with the PCAOB foreseeing that “the future of audit will be able to provide a greater level of “reasonable assurance” as auditors may be able to examine 100 per cent of a client’s transactions.” 

 This view is also backed up in a large review of the UK Audit market performed by Sir Donald Brydon. ” As such technologies become widespread in use, stretching beyond journal testing, they will clearly have an impact on the cost of audit (less human checking) and on the depth of testing that will be possible” noted Brydon. 

 Cost savings and the search for efficiencies have often been key drivers of technology adoption in audit for audit partners, but the importance of demonstrating higher levels of audit quality has become clear. The fact that BDO calls out technology as a key aspect in their recent win of SAP as an audit client demonstrates this fact. 

 

AI: An enabler for risk-based auditing 

 Whilst the PCAOB speaks of transaction scoring as a technology of the future, firms are leveraging MindBridge’s 100% risk scoring across the US today. By scanning transactions using a variety of techniques, auditors are both better able to assess risk, and better able to find those risky and unusual transactions. This translates to an audit with less ticking-and-tying, and a greater focus on what matters. It allows fewer audit staff to get through more information and provides greater assurance at the end of it all. 

 An example of an audit algorithm in action is MindBridge’s “outlier detection.” This category of algorithm identifies unusual financial patterns, helping fulfil the requirement of ISA 240, which sets an expectation for auditors to look for unusual activities. An additional benefit of outlier detection is that its methodology consists of unsupervised machine learning, meaning algorithms are not trained or taught on specific data. 

 This overcomes bias in data analysis, with reviewed transactions (i.e., the general ledger of companies), identifying what is normal for the audited entity and separating out what is empirically unusual activity.  

 The unsupervised methods of outlier detection allow for data to be analyzed and anomalies drawn out without requiring training on similar entities. It can also be applied to all types of organizations, irrespective of their size or industry.  

 While outlier detection is effective for detecting new activity and outliers in data, it does not have a prior or pre-existing understanding of accounting processes. It is our belief that there is still a role for the expert system in the context of risk scoring for audit. MindBridge’s “Expert Score” is an example, it’s an indicator that flags transactions based on a database of pre-existing rules determined to be unusual. Write-offs directly between cash and expense will consistently get flagged by Expert Score. 

 Expert Score has recently been enhanced by looking at the prevalence of financial flows in the data selected to take part in our curated learning process. Unusual transaction flows are studied and documented before being added to the Expert Score rule base. 

 

Demonstrating quality: key to growth 

 By leveraging these techniques and changing the profile of work, the firms that are most successfully implementing MindBridge are driving success in the market and growth. By speaking to the value throughout the customer lifecycle, these firms are ensuring that the customer sees the value of working with them. 

Expand your expertise, watch this short webinar from MindBridge here and learn how firms are adopting AI to drive growth. 

Will DAS (the Dynamic Audit Solution) change the audit industry?

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A paper boat on paper water, symbolizing whether or not programs like the AICPA Dynamic Audit Solution will hold water.

The audit industry has seen a bit of a shakeup in the past few years. New technologies, regulator crackdowns, big firms acquiring and merging, and a general push for improved processes and a review of age-old standards are all signs of new things on the horizon for our industry. But while there was a lot of talking, we didn’t see much walking. 

But, all that changed, at least for auditors, with an announcement from the AICPA in 2018.

Nearly three years ago, the “Dynamic Audit Solution Initiative” was announced. Projected to release in 2021, the Dynamic Audit Solution, or “DAS,” as many in the industry affectionately call it, is a “multiyear initiative to create a new, innovative process for auditing using technology.”

As the beta release approaches, we wanted to take a look at the Dynamic Audit Solution in more detail. As a pioneer in AI-powered risk assessment, MindBridge is highly invested and interested in any and all innovations in our space. When it comes to DAS, we want to know what it is and what it means for us and our industry.

In this article, we’ll answer those questions and consider what the impact of a Dynamic Audit Solution might be, for better or for worse.

What is DAS (the Dynamic Audit Solution)?

We don’t know a lot about the Dynamic Audit Solution, but what we do know is exciting. The AICPA sees DAS as the next step toward the future of audit and assessment by leveraging technologies never before seen on a large scale. That, obviously, has a lot of people excited.

There aren’t a ton of details on what exactly the AICPA’s DAS will look like. We haven’t seen any product screenshots, and the core functionality hasn’t been mentioned in any major press coverage. 

But, there are a few key aspects of the technology that have been announced, as well as some information on what the team behind the product are considering as they are building it.

AI, automation, data, and AICPA

At its core, the Dynamic Audit Solution will be an AI-powered product. In an interview with AccountingToday, Matt Dodds, CEO of CaseWare, one of the organizations involved in the project, made a point to note that “the solution is driven by data analytics and AI.” The idea here is that artificial intelligence capabilities will allow auditors to process more data more efficiently, allowing them to create higher quality audits in a fraction of the time.

It isn’t quite clear what areas of the audit solution will include artificial intelligence, or how the AICPA auditing standards will regulate and legitimize control points, risk assessments, and other key factors to a quality audit. But, the need for AI to process increasingly complex and large data sets is clearly at the top of the priority list for the AICPA. As are data analytics.

According to the AICPA, the Dynamic Audit Solution will require “audit professionals become conversant in data science, data integration and analytics.” Essentially, artificial intelligence and automation will allow auditors to become experts in the data that they spend so much time analyzing. Once that data has been processed, though, auditors will be able to better understand and communicate the results of an audit to clients. 

As the traditionally manual tasks of an audit are automated, audit professionals will be afforded more time to converse with clients. This will allow auditors to offer clients a true assessment of the audit findings, while also expanding into a more continuous audit through advisory and consulting services, avoiding independence issues wherever possible.

All of that being said, what does the Dynamic Audit Solution mean for auditors themselves, and for the industry largely?

What does the release of DAS mean for the industry?

The Dynamic Audit Solution is going to mean different things to different people. For auditors, it means a potentially new technology to help them create more efficient and quality audits. In theory, that is. As well the automation of certain audit tasks will allow auditors to become data science professionals, consultants, and any range of financial experts to help their clients better understand their data and assist them in their endeavors. 

But, such a large scale release of an AI-powered solution has industry-wide effects as well, which the AICPA have outlined.

Technology is considered to be one of the four “key drivers” of the DAS project, according to the AICPA. The other three are Methodology, Standards, and New Skills. Artificial intelligence is at the heart of the Technology driver, but is also the reason that the three other drivers are mentioned at all. 

As the AICPA introductory document to DAS notes, audit methodologies, standards, and skills will need to be reevaluated and evolved to meet the demands of artificial intelligence. This means that, as an industry, we are potentially looking at a large-scale overhaul of the AICPA auditing standards, regulations, and methodologies that we’ve come to know over the past 100 years. In fact, some of these revisions are already in motion.

While it might be scary to some, this evolution was all but inevitable, hence the push by the AICPA to introduce DAS in the first place. In fact, in many parts of the world, organizations like the AICPA are being pressured to revise regulations and standards to meet the needs of today and tomorrow’s audit professionals. 

While many have feared the advent of new technologies in the face of storied regulations and standards, large organizations like the AICPA are helping to fix that by entering a new age of tech-driven audits and accounting services.

The question is, can it be made to work?

The Dynamic Audit Solution: A new hope?

Everyone seems to have a different opinion on the Dynamic Audit Solution. Whether or not you think it will work depends on your perspective, and what outcomes you want to see from it. But, as the development process continues and feedback is given, ultimately, the Dynamic Audit Solution can be made to work, even if some of our fears come to fruition.

We’ve outlined what the AICPA and their collaborators hope to achieve with DAS, including automation of rote tasks, expansion of service offerings from auditors and firms, and a revision of AICPA auditing standards and methodologies. What these achievements mean for various auditors and firms will surely vary, so it’s hard to say whether or not the DAS will “work” for everyone, so let’s talk about whether or not it can achieve what the AICPA hopes it will.

The AICPA is an important and storied institution in our industry. It has been a stalwart of standards, regulations, and a representative for CPAs everywhere since its founding in 1887. But, that might be exactly the problem. 

Old dog, new tricks?

While the Dynamic Audit Solution is a great sign of evolution in our industry, it’s a little late to the party.

MindBridge, along with many other innovators in the audit and accounting industry, have worked on this for a long time. We know the market, we know the challenges, and we know what it takes to create a robust product that services not only the auditors on the front lines, but the larger firms, businesses, and stakeholders that invest in technology. 

We had a running start, while DAS is still at the starting line. We understand that agility and flexibility are necessary to address user needs, and delight our evolving industry with a tight feedback loop, among other considerations that come with time, practice, and experience.

Companies like MindBridge are ultimately closer to the needs of enterprises and stakeholders in the audit industry. These are the people pushing firms to do more with less, and produce more effective and high quality work with less resources. We understand the struggle in the market in a way that the AICPA and other organizations may not. 

Part of the challenge will be to establish systems of review in order to meet the needs of an ever-evolving industry. The AICPA is a storied organization that may find it challenging to balance procedure with market need.

Even still, it may be even more difficult than that.

As a standard setter in the audit industry, the AICPA may find themselves in an awkward position with regulators and other standards enforcement agencies.

Audit Standards vs. Innovation

Comparatively, standards setters have been historically less agile than innovative and tech-forward firms. Large organizations have enough hurdles to jump over as is, without being the literal standard setter pushing back on these technological developments. 

The AICPA’s involvement with regulators and imposing audit standards poses a unique challenge to the development, release, and review of a Dynamic Audit Solution. As they mention in their own Introductory Document for DAS, the AICPA anticipates an upheaval of standards and regulations that have inhibited the use of AI-powered technologies for audit in the past. 

It will be interesting to see how a standard setter like the AICPA can build a tool and roll out their procedural recommendations at the same time. This brings to light questions around feedback and updates, and whether or not large organizations are flexible enough to meet the needs of our ever-evolving industry in a timely manner.

At the heart of this is the ability for tech firms to move quickly, update and adjust to new risk factors, changes to normal business processes, and therefore stay ahead of the standards curve. 

Can the standard setter balance that need for speed and agility to enhance client satisfaction while also delivering on software changes needed for a dynamic business environment?

DAS will bring us a long way with standards that embrace technology. However, we will want to make sure that the AICPA focuses more on standards agility to help their members impact and delight the outcomes for the entities they audit.

We will have to wait and see what becomes of DAS in light of current or amended standards, but it’s more than valid to suspect that the industry-wide perspective shift may take some time.

DAS, and the future of audit

Ultimately, the AICPA’s investment in AI and data analytics, and the development of the Dynamic Audit Solution as a result, is exactly the type of thing our industry needs. Big players like the AICPA need to step up and embrace technology, and look to the future of audit and accounting more generally.

At MindBridge, innovations like these make us hopeful for the future of our industry, and have convinced us that we, and our peers in the industry, are having a marked impact on the present and future of audit and accounting.

As our Founder, Solon Angel, notes in his own article on the Dynamic Audit Solution:


“The bottom line is that artificial intelligence is being considered by all players, and this is something that I welcome with open arms. No matter how small or large the investment, every hour or dollar spent works to improve our industry. In light of recent fraud cases around the world, there is a clear need for as many initiatives as the Dynamic Audit Solution as possible, using different AI approaches is better than the status quo.”

We couldn’t agree more. We’re looking forward to the release of the Dynamic Audit Solution to make us better and challenge us to continually improve, evolve, and engage with our expanding client base. For more articles on the audit and accounting industry, visit our blog here.

MindBridge is performing tomorrow’s audits, today.

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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?

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