AI for enterprise risk management (webinar recap)

September 21, 2024March 3, 2022 by Coleman Howard

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.

How accounting firms are driving growth with AI

June 7, 2022February 17, 2022 by Stuart Cobbe, ACA

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

January 17, 2023February 3, 2022 by Stuart Cobbe, ACA

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?

September 21, 2024February 5, 2021 by John Colthart

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.

Find out how AI empowers the financial leaders of the future.

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

May 26, 2022January 19, 2021 by Rachel Kirkham

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.

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?

Digital auditing tools and AI in auditing practice: A conversation

May 26, 2022January 13, 2021 by Mike McLean

INTERVIEW WITH

Stephen McIntosh
Tax consultant, auditor, INTARIA AG

In July of 2020, MindBridge took another step in the journey to global expansion. Partnering with Regensburg-based startup, 5FSoftware, MindBridge’s software solution is now being distributed to firms in the German and Austrian markets.

This is a major development that will allow more firms to expose anomalies, intentional or not, during their annual financial statement audits as efficiently as possible.

We sat down with Stephen McIntosh, an auditor and tax consultant for Intaria AG in Munich. Intaria is the first firm in Germany to use MindBridge, and we wanted his perspective on the importance of AI for audits, and what the future of the industry holds.

Stephen sat down with Marco Bogendörfer, co-founder of 5FSoftware.

For more information on MindBridge’s partnership with 5FSoftware, check out our release here, or check out the full interview from 5FSoftware here.

Without further ado, enjoy this excerpt.

This interview has been translated into English from German.

Marco Bogendörfer: Let’s start with a look at the audit profession in general: How far have we come in the digital transformation of the auditing practice in Germany and Austria – and what impact does it currently have on existing processes in an annual audit?

Stephen McIntosh: That’s not easy to answer and depends crucially on which audit firm you look at. The Big Four have invested several billion euros in digital technologies for years now to create their own solutions. Of course, small and medium-sized audit firms do not have this financial strength.

Data such as requirement notification, order, receipt of goods or payment are still too often left unused during the annual audit.

But beyond that, in my opinion, it is a matter of fundamental affinity for digital solutions and a willingness to invest in the auditing practice or its management. If it is the case that firms possess this willingness, the digital transformation can and will continue to advance in medium-sized and smaller auditing firms as well.

Marco Bogendörfer: How can digital tools make audits more efficient and higher quality?

Stephen McIntosh: An increase in efficiency is usually achieved when digital audit tools can take over recurring tasks. When employees no longer have to manually print, envelope, send and evaluate balance confirmations, they can focus more on important issues.

With digital audit tools – the International Standards on Auditing (ISA) refer to them as Automated Tools and Techniques (ATT) – I can seamlessly analyze 100 percent of the business transactions of a fiscal year for specific anomalies. A human being would take far too long to do this. Increasing audit quality by using digital tools such as these is our top priority. We can review certain areas without any gaps, while in other areas digital audit tools enable us to take samples of even better quality, as we can consciously select items with a greater risk of error.

Marco Bogendörfer: Currently, what are the biggest obstacles or challenges for the widespread use of data analytics tools?

Stephen McIntosh: From a technical point of view, the biggest challenge is to get the data first and then to import it quickly and completely into the respective data analysis tool. There are simply so many different ERP or accounting systems that the process of exporting data is never the same and the information contained in each is very different.

Within the auditing practice, the auditing process must be adapted. The analyses must be used from the beginning of the audit planning and then until the end of the audit. Only then can the integration of the software lead to increased efficiency. However, this also requires that the audit teams have IT competence in addition to accounting and auditing knowledge. This in turn means that training is required for the employees concerned.

Marco Bogendörfer: How does MindBridge add value during a final audit?

Stephen McIntosh: In many ways. The first, very significant improvement compared to our previous tool is that MindBridge generates the balance sheet and profit and loss statement from the imported data. We can therefore immediately check the data received from the client for completeness and accuracy.

MindBridge carries out a risk assessment of all transactions in a fiscal year. For each individual transaction, the system is transparent in showing how it arrived at the risk assessment. In particular, the AI-based machine learning algorithms can identify those transactions that are unusual or conspicuous compared to all others.

We can immediately check the data received from the client for completeness and correctness with MindBridge.

Marco Bogendörfer: How can digital tools make audits more efficient and higher quality?

Marco Bogendörfer: Currently, what are the biggest obstacles or challenges for the widespread use of data analytics tools?

Marco Bogendörfer: How does MindBridge add value during a final audit?

We can immediately check the data received from the client for completeness and correctness with MindBridge.

Additional added value is provided by the visualization of financial results and the many possibilities to dive directly into the trends and ratios for further evaluation. These are very helpful for understanding account performance during the course of the year, and for discussing the causes of these developments with clients.

Marco Bogendörfer: How does MindBridge actually work for auditing practice and what kind of data sets can be analyzed with the help of MindBridge?

Stephen McIntosh: MindBridge analyzes all postings of a fiscal year at the general ledger level. For this purpose, we usually have our clients provide us with the “export tax audit”, formerly also called GdPdU data. MindBridge also offers the possibility of carrying out analyses for the subsidiary ledgers of debtors and creditors. We do not currently use these yet, as we are focusing on the introduction and use of the analyses at the general ledger level.

Marco Bogendörfer: How was the use of MindBridge in your office received by employees? Clients?

Stephen McIntosh: All employees who have seen MindBridge or its analyses were impressed by the visual presentations and the possibilities of evaluating and analyzing the existing data in greater depth. There is also great interest in seeing and questioning the risk assessment.

During an audit, I showed my client MindBridge and we looked at the higher risk transactions together. We also questioned why the AI-based algorithms classified these transactions as “high risk”. For all transactions, we were able to understand the “assessment” of the algorithms, even if in the end there was no booking error or even a fraud issue behind it. But first and foremost, it was all about identifying anomalies, so-called outliers, and that worked. My client took a very positive view of the software and also the use of the software during our audit.

Marco Bogendörfer: How can the audit evidence obtained through new technologies be documented appropriately?

Stephen McIntosh: Basically, there are no concrete regulations on how the use of the technologies, and the results and audit evidence obtained must be documented. As a result, it must be possible for a knowledgeable outside third party to understand what was done with which results and on what basis and what conclusions were drawn from them.

MindBridge, for example, provides a standard report that explains the analyses carried out by way of example, as well as graphically depicting the risk classification of all transactions and the risks per balance sheet and P&L item with the respective employees making book entries – and summarizing the quantitative analysis results per analysis (control point). This report can be supplemented with comments via editable text fields, so that the conclusions drawn in each case and/or the further audit procedures can be documented centrally in this report. In my opinion, this report is a good basis for documentation.

Marco Bogendörfer: What skills and mindset should auditors bring to the successful digitization of an annual audit?

Stephen McIntosh: They should be open to current digital developments, recognize the relevance of digital transformation in their own auditing practice and be willing to invest. It is also very helpful if auditors have a certain amount of knowledge about the basic nature and structure of the financial data to be analyzed.

They should be open to current digital developments, recognize the relevance of digital transformation in their own practice and be willing to invest.

We are in the middle of the nationwide implementation of MindBridge and the investments have been kept within reasonable limits. The intensive work on digitization regularly leads to further exciting topics and questions, so there are already other topics that I would like to tackle next.

Want to learn about how to drive efficiency with data-driven audit planning?

From person to machine: The role of audit data analysis

May 26, 2022December 16, 2020 by Stuart Cobbe, ACA

An auditor can view themselves as many different personas, but up until recently ‘audit data analyst’ was not one of those personas. The truth is, I’ve always thought that this was a bit of an unfair position for auditors.

For as long as I have been involved in the accounting and finance industries, auditors have been drawing conclusions about large populations of data by using random sampling or a particular strategic lens. What has always impressed me is how a seasoned partner can spot an error deep in the numbers just by looking at the primary statements.

While strong audit data analysts are still applying their incredible talents, many auditors are beginning to leverage new audit technologies to streamline their analysis methods.

Embracing new data analysis techniques during audits

What’s most interesting today is how professional data analytics techniques from other fields are being combined with traditional audit approaches. This has enabled new ways for auditors to interrogate, understand, and gain assurance during data journal entry analysis or general ledger analysis. This ranges from basic aggregation techniques such as calculating proof in totals and creating moderately complex data visualizations to machine learning techniques designed to spot unusual patterns.

Using AI-powered technology such as Ai Auditor, audit data analysis appears to be entering a new phase of progression. AI audit solutions leverage machine learning to analyze general ledgers and deliver automated risk scores across all transactions and financial data.

How the role of the data analyst is evolving with AI technology

Learning how to properly implement these technologies to evolve auditing processes and general ledger analysis requires consideration. However, I have seen many instances where these cutting-edge audit analysis technologies were able to flag truly interesting items such as the purchase of a Porsche for a company director. When one experiences these types of results with AI audit software, it’s easy to believe that the future is here for journal entry analysis. And, long gone is the day of manual data segmentation in Excel.

Many of these AI audit solutions work by building some expectation of normal within a specific pool of data. The many breakthroughs that are still occurring in data science and artificial intelligence will likely improve the machine’s sense of nuance. As more accurate models involve higher levels of complex analysis, we must, as an industry, weigh this fact against our need for explainable results.

This is not the end for analyzing audit data. Some auditors will always carry the persona of data analysts because they are inherently great at decoding data. However, perhaps that role is evolving alongside new AI audit technology. And perhaps, that’s a good thing.

Want to learn more about how auditors are using AI?

Tools and tips for the audit busy season

September 22, 2022December 8, 2020 by Solon Angel

For most auditors, surviving another audit busy season can be a rough ride. Between the 60-80-hour workweeks and the constant pressure to meet deadlines, there’s little time to rest, gather with family or friends, or enjoy personal hobbies. The reality is that stress is at an all-time high during the audit busy season, and many auditors can reach the brink of burnout.

The COVID-19 pandemic and work-from-home mandates have made things harder for some. Auditors not only have to work extra-long days, but there are fewer chances to break away from the desk and get some much-needed downtime. As the lines between work and home become even more blurred, there’s a serious risk for increased mental health crises.

Auditors are also having to juggle the inherent challenges of remote audits. Everything from trying to figure how to securely access client information and ensuring cybersecurity best practices, to scouring financial data to detect rising cases of fraud put even more pressure on auditors.

Below, we share some tips and best practices that can help auditors prioritize self-care and ease the stresses of the busy audit season.

Top 5 best practices for the audit busy season

1 – Choose the right auditing tools

Conducting effective remote audits begins with selecting the right audit tools. Everything must be considered, from how an audit team will communicate with clients to how files will be shared.

For instance, using a cloud-based AI auditing platform can simplify the sharing of financial data. Clients can quickly upload files into the secure AI platform, allowing the audit team to remotely access and analyze information. With AI power at hand, auditors can also run multiple algorithms across all client transactions simultaneously and cross-correlate data using dozens of testing criteria. This gives them a clearer picture of potential risks.

2 – Prioritize your personal wellbeing during audit busy season

Working from home for long periods of time can wreak havoc on anyone’s mental and physical health. Coupling this with the added stresses of the audit busy season, and auditors become highly susceptible to burnout.

Scheduling short bouts of exercise, yoga, or meditation each day can make a big difference. According to the Anxiety and Depression Association of America, even taking five minutes for light physical movement can reduce stress and stimulate anti-anxiety effects. Auditors who take time to prioritize self-care, get outside for walks, and use meditation apps will be able to better manage the stresses of the busy audit season. Plus, you may even produce better work.

3 – Ease the wake-up-and-work rush of the busy season

Before getting to the at-home workspace, auditors can plan some time for a burst of exercise and home-cooked breakfast or jump in the car to snag a latte at their favorite drive-through coffee shop. These small tasks bring some level of normalcy and variety to what can feel like endless days of remote auditing.

As well, setting firm boundaries around when a workday begins and ends will help auditors delineate work from quality time with family or simple relaxation. Working from home doesn’t have to mean that you’re “always on” or “always available.” This mindset is a one-way ticket to Burnout City.

4 – Re-evaluate auditing best practices

Auditing methodologies and best practices evolve constantly. This is especially true as new technologies become more widely accepted and used in auditing practices. To minimize stress and ensure the highest quality audits and risk assessments, auditors should always take some time to review any updates on audit methodologies and standards. This allows audit teams to better plan for audit engagements and ensures they’re using the most current information to handle their remote audits.

For example, check out our recent blog titled ‘How the new SAS-142 audit evidence standard embraces technology and automation.’

5 – Keep up with developing cyber risks

Working on remote audits while trying to meet looming deadlines is hard enough. But today, it’s become even more imperative for auditors to stay informed about the latest cyber risks and take action to prevent data breaches. The best way to do this is by partnering with transparent and trustworthy technology partners. Auditing firms should vet technology providers by asking about their cybersecurity policies and initiatives, their accreditations and certifications, and any accessible tools that ensure the highest level of resilience to cyber attacks.

Delivering quality work efficiently during the audit busy season

As another busy audit season approaches and remote audits become the new norm, auditors need to rethink how they’re going to manage the current and upcoming stresses and challenges. By implementing the right strategies and tools, auditors can better navigate the audit busy season without reaching a state of complete exhaustion. More than that, they can retain the highest quality of audits and assessments, without compromising data privacy and security.

Wondering how you can streamline your remote audits? Contact our team to schedule a quick demo of our AI auditing platform.

Want to learn how AI can empower finance leaders of the future? Watch the on-demand webinar now.

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

What is AI auditing?

May 26, 2022October 27, 2020 by Shaye Thyer, CA

One red shape separated from other shapes

Have you noticed that no one really calls it a “smartphone” anymore?

It’s just a phone.

The fact that it is “smart” is a given — it’s phone 1-0-1, it’s the least possible useable thingy (aka minimum viable product), it’s the baseline for customer experience.

No longer do phone users want to:

Type words in full in an SMS
Carry a phone AND a camera
Have to sit at a desktop to scroll social media endlessly
Read actual maps
Sit and wait for anything without being able to check emails/Twitter/Facebook/latest news/ browse internet/just generally ignore the world around them….

Harshly, there’s a reason why non-smartphones are now referred to as “dumb phones”.

If I consider it appropriate for my 9-year-old to have a phone of any sort in the near future, it will most certainly be one of these “dumb phones”. In fact, it will be so dumb that she finds it SO boring that she’ll only use it for emergencies (fancy that!) and avoid phone trouble traps of selfies, text neck, and cyberbullying.

What’s this got to do with AI audit?

“AI auditing” is the new “auditing”.

We’re incredibly privileged to have advances in AI technology that are being democratized by companies for real-world applications NOW. As such, we won’t have “AI audit” and “AI auditors” for much longer — we’ll simply have auditors doing auditing where the assumption, the MVP, and the baseline expectation is that they are powered by artificial intelligence, to all the significant benefit of their clients, the profession, and themselves personally.

Why?

AI auditors…

…are more efficient

A well-planned audit is an efficient audit. AI audit can, for example, risk-rate 100% of the transactions in the general ledger and sub-ledgers to produce an aggregated risk profile of the data that makes up the business’ financial statements, facilitating laser-like focus on the areas that matter.

…deliver better audit quality

Audit is an essential source of public confidence in financial reporting and hence trust in business and the wider economy. AI audit enhances quality by allowing auditors greater certainty to relay to clients:

That the financial statements are free from material misstatement
Details of any material deficiencies detected so that they can be addressed

Rather than using risk assessment and data analytics processes to find the needle in the haystack, AI audit sets the haystack on fire to discover more needles with a fraction of the effort.

…add more value

AI-powered analytics within the audit process allow auditors to surface insights perhaps not available to clients from their internal systems. With the capacity created from more efficient planning and execution, AI auditors can feed these valuable insights back to their clients, creating client “stickiness” through real value provision.

…can diversify into new service offerings

Fast emerging in the world of audit is the concept of the “continuous audit” or “continuous risk management” as a service. Imagine a more periodic peace-of-mind, or sense-check or proactive fraud-risk indicator for business owners, CFOs, audit committees, boards, and CEOs alike. Brilliant in theory but generally difficult to deliver to market commercially without some very controlled and prescriptive process or automation. AI is the true enabler of these services to market broadly and commercially, leading to a more regular income stream for firms, tremendous value-add for clients, and more interesting and impactful work for auditors.

…have better margins

Just like all compliance activities in the accounting industry, the annual compliance audit is considered a “grudge purchase” by many clients. They know they need it but don’t really like enduring the process or let alone paying for it. This puts huge downward pressure on fees and creates what is known as “margin-squeeze”.

With a combination of a more human, client-centric process (enabled and amplified by great technology), more value delivered through deep business insights, and the enablement of more valuable periodic services (for example), AI audit helps clients shift towards recognizing the opportunity for continuous improvement and peace-of-mind around quality that the audit process brings. This mindset shift is essential for audit teams to successfully position fees that reflect the value of the service delivered now and into the future, and thus preserve commercial margins for their firms.

AI auditing is here to stay

Just like Apple did with the release of the first iPhone and Xero did with the introduction of the single ledger, both in 2007, today’s AI auditing will reset client expectations for audit across the industry. Supremely efficient, deeply analytical, highly valued, and wonderfully human-centric audit experiences will re-define the audit process and profession and ultimately re-define the notion of reasonable assurance.

Want to learn more about how auditors are using AI?

The Digital Accountancy Forum 2020: Restoring trust in auditors with AI

May 26, 2022October 1, 2020 by MindBridge

The Digital Accountancy Forum and Awards

MindBridge is proud to sponsor this year’s virtual Digital Accountancy Forum. The forum brings together leading accounting firms, industry bodies and regulators, advisors and consultancies, law firms, and tech vendors to discuss and challenge key issues impacting the sector.

On top of providing an opportunity to connect and network all day through the virtual booth, the event will also see MindBridge’s Founder and Chief Impact Officer, Solon Angel, present on how AI can help auditors keep companies out of trouble in a session at 3:00 pm BST.

Packed with valuable takeaways, the session will give real examples of how AI-based data analysis, planning, assertion testing and more can drive better client conversations and give auditors the evidence they need to back them up.

Solon adds: “From Carillion, to Patisserie Valerie, to Wirecard, the audit profession is being blamed for fraud schemes, scandals, and financial collapse. At the same time, the industry is slow to consider radically different ways of performing audit, and has instead focused on automation of the old ways of doing audit. It’s time to enable auditors to do their best, by giving them the knowledge and tools they need to uncover the truth behind an organization’s finances and visualize data in a way that empowers leaders to take action.”

But how can this be put into practice and how can AI really help?

Join Solon as he explains how machine learning works to augment human judgement, providing a clear understanding of how firms, regulators, standards bodies, schools and technology vendors can work together to restore trust in auditors.

At the end of the discussion, you will have heard:

Why AI offers much more than automation
How data science augments an auditor’s experience and judgement
How data analytics enables new ways of thinking and services for clients
Why restoring trust must include everyone, from regulators and firms to schools and technology companies

There will also be the opportunity to hear our Director of Growth Europe, Stuart Cobbe, join industry experts on the closing panel discussion. This session will explore the future of the accountancy profession, touching upon:

If globalisation will have an impact on developing the next generation of accountants
How the industry can ensure the accountancy profession remains attractive to the younger generation
What future technological changes are needed to increase the automation of accountancy

We look forward to seeing you there! Register your attendance here. You can also meet our UK and product teams at our virtual booth!

If you’re looking for tips on how to make the most out of attending a virtual event, take a look at these do’s and don’ts to get you started.

Leading the way with Ai Auditors: Themes from the Digital Accountancy Forum 2020

May 26, 2022October 1, 2020 by MindBridge

Yesterday saw the Digital Accountancy Forum return for the ninth year, but it was the first year our MindBridge team has been involved. The packed agenda, including a session from our Founder and Chief Impact Officer, Solon Angel and a panel discussion involving our Director of Growth Europe, Stuart Cobbe, was full of valuable insight celebrating the best and most innovative developments in modern accounting.

We were delighted to have many engaging conversations with delegates looking to find out more about MindBridge. In particular, our team spoke to numerous accounting professionals about the future of audit, what’s new in Ai Auditor, how AI can assess financial risk in times of crisis and why one of our customers, Moore Kingston Smith, a top 20 UK chartered accountancy and audit firm, is leveraging MindBridge’s Ai Auditor.

Introducing Ai Auditors

Solon’s session, discussing how AI can help auditors to keep companies out of trouble, was quite relevant in this Covid environment. Solon talked about what it takes to be an Ai Auditor, how data science can augment a human auditor’s experience and judgement, why data analytics and AI are slightly different, how they can enable new ways of thinking and why restoring trust must include everyone. It was a presentation packed with insight, takeaways and learnings for accountancy professionals.

Rounding up the session, Solon introduced the concept of Ai Auditors – human auditors that have been augmented with AI – with a great quote from Moore Kingston Smith about how working with MindBridge has enabled them to pick samples and look at different transactions in a more robust way:

“…if someone asks me why we have audited a particular sample, I can explain the computer-based technique which is a lot more robust than saying one of my trainees picked ten transactions…”

The future of accountancy firms

Towards the end of the day, MindBridge’s Director of Growth Europe, Stuart Cobbe took part in a panel session chaired by Jon Lisby, Director, Global Alliance Advisory Services, exploring where the accountancy profession is heading and what future opportunities might look like.

When discussing what the firm of 2025 will look like, Stuart added that accountants have been agile in their response to the pandemic, with a lot of changes underpinned by technology, enabling different ways to create and add value:

“The accounting or audit firm of the future will be more varied with its skill composition and it will be more agile in the way that it plans for its business. It will also be much more responsive to the needs of the market; less checklist-driven and more critical thought-driven.”

The new era of audit

We were thrilled with the volume of engaging and insightful conversations that our team had with delegates at the Digital Accountancy Forum. Commenting on the success of the event, Solon adds:

“The Digital Accountancy Forum was truly a great event, showing what can be achieved virtually! Every delegate we spoke to was keen to learn about Ai Auditors and how AI can really transform the audit process. We’d like to thank everyone who visited our virtual booth and attended our sessions – we’re already looking forward to next year’s event!”

If you didn’t get a chance to chat with one of our team members at the virtual booth but would like to find out more, please email info@mindbridge.ai.

What to expect from audit software in 2021 to 2022

August 29, 2022September 28, 2020 by Kate Hashimoto, CPA

An outlook on audit software trends in 2021-2022

In recent years, demand for accounting and audit software has been on the rise. Mostly, accounting professionals are looking for ways to speed up routine tasks and focus on what matters most—providing clients with valuable business insights and guidance on financial strategy. Already, many firms have seen how AI audit software can help their teams improve risk assessments and build stronger audit plans. That’s because some of the best audit software helps auditors become more efficient at combing through surging amounts of company data.

As we move through 2021 and into 2022, the interest in AI audit software isn’t slowing down. Below, we’ve identified five key trends that we believe will continue to propel the accounting and auditing industry forward in adopting AI audit software.

5 audit software trends to keep an eye on:

1. Increased demands for automating routine tasks

Accountants and auditors are under a lot of pressure to identify risks and turn reports over in the least amount of time.

The challenge is that when auditors use traditional data sampling and analysis methods, billable hours can add up fast. This is why in recent years there has been a push for greater automation in accounting practices using audit software. Some of the best audit software combines machine learning, data analytics, and AI to deliver higher levels of automation on routine tasks.

By automating processes with AI audit software, auditors can reduce the off-chance of human error while also ensuring 100% of the data is thoroughly analyzed for risks. This frees up an auditor’s time to focus other critical tasks such as exploring data trends and studying risks characteristics so they can ultimately deliver greater value to clients. As we move into 2022, these tangible efficiency gains will continue to drive the adoption of AI audit software.

2. Growing adoption of cloud-based solutions

In recent years, cloud-based software has become increasingly popular in the accounting and finance industries. Since these cloud applications are hosted in highly-secure remote datacenters, it’s easier for accountants to access information from home offices. They simply login to an online platform which is protected with built-in cybersecurity features.

If we look at statistics from 2018, about 43% of CPA firms already had employees regularly working from home. And according to Accounting Today, the global spread of COVID-19 has already contributed to a sudden surge of businesses moving over to cloud-based bookkeeping software.

Even before COVID-19, a survey conducted by Sage reported that about 67% of accountants believed that cloud technology can make their roles easier. And 53% of the respondents had already adopted cloud-based solutions for project management and client communication.

Near the end of 2021, we expect that this trend toward enabling remote work with cloud-based solutions will significantly increase for accounting and audit software as well.

3. Tackling big data in accounting

Big data is not just a buzzword anymore; it’s an opportunity for professionals to build strategy by analyzing large amounts of data from many sources. The problem is that data volume and sources can seem endless. In fact, there is broad agreement that the size of the digital universe will double every two years at least. IDC predicts that the Global Datasphere will grow from 33 Zettabytes (ZB) in 2018 to 175 ZB by 2025.

The ability to thoroughly mine the entirety of a company’s financial data requires smart tools. AI audit software helps auditors go through and make sense of large volumes of data in very little time. As touched on earlier, this increases an auditing team’s productivity and allows them to generate more accurate insights for the client.

According to a Sage research study published in early 2018, 66% of accountants said they would invest in AI to automate repetitive and time-consuming tasks. In 2022, as big data continues to surge, more accountants will likely agree.

4. Greater need for risk assessment and fraud prevention

Risk assessment is a core component of every audit. However, a recent survey of peer reviewers found over half of 400 audits they reviewed were non-conforming because of non-compliance with the risk assessment standards (AU-C Sections 315 and 330).

In recent years, companies have been recognizing that fraud is a growing concern. In 2017, a vast majority of C-suite and other financial executives surveyed by KPMG believed that auditors should use bigger samples and more sophisticated technologies for gathering and analysing data. This year, the COVID-19 situation is said to potentially increase risks of material misstatement and fraud. Much of this involves concerns of financial pressures facing corporations and employees, as well as breakdowns in internal controls with remote work situations.

For 2021 fiscal year-end audits, auditors are in a unique position to tackle these risks head on using AI audit software. That’s because the audit software enables better fraud detection and risk assessment by testing and performing statistical analyses on 100% of a company’s financial data. With a higher likelihood of fraud looming this year, accountants could be more willing to put AI software to the test.

5. Opportunity for growing advisory services

Traditionally, financial statement audits were driven by statistical sampling of past activities. But auditing practices as we know them are changing quickly. With access to more automated solutions, the future of auditing will likely involve real-time transaction analysis, risk evaluation, and data validation.

Using AI audit software today, an auditor can analyze the full scope of a company’s transactions and provide real-time insights regarding an organization’s risks and opportunities.

As we look ahead, auditors will likely be spending less time handling those manual, time-consuming audit procedures in 2022. Instead, auditing teams will have an opportunity to shift resources towards analyzing data, providing insights, and advising their clients. According to experts, a hybrid approach that combines the use of accounting technology and a focus on financial advisor input will continue to gain traction in the near future.

It’s time to capitalize on AI audit technology

While we can’t predict the future, we do know this— AI audit software will continue to help accountants and auditors 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. Those who are forward-thinking and ready to embrace artificial intelligence and audit technology will reap great benefits today, and tomorrow.

Ready to automate risk-based journal entry testing? Read this blog post from Solon Angel, Founder of MindBridge for some great advice.