Ai in Finance Archives

What Is anomaly detection, and why is it important?

September 26, 2022August 16, 2022 by John Colthart

what is anomaly detection blog hero image

Anomaly detection is a powerful technique for detecting fraudulent transactions and behaviors, thanks to financial institutions’ ever-increasing amounts of data.

Explainable AI and audit: transparency & trust

September 22, 2024August 11, 2022 by Coleman Howard

CFO using mindbridge risk analytics to find embedded leases

MindBridge’s Chief Technology Officer, Robin Grosset, and VP of Analytics and Data Science, Rachel Kirkham, hosted a virtual webinar on “Explainable AI, transparency, and trust.” The main focus of this webinar was to explore how business and audit professionals can rely on AI-produced data analytics results to make better decisions.

Simplifying remote audit using AI auditing software

September 21, 2024March 26, 2021 by Solon Angel

The explosion of remote work is one of the biggest shifts to come out of the COVID-19 pandemic. Social distancing rules and concerns of employee safety have forced many to settle into working from home. But what does that mean for the future of remote audit, and for the auditing software that makes it possible?

For accounting firms specifically, the necessary distance created by COVID-19 has meant a major increase in remote audits. While the process of embracing remote audits hasn’t exactly been easy for accounting firms, many are now turning to artificial intelligence (AI) to automate data assessment and analytics to retain the quality of their audits, while making remote work simpler and more efficient. Below, we’re exploring some of the challenges of remote auditing and how using AI in remote audits can be a game-changer.

Accountants weren’t prepared for sharp increase in remote audits

In the world of COVID and social distancing restrictions, the typical site visits that take place during audits have been put on hold. What was once a routine process of going into the field to comb through financial data, speak with key employees, check internal controls, and handle other in-person tasks has all been diverted online.

The problem is that many accountants weren’t prepared for this shift. A recent survey by IMA and Deloitte, which polled over 800 finance and accounting managers, showed that 75.7% of respondents said their company’s accounting processes are either largely manual or are still a considerable manual effort.

Because of this, the majority of auditors working remotely are facing big challenges. For one, figuring out how to securely access a company’s financial data is not always straightforward. Companies today are acutely concerned about cybersecurity risks and adhering to data and privacy protection regulations. To successfully handle remote audit engagements, accountants must choose solutions and tools that are fully hardened and meet cybersecurity best practices.

Fraud is on the rise during the COVID-19 pandemic

Even with secure access to general and sub-ledgers as well as other information, detecting risks across financial data has become harder. The fact is, fraud is on the rise as a result of this pandemic. Not only are companies under a lot of pressure to minimize loss and meet fiscal projections, but it’s extremely difficult to monitor internal controls when key employees are working from home.

For instance, an article in Accounting Today titled ‘The craziest work-from-home expenses of 2020’ shows just how outlandish some fraudsters have been with expense claims during COVID-19. Everything from a $7,600 facelift which was listed under ‘Repairs and Maintenance’ to €200 worth of tea which was credited as an ongoing company perk is being flagged. For every instance of fraud that is caught, another illegitimate expense could easily slip through the cracks.

To help counteract these new work-from-home challenges, 40% of respondents in the IMA and Deloitte report said that they’ll be implementing more automated tools in the future. Uniquely, just over 20% of those respondents are focusing on AI. That’s because whether in an office or at home, accountants can use AI auditing technology to strengthen remote audits and simplify everything from building an audit plan to identifying and assessing risks.

5 ways AI auditing technology enhances a remote audit

Increase fraud and risk detection with AI-powered insights – With fraud on the rise, accountants must be hypervigilant when combing through all financial data. Truly AI-embedded auditing software helps auditors run multiple algorithms across all client transactions simultaneously and cross-correlate data using dozens of testing criteria. Auditors can then effectively identify all potential risks or fraud within the financial data and negate the weakening effects that work-at-home situations have had on internal controls. By taking this data-first approach, auditors can also detect anomalies such as rare monetary flows and unique account activity which can be difficult, if not near impossible to anticipate or test manually. Working remotely with AI auditing technology essentially enables auditors to get a better understanding of risks across a client’s financial data.They can then focus on delivering quality assessments and audits and offer their clients more data-driven value.
Be better prepared to ask the right questions – With AI auditing software, accountants can become more effective at identifying real risks and anomalies versus a firm’s typical transactions. They can then direct resources to investigate those potential red flags and become better prepared when conducting interviews or gathering more information from clients. Honing in on riskier transactions and asking the right questions helps to enhance the accuracy of remote audits and ensures auditors deliver strong financial insights to their clients.
Build a more comprehensive audit plan – An AI platform will rank transactions based on risk level. The MindBridge risk discovery platform also gives accountants an intuitive visualization dashboard that shows a holistic view of a client’s financial transactions from month to month. This makes it easier for auditors to spot risks during remote audits and dive deeper into the data that stands out based on their professional judgment. These risk-based AI rankings also help to confirm an auditor’s own risk assessments and build a more comprehensive plan for the remote audit engagement.

Learn more about how MindBridge can help you sample less, and discover more.

Work with a secure cloud platform to access financial data – Choosing secure cloud-based AI auditing software can make all the difference in remote audits. Not only is it easy to upload and share financial data from various accounting software platforms, but leading AI auditing providers will offer solutions with built-in cybersecurity features and SOC 2 Type 2 compliance certifications. Sharing these details with customers before remote audit engagements can help ease cybersecurity concerns.
Get hands-on support for data ingestion and analysis – Working with new technologies to facilitate remote audit engagements can be overwhelming to some firms. Having hands-on support from solution experts can help ease the transition. Both auditors and their clients will feel confident knowing they have support at the ready should they have questions or need guidance. This support also ensures they get the most value from the AI auditing software.

Thinking long-term about AI for remote audit

As accounting firms everywhere navigate the challenges of remote audits, groundbreaking auditing technologies are just some of the tools helping them identify financial gaps and ensure quality assessments. And though work-at-home mandates may not last forever, the benefits of AI technology can. Accounting firms that choose to leverage AI technology for remote audits today will continue to see returns on this technological investment well after this pandemic subsides.

Are you wondering how to work new technologies into your existing audit process or what other benefits they can offer? Check out our article, “Should you update your audit methodology?”

Ready to embrace AI to strengthen your remote audit?

Contact our team to schedule a demo of the MindBridge risk discovery platform.

Should you update your audit methodology?

May 26, 2022March 23, 2021 by John Colthart

Does your audit methodology need a facelift?

When most people think of an auditor, they picture someone working away on a calculator with a gigantic stack of paper beside them. Invoices, transactional documents, payroll documents, let your imagination run wild. The point is, there’s always a stack of paper, albeit some have started to become digital in form. But, what if an updated audit methodology and audit process could change that?

But just because something was popular once, definitely doesn’t mean that it’s the best way to go about things. Take Pet Rocks for example. Tell me with a straight face that Pet Rocks were a good idea.

Audit best practices and compliance are constantly evolving, especially as data sets increase, remote audits become more prevalent and regulators/standard setters look for more analytically driven procedures. This also means that maintaining SALY (Same as Last Year) will challenge firms in staying relevant to their clients, risk of client acquisition or retention, and have peer reviews/inspections/audits scrutinized more thoroughly. To continue to offer clients top quality audits and risk assessments, add new value to their clients and win more business, auditors should routinely evaluate their audit methodology and process.

Here are just a few points to keep in mind if you’re on the fence about updating your audit process.

Audit evidence standards are modernized

Now is the best time to work on new engagement models, modernization and change. Sure it can be hard, we hear you, but the reality is that the industry has moved, and it’s to be expected that firms and individual auditors will need to keep up.

The American Institute of CPAs (AICPA) recognized that fact when it released the Statement on Auditing Standards (SAS) No. 142 Audit Evidence in July 2020. The new audit evidence standard, which takes effect for financial periods ending on or after December 15, 2022, modernizes private company auditing standards and includes significant updates around how technology and automation can be leveraged throughout the audit process.

“Our substantially revised standard addresses the evaluation of audit evidence and has been modernized to reflect our current business environment,” explained Bob Dohrer, CPA, CGMA, AICPA Chief Auditor, in a press release. “It recognizes the use of automated tools and techniques such as audit data analytics, AI, and remote observation tools to obtain audit evidence.”

With so much of the audit process tightly wrapped up in regulation, this new standard represents a huge step toward the future of the audit industry and acknowledges the ever-evolving nature of business.

For more detailed information on this new standard and what it means for your business, check out our blog, “How the new SAS-142 audit evidence standard embraces technology and automation.”

AI won’t replace auditors

When it comes to implementing new technology into your audit methodology, you might be thinking, ‘But what about my team?’

Since the dawn of technology, there has been apprehension about robots and machines replacing jobs done by humans. But here’s the thing: artificial intelligence will not replace auditors, but auditors using AI will replace those who are not using it. In fact, data science can augment an auditor’s experience and judgement.

Now, there is reasonable concern around AI’s ability to conduct an effective audit, and whether or not regulators are going to embrace these technologies as sources of high quality risk assessment and evaluation. But, as the revised ISA 315 audit standard shows, regulators are inching closer to the adoption of industry-changing technological changes, such as integrating data analytics into the formal audit process.

It’s safe to say that accounting and audit firms that embrace new technologies will dominate the market. The bottom line is that AI is about task replacement, not human replacement.

Want to learn more about how auditors are using AI?

The continuing implications of COVID-19

Before the global COVID-19 crisis, technology and automation were already on their way to becoming the future audit process norm.

However, the global pandemic underscored the need for the audit industry to more readily utilize new technologies. COVID-19 made change unavoidable and advanced the future of auditing and disrupted a long-standing complacency that had settled over the audit industry

While the pandemic may be temporary, many of the changes it has brought will be permanent.

There’s no question that COVID-19 has transformed how many firms will work and collaborate going forward. Since more teams are working remotely, a cloud-based AI auditing platform can simplify data sharing and ensure cybersecurity best practices are in place for the new norm of remote audits.

With AI-embedded auditing tools like MindBridge, customers can experience a more streamlined and integrated audit and risk discovery process.

It’s also important to keep in mind that just as your team is working remotely nowadays, so too are your clients’ teams. Providing a remote-friendly audit approach means your firm will be more relevant to current and potential clients, which, in turn, gives your firm a competitive advantage.

An updated audit methodology can add value for clients

Businesses have traditionally seen audits as simply a compliance exercise, and that auditors merely verify if financial statements comply with standards, and find out whether or not their transactions look risky.

However, that perception is changing, and clients are now expecting more services than a calculator and Excel spreadsheet can offer.

Financial technology and automation have given rise to a component that’s changing the audit field: insights. AI-embedded audit tools allow for detailed risk assessments and insights, which provide added value to a client and result in higher quality audits altogether.

The future of auditing will have a heightened emphasis on exploring data trends, studying risk characteristics, and real-time transaction analysis. With these capabilities, auditors can gain a deeper understanding of their clients’ financials. At the same time, clients have greater confidence in the audit process.

When it comes to audits, clients now want more than a rear-view mirror perspective. They want to know what to keep an eye out for.

That’s why diversifying your firm’s offerings will be fundamental to longevity and growth in the future world of audit. Adopting an AI-embedded risk discovery and audit procedures not only makes audits more effective and efficient, but also allows for expansion into advisory and transaction services. Modifying and diversifying services adds incredible value for clients and can lead to a more regular income stream for firms, not to mention smoothing your delivery timing and ridding teams of rote and menial tasks.

Embracing change in your audit methodology

Let’s be honest, not everyone embraces change. It can be intimidating; it’s new and unknown. It can take a lot of effort and planning to put something new into action. But more often than not, change is beneficial.

Evolving and implementing new practices is an essential part of doing business today. It’s safe to say that a retail business that doesn’t utilize technology to have an online presence would most likely fail in the marketplace today (let alone tomorrow).

Of course, updating your audit methodology doesn’t happen overnight. It can be a long and tedious process that may even require some research on change management best practices. An important aspect of changing your audit methodology is finding an approach that’s right for your team.

At MindBridge, we can help you develop your new audit process that meets both your needs and the needs of our clients. While there are many unknowns about the future of audit, one thing is for certain: AI will be a part of it.

Want to learn more about how AI is reshaping the audit industry? Register for our on-demand webinar “Demystifying artificial intelligence and the impact on auditing.”

Financial automation: The good, the bad, and the future

September 22, 2024March 18, 2021 by Benjamin Wyrick

Well, it’s finally here. According to an article from Forbes Magazine, we have reached the age of automation. From AI and machine learning to financial automation and robotics, we’re officially an automatic civilization. Please, be kind to our new robot co-workers.

Okay seriously, this is important stuff, even if we did all see it coming. Especially when it comes to the ever-expanding world of finance.

In every industry, every business, and every firm, finances and how they are managed are vital to the growth and development of a company. Whether you’re a business owner, CFO, or part of the finance department, the role of automation in the future of finance is vital to your role, growth, and the evolution of your organization.

Financial automation doesn’t just mean automating payroll, although it doesn’t hurt to do that as well. Automating financial processes incorporates much more, including risk assessment, audit, and compliance among many other aspects.

An article from DigitalistMag outlines the capabilities of today’s financial automation services, describing the ability to “gain new insights from existing data to optimize credit decisions and improve financial risk management, automating business processes that previously required manual human intervention, and improving the customer experience.”

Financial management has evolved rapidly since the advent of computational technology. As this technology evolved, financial experts and professionals soon recognized that process standardization and centralization are absolutely necessary to increase the efficiency and effectiveness of modern organizations. As efficiency grew into a central tenant of management processes, financial automation became the next logical step for businesses and organizations.

In 2016, McKinsey estimated that 60% of all occupations have approximately 30% or more capabilities that can be automated with existing technology. Moreover, there has been a significant change in the understanding of what can be automated and what should be automated, which has become increasingly evident due to the unprecedented effect the COVID-19 pandemic has had on work.

For businesses looking to hire and outsource their financial processes or professionals who want to simplify and streamline internal processes, it may be time to look at automating them instead. For many, this has already begun, as “CFOs around the world heavily invest in financial automation software as a next step in the evolution to enable enterprise transformation.”

In this way, financial automation could lead to a complex or fundamental shift in how an organization’s core business is conducted.

Taking the first step toward financial automation can seem daunting. However, with more businesses adopting automation into their day-to-day financial practices, it’s clear to see the power this technology holds.

So, what exactly is financial automation?

What is financial automation?

For us mere mortals, financial automation can be as simple as automatically depositing your paycheck, paying bills, or saving a portion of your income per month. The concept is similar for businesses and corporations, but at a much larger scale, and with a lot more moving parts.

Financial automation is the process of utilizing technology options to complete tasks with minimal human intervention. These tasks would normally be accomplished by employees, which, in theory, frees up time for them to perform more complex tasks.

According to another automation study from the McKinsey Global Institute’s automation research, current in-use technologies can fully automate 42 percent of finance activities and mostly automate a further 19 percent.

While many still consider financial automation and intelligent software to be on the horizon, organizations have already started to utilize cutting-edge tools and technologies such as advanced analytics, process automation, robo-advisors, and self-learning programs. A lot more is still yet to come as technologies evolve, become more widely available, and are put to innovative uses.

Levels of automation

The initial forms of automation were (and still are) macros and scripts: simple rules-based automation that repeated simple work with highly structured data – things like general accounting operations, revenue management, and cash disbursement have an over 75% fully automatable ability with already existing technologies.

Robotic process automation (RPA)

RPA is the basis (above macros and scripts) to understand the capabilities of automation. An example of an RPA would be simple software that can perform repetitive tasks quickly with minimal effort, like some of the rote tasks mentioned earlier.

According to the 2017 McKinsey research (also mentioned earlier), about a third of the opportunity in finance can be captured using basic task-automation technologies such as these.

Artificial intelligence (AI) and intelligent automation (IA)

On the other end of the spectrum is artificial intelligence. Artificial intelligence is theoretically achieved when software is able to make intelligent decisions while still complying with controls using algorithms or machine learning.

Machine learning algorithms demonstrate the ability for computers to take in a constant stream of data, analyze that data for patterns and recommend solutions to problems humans can’t even see, proving vastly positive results in improving a company’s financial proficiency.

Once a dream for financial professionals and business owners, this form of financial automation software is becoming a reality, shaking up the way that tasks are performed, and even introducing other aspects such as forecasting into the mix.

Improvements with financial process automation

The umbrella of finance – from payroll to predictive forecasts can involve menial and repetitive tasks which leave limited time and resources to focus on value-adding activities to grow your organization. When financial process automation is added, it serves as a pivotal support to free up needed resources and time.

As these technologies can cover more ground and more deeply analyze company financials, many organizations are finding that AI and automation technologies are actively improving their bottom line. According to a survey from the Association of Certified Fraud Examiners via the Harvard Business Review, “organizations lose 5% of their revenue every year due to fraud. The typical fraud case causes a loss of $8,300 per month and lasts a full 14 months before detection. And lack of internal controls contributed to nearly one-third of all fraud cases.”

Risk discovery is just one aspect of financial automation, but a growing one.

As AI, RPA and IA continue to use machine learning to do more and perform more intricate tasks, offering insight into finances, we are seeing how this can be incorporated into an organization’s long-term organizational strategy. MindBridge, for example, has developed AI technology for risk discovery, a complex financial task that incorporates not only transactional analysis, but offers broader insights into financial health and integrity.

Want to learn more about how auditors are using AI?

By automating certain financial processes, “finance professionals can not only provide real-time insights into the current status of the business but, with advanced predictive algorithms, they can look into the future and proactively steer the business.”

Financial automation and its capabilities are excelling at a fast rate. With the help of AI, RPA, and IA, standard automation practices can be enriched beyond simple pre-programmed controls and scripts. From McKinsey & Company once again, AI algorithms can learn from historical datasets and the interactions of the financial professional with the system, thereby improving the matching rates tremendously. In this context, matching rates refer to the ability at which an AI system is able to tag users to certain data sets based on their profile of demonstrated usage. Furthermore, the AI technology allows automatic extraction of unstructured information from documents, such as emails.

Of course, return on investment is always a concern. It can take a lot of time and effort to implement new technologies, and savvy business leaders need to know that the tools and processes they put their money behind will work.

According to Gartner, “AI augmentation will create $2.9 trillion of business value and 6.2 billion hours of worker productivity globally.” Basically, they define this term as the combined work of humans and technology, with the people at the center of the operation.

Source: Gartner.

If these forecasts are correct, executives should be clamouring for AI and automation investment. Even a small piece of this pie can level up your office, department, or organization writ large.

What financial process automation could mean for work structure

One of the biggest concerns associated with exploring financial automation and therefore implementing financial automation software is what happens to the employees and the roles formerly associated with those finance objectives.

There’s no doubt that introducing financial automation will change the roles of many employees and even the manner to which employees are trained or progress toward career objectives. One thing is for sure though, automation will replace low-value, simple, and time-consuming tasks, thereby giving staff the flexibility to expand their roles, and spend more time on value-adding activities to help drive a company’s competitive advantage.

In an article from PWC on change management, they outline five steps that can help firms adopting financial automation make the transition as smooth as possible:

Prepare for human capital risks like you’d prepare for any other risks
Help people find their way
Create organizational support for success
Expect changes to jobs, compensations, and structure
Learn new ways to develop your team

To unlock financial automation’s full potential, managers must be willing to re-engineer processes, and redeploy resources to optimize efficiency and output.

Another consideration for anyone looking to adopt automation and AI technology is assurance and verification. This verification work ensures that the technology in place is doing what it’s supposed to do, at the level of work required to meet compliance requirements and quality assurance standards.

Internal teams can “test” automations by utilizing what are known as “Test Frameworks” for applications. Some examples of framework tools come from SmartBear and Selenium. However, it’s a lot of work, and unless you have dedicated developers that can help your team test automation tools, you’re sort of stuck. For many businesses, it’s much easier to work with platforms and tools that have done this testing themselves by utilizing a third party.

A future with financial automation

Although IA and machine-learning algorithms are still considered in their infancy, that doesn’t mean finance leaders should wait for them to mature fully. According to McKinsey, many automation platforms and providers that struggled a decade ago to survive the scrutiny of IT security reviews, are now well established, with the infrastructure, security, and governance to support enterprise programs. “Where a manager once had to wait for an overtasked IT team to configure a bot, today a finance person can often be trained to develop much of the RPA workflow.” The exponential growth in structured data fueled by enterprise resource planning (ERP) systems, combined with the declining cost of computing power, is unlocking new opportunities every day.

MindBridge is a great example of a pioneer in unlocking the expanded capabilities of AI and RPA within the finance sector. With AI-embedded risk discovery, MindBrige can risk-rate 100% of the transactions in 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.

The future of financial automation seems bright, already beginning to reshape the way in which financial services are performed in organizations large and small. Incorporating AI, RPA, and other forms of automation can seem daunting at first, as there are many tasks and organizational changes that go into implementing new technologies and processes.

By empowering your finance team with AI co-workers, they reduce the time spent on mundane tasks, enabling your team’s human intelligence to shine operationally. Financial efficiency and accuracy means happy stakeholders, and a growing business. What’s not to love?

For more articles like this one, visit our Resource Center.

A better approach to journal entry testing: Audit analytics automation

May 26, 2022September 7, 2020 by Solon Angel

Internet companies have been driven by data for decades. For instance, Amazon was using basic AI systems over 20 years ago. Netflix, Microsoft, Google and many others have dominated their categories by using a data and algorithms-first approach. Yet when we look at the accounting world, many still believe that data and analytics are a novelty, optional, or separate from the work that they do.

When it comes specifically to journal entry testing, most auditors today have been using antiquated approaches and sampling techniques. Many justify the use of these limited audit risk methods by saying they comply with existing standards. But these standards such as SAS 99, Consideration of Fraud, actually only require auditors to gain an understanding of the business and focus on identifying items that warrant further auditor considerations.

According to SAS 99 or other international standards, there is nothing to discredit the use of advanced methodology and latest AI-powered technologies. In fact, almost 20 years ago, the American Institute of CPAs published the 2003-02 Practice Alert with guidance for the use of analytics. Today, recent advancements in auditing software allow accountants to better evaluate audit risks and deliver pertinent insights to various stakeholders.

The challenges with traditional journal entry testing

Traditionally, accountants had a lot of groundwork to do during an audit risk assessment. First, they would spend a considerable amount of time doing data preparation on usually limited data columns and file sizes. Then, they would try to determine which analytics to apply to the data.

As Enterprise resource planning (ERP) systems grow more complex, not all audit procedures can keep up. Data clipping or manually converting a GL report into an Excel file is known to exclude data or cause errors during the audit process.

Existing script-based data analytics engines are exclusionary based, meaning they extract data as an auditor applies various procedures. This decreases the chance of detecting anomalies and doesn’t allow for a truly comprehensive audit risk assessment. This is why many leading accounting firms, including the Big Four, are moving away from these outdated auditing procedures. These more traditional methods for risk-based journal entry testing cause inherent liability and poor quality.

Using more advanced AI-powered auditing software, an audit team can gain more far-reaching insight. By pinpointing control points, the AI auditing software can identify and learn what’s normal or not and then analyze a wider range of data without inherent exclusions.

3 ways to automate risk-based journal entry testing

1. Start with a data-first approach

Before thinking about which audit tests or procedures to apply, you need to start with the data. This is called a bottom-up approach to audit risk assessment, instead of top-down. The idea is to let the data speak first. Then, you can look for standard procedures and identify any underlying risks.

Seek to get as much information on the system available as possible from your client: GL reports, Charts of Accounts, opening and closing balances, bank statements, as well as the previous year’s data.

With this modern approach, you can leverage historical data in new ways. This can include automatically doing pre-emptive calculations and forecasts to better understand potential audit risks.

For example, MindBridge Ai Auditor automatically generates ratios and forecasts that you can annotate and add to your audit plan, seamlessly.

2. Leverage the community effect

Try to avoid reinventing the wheel and be curious of what automation can accomplish. It is not just about using new auditing technology. Try to understand the definition of risk that is built into the automation. A few AI or cloud accounting software vendors like MindBridge have spent countless hours with industry partners embedding specific risk analysis into their software packages.

Auditors are required to “test the appropriateness of journal entries recorded in the general ledger and other adjustments”. In the past, you would have had to define the procedures yourself. But today, with everyone connected online, communities have emerged around your choice of tools. These communities include other accountants that might have implemented fully automated procedures into their methodology and are eager to contribute best practices and tips with others.

During the Influence 2020 conference, some MindBridge Ai Auditor customers such as Baldwin CPAs and GRF CPAs shared their first-hand experience of using our AI accounting software as well as practical advice for other users.

3. Pay attention to complex transactions

Your clients are not in the business of ensuring the right controls or worrying about anything else other than running their business. They simply don’t anticipate bad behavior, bad actors, or white-collar criminals. It is not enough to just design procedures or automate the classic CAATs-style audit tests. Instead, you can leverage the full power of advanced audit risk assessment techniques such as “Rare Flows” and “Expert score” using powerful AI auditing software. These improve your ability to detect high-risk transactions or the sidestepping of the company’s internal controls.

Some employees, including senior management learn ways to work around a specific control. For example, employees can post numerous smaller journal entries to various departmental general ledgers to circumvent approval processes. This also makes it more difficult for auditors to detect the fraud.

This is where AI can excel and really help you. Rare flows and unusual transaction analysis can help you quickly identify audit risks and conduct a more thorough journal entry testing. After saving time on the previous tasks, you will be able to dig into the data and ask the right questions.

Evolving audit risk assessments and your business

Accountants and auditors are not here just to perform repetitive tasks or follow outdated procedures. The core principle of the profession is to be business advisors to their clients.

By using advanced technology for risk-based journal entry testing, auditors can streamline the auditing process and avoid spending billable hours digging for issues in only one area. Instead of limiting themselves to simply extracting data from a general ledger, they can ask for more reports and more data. This allows them to get a deeper understanding of all the anomalies in client files to perform a more thorough audit risk assessment.

With greater automation in journal entry testing, auditors will be able to get more insights from a larger dataset in minutes, and their clients will notice. That’s because after using AI accounting software in the auditing process, the audit team will be able to ask more relevant questions that lead to smarter business outcomes.

Want to learn more about the benefits of AI auditing software? Read how K·Coe Isom embraces AI accounting technology to gain new insights into their clients’ businesses.

How AI and data can power an effective audit plan

September 29, 2024July 2, 2020 by Gena Song, CPA, CA

An effective audit starts with a solid audit plan. While the overall audit strategy and plan can vary between clients, an auditor will usually establish risk assessment procedures and a how-to response for the risk of material misstatement.

The challenge is that sometimes, even the most thorough and comprehensive audit plans can still have gaps. In fact, every auditor understands there will likely always be some degree of uncertainty and unidentified risks before an audit begins. It’s in the initial audit planning stages that an audit team will often ask:

How can we lessen those unknown risks?
Is there an opportunity to confirm initial assessments about the industry or company?
Are there blind spots that we haven’t considered?

This is where machine learning (ML) and artificial intelligence (AI) can help. In this blog, you’ll learn how you can use MindBridge AI to spot risks and shift resources during preliminary engagement activities through each phase of the audit planning process.

Pinpointing audit risks using a data-driven method

Identifying the inherent business risks associated with the company is an important first step in the audit planning process. An auditor must analyze key risk factors such as understanding the industry risks, the company’s business, and any recent changes within the company to determine if and how these considerations will impact the audit plan.

Using Ai Auditor, an audit team can enhance the risk assessment process by retrieving powerful risk insights. That’s because Ai Auditor examines 100% of the company’s transaction data and alerts the team to any anomalies or underlying risks associated with the entity. With detailed data at-hand, the audit team can then move forward with greater confidence in the audit engagement, trusting that the risk assessment is comprehensive and complete.

Ai Auditor can also help the team to identify new risk areas that have might not been flagged in previous audits and include them in their audit plan. Not only does this ensure a well-planned audit, but it also minimizes the potential for duplicating audit procedures later on.

Evaluating the effectiveness of the company’s internal control over financial reporting is another area where using Ai Auditor can be a benefit. Much like traditional testing, the platform automatically identifies control points to spot high-risks transaction data. The auditing team can also adjust these control points and use other capabilities within the platform to recreate traditional control testing models. This data-driven audit method saves the team time while ensuring high levels of accuracy and diligence.

Building an effective audit strategy with Ai Auditor

After initial risk assessments and tests, the auditors will be able to establish an overall audit strategy. This sets the scope, timing, and direction of the audit and guides the development of the audit plan.

For instance, the audit team will derive important conclusions after evaluating the effectiveness of internal control over financial reporting. These will help them decide whether to use control testing, substantive testing, or a combination of both in their audit plan.

When planning the timing of the audit, the team might also consider using Ai Auditor during interim analysis and take advantage of roll-forward capabilities at year-end to ensure a more effective audit.

Considering how much time and resources go into an audit, Ai Auditor can become a force-multiplier for an audit team. The platform provides insights that help them become more efficient as they move through audit planning to engagement completion.

Developing an audit plan with data at your fingertips

As an auditor begins developing and documenting the audit plan, the reporting features within Ai Auditor can help. An auditing team can export powerful graphs and data to support the audit plan regarding details such as the planned nature, timing, and extent of the risk assessment procedures; the planned nature, timing, and extent of tests of controls and substantive procedures; and other planned audit procedures.

The team can also use Ai Auditor to download a single report that details any flagged items and automatically add this report to the audit plan. This ensures the team conducts deeper investigations on those transactions or simply helps to justify why certain samples were selected.

Completing the audit engagement with success

Ai Auditor helps to simplify auditing planning. The platform offers valuable insights and data that help an auditing team streamline risk assessments, build an effective strategy, and outline a comprehensive audit plan. And since an audit team will be able to conduct investigations easier and faster through every phase of the plan’s process, they’ll have more time to offer clients valuable insights and guidance.

Looking for more? Register to access our on-demand webinar titled ‘Riding the Waves of Transformation’ with Tom Hood, CPA.

Improving audit risk assessments with AI-driven analysis of Accounts Receivable and Accounts Payable subledger data

September 29, 2024June 18, 2020 by Stefano Colaiacovo, CPA, CA

Improving audit risk assessment longterm

The cornerstone of well-planned and high-quality audit engagements is a robust risk assessment process. Such a process is critical to identifying risks of material misstatement and their relative significance by providing a fulsome understanding of the entity subject to audit and the environment in which it operates.

The nature and extent of these audit risk assessment procedures will certainly differ from engagement to engagement, reflecting different types of operations, industries, and financial reporting complexities, however preliminary analytical review procedures are a common thread across all audits as a requisite component of the risk assessment process.

Traditional preliminary analytical review procedures

Practically speaking, preliminary analytical review procedures could include any combination of the following (not exhaustive):

Comparing actual financial performance to historical trends and balances
Reviewing actual financial performance (ratios, key financial metrics) against industry benchmarks
Reviewing actual financial performance compared to management forecasts and/or budgets
Performing inquiry of management to ascertain operational drivers for certain trends and patterns in the year-over-year results (i.e., “what’s changed?”)
Examining any material new contractual agreements executed in the period (leases, customer contracts, debt agreements, etc.)

Traditionally, these types of analytical review procedures take place at the level of how the financial statements aggregate the data by account or class of transactions, or perhaps at more granular levels of the chart of accounts. For example, you may compare how gross margin in the current period compares to historical periods or how increases in inventory year-over-year tracks with corresponding movement in the cost of sales accounts. In any case, it is ultimately the general ledger trial balance data and activity detail that underpins this type of review.

With a view towards a robust risk assessment process and obtaining a deep and operationally relevant understanding of your client’s business environment and financial performance, analysis and interrogation of the AR and AP subledger data as a complement to the traditional preliminary analytical review procedures at the financial statement level could be a source of highly valuable context to the results and empower you to conduct a more focused inquiry of your client’s management.

Accounts Receivable & Accounts Payable as critical inputs to audit risk assessment

Visualizing and interrogating subledger data can provide high-value insights and expose “root causes” behind some of the general ledger variances and patterns identified as part of your traditional preliminary analytical review procedures. This empowers you to better pinpoint an assessed risk and tailor your testing approach to most efficiently respond to that assessed risk.

Some examples of how to best leverage subledger information include:

Understanding how certain vendor and customer aged balances trend throughout the year

The aggregate total values of AR and AP at balance sheet dates might be relatively consistent year-over-year but there may be cause for further investigation and inquiry if, for example, the monthly ending balances demonstrate significant volatility throughout the year or seem out of pace with corresponding monthly sales or purchasing trends.

Understanding operational key performance indicators for customer and vendor “health”, and tracking those over the audit period

Tracking basic operational metrics like Days Outstanding and Turnover ratios, for specific vendors, customers, and in total, provides a lens of relative customer “quality” or vendor settlement patterns that may allow for risk to be identified more granularly. Comparing these ratios for a particular customer against the “aggregate” value allows you to identify specific customers or vendors that lag the overall average and therefore may indicate an existence or valuation risk around those balances or underlying contracts.

Expose the nature and volume of transactions on credit with related-party customers and vendors

Reviewing the subledger detail for transactions with all related entities is information that may not be readily available on the surface of the general ledger data and the relative dollar volume and activity of these transactions could be relevant to how risk is assessed around the accuracy, valuation, and presentation assertions.

Surface invoices or other records in the subledger (debit or credit memos, unapplied payments, etc.) that may be significantly aged

Isolating items in the subledgers that are significantly aged may tie directly to the risk around valuation and existence of these items specifically. Under a more nuanced lens, the existence of these types of stale records (or lack thereof) may be a relevant consideration to corroborating your understanding of the controls framework and how closely the subledgers are being reconciled and actively maintained.

Evaluate the volume and frequency of transactions at the level of a specific customer or vendor to corroborate inquiry of management and your understanding of the entity

Understanding basic data points around volume and frequency of transactions with a particular customer or vendor may help corroborate information learned from inquiry or your knowledge. For example, reviewing transactions with the entity’s landlord to confirm that 12 monthly equal rent payments were posted. Scanning this type of activity (either manually or with automated techniques) can surface invoices or payments for amounts that are potentially unusual for a certain customer or vendor and therefore perhaps may be indicative of risk.

Review for the volume and frequency of manual adjustments directly to the subledger detail

Manual adjustments or entries directly to the subledger, i.e., entries that don’t have a commercial document of record (invoice, cheque, credit memo, etc.) associated to them, may indicate fact patterns or internal processes that warrant further consideration from an audit perspective.

Perform basic statistical and rules-based tests and interrogate the subledger data to inform risk assessment

Certain procedures around data quality that are traditionally associated with journal entry testing, such as the following, may be very relevant to the subledger information. This includes any “hits” that would be relevant to deepen your understanding of your client’s accounting system and internal control framework and also advise the severity of assessed risk:

Reviewing descriptions for suspicious keywords
Duplicate document IDs
Two-digit Benford analysis
Other rules-based tests

How MindBridge automates and streamlines AR & AP subledger analysis

MindBridge AI has dedicated AR and AP modules that automatically analyze the subledger data and, without any scripting, provide high-value visualizations of the data and transaction-level analysis. These capabilities empower you to leverage subledger-level insights and anomalies as critical inputs to the audit risk assessment process.

Trends and patterns

Ai Auditor provides the ability to visualize how monthly AR and AP balances or net monthly activity tracks over multiple years, at the customer and vendor level and also in aggregate. The visualization is customizable and provides the ability to compare certain customer or vendor trend lines against each other and identify patterns of deviation.

Vendors and customers who are related parties to the entity subject to audit are flagged directly in the summary detail to identify for specific review.

Key performance indicators

Days Outstanding and Turnover ratios are calculated at the customer and vendor level and visualized on a monthly basis, allowing you to identify where there are periods of potential distress or deteriorating quality. Similar to the ending balances and activity, you are also able to customize the visualization and compare certain customers or vendors against each other along the lines of these metrics to expose patterns of interest.

Ai Auditor also automatically identifies any new customers or vendors in the audit period, allowing you to identify the related volume of sales or purchasing growth specific to these entities.

Aging

Aging at the customer and vendor level is automatically calculated and captured across respective buckets of days outstanding (0-30 days, 31-60 days, etc.). For certain entries that are significantly aged or stale, you’re able to drill-in to all the transactions with a particular customer or vendor and ascertain which invoice(s) are contributing to those totals.

Data interrogation and risk

Navigating and querying the transactional level data via the Data Table in Ai Auditor provides a powerful and effective way to explore and validate the subledger activity. The Filter Builder functionality allows for multiple conditions to be placed on a query, using any element of the transactional record (date, amount, user, entry type, etc.). This allows you to build and save functions that allow you to get a sense of the type, frequency, and volume of transactions with certain vendors or customers.

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

Combining the query building capabilities of the Data Table with the feature of every transaction being scored against the various Control Point tests, you are empowered to identify relevant populations for sampling and have selections automatically identified on a risk-stratified basis. Approaching the sampling process through the lens of transactional risk ensures that you’re focusing your audit procedures around the entries which appear anomalous.

Take the first step towards unlocking critical subledger-level insights for risk assessment

To learn more about Ai Auditor and subledger analyses, contact sales@mindbridge.ai.

AI in finance: Helping professionals shift from hindsight to insight to foresight

December 18, 2020June 11, 2020 by Eli Fathi

We are facing an unprecedented time of global uncertainty created by the COVID-19 virus that has unleashed a global healthcare crisis. Humanity is fighting a war against an invisible enemy that is attacking humans around the world and sparing no country. We need not be pessimistic or optimistic but rather realists and learn from the history of humanity. Human ingenuity will prevail, and humanity will survive.

We have entered a new world after COVID-19 with very different assumptions than we had in the old world when the world GDP yielded a record high of $85T. The world GDP has been severely impacted by the lockdown stipulations that were imposed to minimize the spread of the virus within the population. The key pillars of the economy are consumer and companies’ spending. If this slows down, it can lead to a recession and even depression. The lockdown restrictions are being relaxed and governments and central banks around the world are injecting massive amounts of funds into the hands of individuals and companies in an effort to reopen the economy to avoid an economic crisis.

How can artificial intelligence in finance help organizations pull through?

A renewed focus on financial errors

During economic uncertainty, an added vigilance is needed by those responsible to ensure the accuracy and integrity of the financial records that are being relied upon to make decisions about the operations of their organizations. A report by the Association of Certified Fraud Examiners (ACFE) “2020-Report to the Nations”- 2020 Global Study on Occupational Fraud and Abuse estimates that the yearly cost to the world due to fraud and abuse is about $4.5T or 5% of the world GDP. They examined over 2500 cases from 125 countries with combined losses of $3.6B with an average loss by case of $1.5M and a typical case lasting 14 months before being detected.

Whereas corruption was the most common type of fraud, the most costly were financial statements fraud schemes, even though they represented only 10% of the cases. The breakdown of the detection methods reveals that analytics plays only a small role in the detection of occupational fraud: Human tips; 43%, internal audit; 12%, management reviews; 5%, by accident; 5%, whereas external audit catches only 4%.

A 2019 survey by Blackline provided insights into the concerns by executives with inaccuracies in financial data. With over 1100 C-suite executives and finance professionals from mid- to large-size organizations around the world, the white paper stated that:

“55% are not confident that they can identify financial errors before reporting results, 70% claim that their organizations made a significant business decision based on inaccurate financial data and 26% are concerned over errors that they know must exist but they have no visibility”.

The power of AI in finance

Finance professionals that rely on outdated tools and methodologies do not offer the best visibility into finding errors, errors with intent, errors that are considered fraud, and general mismanagement of the financial dataset in their organizations. The world is already witnessing a major trend toward moving to the cloud and becoming digital native and these must be vigorously pursued by organizations that want to be of the forefront of growth post the crisis.

Becoming digital native enables companies to move towards a near real-time view of their financial data and, coupled with AI in finance functions, the ability to fully analyze 100% of transactions. This ensures transparency to key stakeholders such as board members and auditors and aids in the identification of any anomalies in their financial records.

Currently, a company’s financial records are examined by external auditors on a yearly basis and evaluated using a sampling method that leaves the bulk of the dataset untouched. This method of rear view-mirror assessment provides C-suite executives with a hindsight perspective and the fear that decisions are made based on inaccurate and untimely information. Using AI-based tools to review 100% of the financial records in near real-time offers C-level executives with insights into data and, by using the appropriate analytics built into the AI applications, offers foresights into the operations of the company.

The two most important behaviors that companies must have to thrive post COVID-19 are resilience and adaptability. Resilience is defined as the ability to withstand or recover quickly from difficult conditions whereas adaptability is defined as the quality of being able to adjust to new conditions. Companies must build their operations and culture around resilience and adaptability so they can work efficiently during the “new normal” when we emerge out of this dark tunnel will become stronger and better off.

An article published by the Boston Consulting Group titled “The Rise of the AI-Powered Company in the Postcrisis World” highlights the tremendous opportunity for companies that are going to digital native, moving to the cloud, and adopting AI in finance applications to supercharge their operations. Arvind Krishna, in his inaugural speech as IBM’s new CEO, said, “I am predicting today that every company will become an AI company – not because they can, but because they must. Digital transformation means putting artificial intelligence at the center of workflows, and using the insights generated from that process to constantly improve products and services.”