Revolutionizing accounting oversight: MindBridge’s next-generation anomaly detection 

The accuracy of financial data is not just an operational need but a strategic imperative. Clean financial data is crucial for reliable financial analysis, accurate reporting, and sound decision-making. Finance teams need to be the trusted source of truth. This allows the business to maintain consistent compliance, manage risks, and optimize performance in business operations … Read more

Change management: What is it, and why is it important?

Change is scary. But with a little risk, a lot of planning, and some extra effort comes an opportunity for growth and reward. That’s what makes change management so important.

As a manager, department head, or executive how do you know when it’s time for change? How do you invoke change within an organization, and how do you get others on board?

Studies in what’s known as change management have shown that there is no one single answer to what most influences and leads to successful transformation initiatives.

In recent years, change management strategies have focused on soft factors like culture, leadership, and motivation. Each of these play a key role in a successful transition. But, for change to truly take hold, it’s also important to focus on the hard factors like duration, integrity, commitment, and effort.

In this article, we’ll discuss the definition of change management, address corporate responsibility during the process, what you and your team need to do to be successful, and show you the best ways to implement transition skills  and best practices into your organization and projects.

What is change management?

Change management is a big, daunting term, let alone task. It’s a rather condensed way of explaining the process when an organization takes on projects or initiatives to improve performance, address key issues, and seize new opportunities. These endeavors may require companies to shift their methodologies, roles, organizational structures, and perhaps even the types of and uses for technology.

Successful transitions dependent upon four core principles. These principles are important to understand before undertaking a large shift in processes or anything else, no matter what the context:

  1. Understanding change – Understand the questions that need to be asked, the why, and the “ins and outs” of the change.
  2. Planning change – This looks different for every organization, but can include achieving high-level sponsorship, identifying stakeholder involvement, and motivational techniques and establishing a team responsible for managing the change.
  3. Implementing change – Roll out the change, ensure everyone has been trained on the new process, technology, etc, knows what their role is and the importance they play in affecting change.
  4. Communicating change – Tools to help everyone understand why the change is happening, the positive effects that will come and the steps to required to ensure success.

Now, that’s just a brief overview. Here’s an in-depth review of these four principles, and how each of them help you work toward successfully-managed change in your organization.

Understanding change management, implementing best practices

Understanding change management begins by understanding its three important levels

According to Prosci, a change management solution, the three levels are: 

  • Individual 
  • Organizational 
  • Enterprise 

In this model, enterprise change management is therefore dependent on both successful individual change management and organizational change management. Each of these aspects build onto one another to enact lasting, ingrained change across your department, team, or organization.

Individual change management – This will require tapping into the mind of your employees. It requires understanding how people experience change and what they need to handle it successfully, and thrive post-implementation. 

ADKAR is a great acronym created by Prosci founder Jeff Hiatt that represents the five tangible and concrete outcomes required for individual staff. 

The acronym stands for:

A – Awareness of the need for change
D – Desire to support the change
K – Knowledge of how to change
A – Ability to demonstrate skill and behaviors
R – Reinforcement to make the change stick

A – Awareness of the need for change
D – Desire to support the change
K – Knowledge of how to change
A – Ability to demonstrate skill and behaviors
R – Reinforcement to make the change stick

For success at the individual level of change management, companies need to be able to communicate these five ADKAR elements to their employees in order for them to understand why the necessity of the change, where the change is coming from, how they can support the change, and how they will be impacted from it and the benefits the change represents.

Organizational change management – These are the steps and actions taken at a project level to support the individuals impacted by the ongoing change process. It starts by identifying the groups or people who will need to change, and in what ways. Once identified, successful organizational change management requires a customized plan for each individual to ensure that they receive the awareness, leadership, and training they need to be successful going forward.

Individual employees are at the center of successful change management processes; their success or failure will determine the success or failure of the processes that are changing organizationally. 

Enterprise change management – This is the ‘final’ level of change management and essentially means that effective change management is embedded into your organization’s roles, structures, processes and leadership competencies. When it comes to enterprise change management, newly-implemented processes are consistently applied to initiatives, leaders will have the skills to guide their teams through the change, and staff will know what to ask for to be successful.

When embedded into your structure, enterprise change management capability means that individuals embrace change more effectively, and the organization itself is able to respond faster to market changes, embrace strategic initiatives, and adopt new technology much more rapidly. 

Now that we’ve established the benefits and principles of managing change, how does it work, exactly?

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

A – Awareness of the need for change
D – Desire to support the change
K – Knowledge of how to change
A – Ability to demonstrate skill and behaviors
R – Reinforcement to make the change stick

How does change management work?

Change management relies on cohesive effort between management and employees to lead a successful transition. If leadership is not able to create a solid plan, and if employees are unable to “embrace and learn a new way of working, the initiative will fail.”

Take transitioning financial technologies and processes, for example. As technology improves and data sets increase, financial professionals and their departments are feeling the pressure to do more in less time. The trouble comes when the quality of work suffers as a result of the attempt to marry efficiency with quality. This is especially true of risk management and discovery. 

Platforms like MindBridge help organizations discover the known and unknown risk in their financial data sets. They can analyze 100% of transactions, provide insights to better communicate analysis with stakeholders, and ultimately produce higher quality work in a fraction of the time.

But, all of this requires a solid, well-executed change management plan. While new technologies are increasingly turnkey, unlocking their full potential takes buy-in at all levels of an organization, and investment in the principles of change. 

At MindBridge, we strive to enable our customers with the tools, resources, and support they need to successfully transition their financial processes. But, for the organizations themselves, there is still work to do. 

When it comes to changing any process or technology, the status quo is always simpler. But, those who are truly committed to growth and the future of their organizations aren’t content with the easy way out.

By integrating proper change management in the deployment process, companies and departments will be able to get employees on board and involved in the process to ensure as smooth a transition as possible. There will be headaches, and you may be uncomfortable. But that’s how change management works. If it were easy, everyone would be successful.

How to plan for transition

To help plan for the transition process, Harvard Business Review discusses the hard factors that need to be discussed more (along with soft factors like culture, leadership and motivation) when implementing change management strategies. These factors allow companies to measure, communicate and influence elements quickly to affect transformation. Before they start, companies need to understand the time allotted to complete the change, the number of people required to execute it, and the financial results that intended actions are expected to achieve. 

To help lead a successful change management operation, there are four specific factors companies can use to determine the outcome and create a path to success:

Duration – The length of time it will take until the change program is complete, and the length of time between reviews built to measure success

Integrity – The ability to select the best staff to lead the program. Look for problem solving skills, results & methodological oriented individuals

Commitment – The level of enthusiasm and resilence  from both management and employees to affect this change

Effort – Calculate the amount of time and effort beyond existing responsibilities, resources that are over stretched may compromise the change program or normal operations.

For future transitions

Change management requires focus, organization, and motivation. Not everyone will be willing to accept and help to invoke this change at the same time. The source of resistance is often individuals or groups, but it can also be systems or processes that are outdated or that fail to fit current business conditions.

Ways to mitigate these obstacles include rewarding flexibility, creating role models for change and repeating the key messages and goals of the project throughout the entire change program.

This is where the message of the “bigger picture” becomes crucial, if employees feel separated from the goals they will question their motivations. But by showing the concrete benefits of change for them, their department, and the organization more largely, you can demonstrate how all this added effort will lead to gains in the future.

For more on creating an effective transition strategy, watch our webinar, Change management 101: Strategies for leading change when adopting AI.

For more articles and resources like this one, visit our blog.

Ready to embrace AI to strengthen your remote audit?

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

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

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

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.

business value forecast by AI type | Graph
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

internal audit advisory

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.

internal audit limitations
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.

future of internal audit profession

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 the new SAS-142 audit evidence standard embraces technology and automation

Person moving to the future of audit

A new audit evidence standard has been released by the American Institute of Certified Public Accountants (AICPA) that includes significant updates around how technology and automation can be leveraged throughout the audit process. Here, we’ll examine this standard and some of the most significant examples of how the AICPA has explicitly considered the applicability of analytics and automation to how audit evidence is gathered and concluded upon.

The Statement on Auditing Standards (SAS) No. 142 Audit Evidence is relevant for private company audits and takes effect for periods ending on or after December 15, 2022.

While the effective date of the guidance allows for lead time for the appropriate methodology changes and technology investment to be contemplated and implemented by firms ahead of calendar 2022 audits, the updates reflect the massive tailwinds of how data analytics and automated tools and techniques are well-positioned as catalysts for the reimagining of the audit life cycle. Furthermore, the potential afforded by these technologies to drive monumental improvements in both quality and effectiveness is only amplified further in today’s remote work environment.

 

Key concepts around audit evidence

It’s worth revisiting some of the basic principles around audit evidence and the responsibilities of the auditor before discussing how data analytics and automation can be transformative to how evidence is collected and generated.

The new standard clearly defines the auditor’s objective around audit evidence as follows:

“The objective of the auditor is to evaluate information to be used as audit evidence, including the results of audit procedures, to inform the auditor’s overall conclusion about whether sufficient appropriate audit evidence has been obtained.” (SAS No. 142, par 5)

The term audit evidence may conjure up images of stacks of source documents (invoices, purchase orders, cheque stubs, etc.) and detailed documentation of ticking and tying them all together in an Excel spreadsheet. But audit evidence isn’t just the outcome of detailed transaction-level testing, it’s more broad and includes the results of your risk assessment procedures and inquiry, any testing of controls, and the results of both detailed and analytical-based substantive testing (SAS-142, par A44).

In other words, the auditor, in exercising their professional judgement as to whether identified risks are properly responded to, has a wide net of support to consider on balance and weighed together to make that conclusion effectively.

So what type of things influence whether evidence is sufficient and appropriate? This comes down to how much evidence is required to respond to the identified risks of material misstatement, and how relevant and reliable that evidence is. The appendix to the standard specifically includes a number of examples and contemplation of what these key terms mean in practice and some of our takeaways (not exhaustive) include:

 

  • What types of factors impact the reliability of audit evidence?
    • Source
      • Is the information from an external source, and therefore less susceptible to management bias (SAS-142, par A22)?
    • Nature
      • Is the evidence “documentary” vs. provided orally through inquiry?
    • The controls over the information and how it’s produced
      • How automated is the process by which data is generated and what is the relative strength of controls that the entity has in place? How is the accuracy and completeness of the information ensured?
    • Authenticity
      • Has a specialist been involved in validating certain assumptions?
  • What types of factors impact the relevance of audit evidence?
    • The accounts and assertions it relates to
      • Does the evidence tie directly to identified risks at the assertion level of an account? For example, purchase documents matched to payable transactions right before balance sheet date provides evidence against an early-cutoff risk but not a late-cutoff risk.
    • The time period it pertains to
      • Does the evidence relate to the period under audit or specific subsets of that period where risk is relevant?
    • Susceptibility to bias
      • How much influence over the information does management have?

 

These concepts are critical to keep top of mind as we consider the role of data analytics and automation because introducing technology to the audit process doesn’t diminish the auditor’s overall objective and requirement to obtain sufficient and appropriate evidence to support their opinion. Rather, the tests and techniques that we’ll review enable the auditor to more efficiently gather, interpret, and perhaps even generate the evidence that satisfy these criteria.

 

Facilitating high-quality and data-rich analytical procedures and risk assessment

Let’s consider the following excerpt from the new standard:

A59. Analytical procedures consist of evaluations of financial information through analysis of plausible relationships among both financial and nonfinancial data. Analytical procedures also encompass investigation as necessary of identified fluctuations or relationships that are inconsistent with other relevant information or that differ from expected values by a significant amount. Audit data analytics are techniques that the auditor may use to perform risk assessment procedures… 

A60. Use of audit data analytics may enable auditors to identify areas that might represent specific risks relevant to the audit, including the existence of unusual transactions and events, and amounts, ratios, and trends that warrant investigation. An analytical procedure performed using audit data analytics may be used to produce a visualization of transactional detail to assist the auditor in performing risk assessment procedures….

Automated techniques such as the ones described in the guidance can be a very powerful and efficient method to assess relationships across the financial ledger. Having this type of analysis “out-of-the-box” at your fingertips, without detailed scripting or manual data wrangling, promotes efficiencies as well.

Here are a few examples of how the capabilities of MindBridge Ai Auditor align with a technology and data-driven analytical review and risk assessment that the standard explains.

Trend analysis

Our Trending analysis allows you to visually compare how one or more accounts moves over time. This allows you to assess how accounts or financial statement areas that you expect to be correlated (accounts receivable and revenue, revenue and costs of sales, etc.) are indeed tracking consistently. It’s important to note that this analysis is available on a monthly basis and is not just a simple year-over-year comparison. This empowers you to have a more nuanced view of what these relationships look like seasonally and more broadly.

Graph displaying trend of accounts, showing ending balance

You are also able to layer in filtering of the trends you are seeing, across additional operational dimensions of the financial ledger. For example, if an organization manages it’s P&L by department or region, you can examine how revenue breaks down across one or more of these dimensions with one click.

Graph displaying trends of accounts showing activity

Ratios

Over 30 critical ratios are automatically calculated by Ai Auditor and the results are visualized on a monthly basis throughout the audit period. How each ratio trends in the current period against prior periods is readily apparent and points of deviation can be flagged for further investigation with your client.

With an appropriate amount of prior period data available, Ai Auditor performs a regression analysis called seasonal autoregressive integrated moving average (SARIMA) to graphically visualize the expected ranges for the ratio in the current period in addition to the trend lines. This is extremely valuable in identifying  algorithmic outliers for further audit procedures and input to risk assessment.

Gross profit ratio expected range

 

Transaction-level analysis

The new standard specifically contemplates how unusual transactions or events in the financial ledger impact risk assessment and this aligns perfectly with Ai Auditor’s core competency of an ensemble-based AI algorithm that runs against every transaction and tags it with a single risk score:

A61. Analytical procedures involve the auditor’s exercise of professional judgment and may be performed manually or by using automated tools and techniques. For example, the auditor may manually scan data to identify significant or unusual items to test, which may include the identification of unusual individual items within account balances or other data through the reading or analysis of entries in transaction listings, subsidiary ledgers, general ledger control accounts, adjusting entries, suspense accounts, reconciliations, and other detailed reports for indications of misstatements that have occurred. The auditor also might use automated tools and techniques to scan an entire population of transactions and identify those transactions meeting the auditor’s criteria for a transaction being unusual…

In Ai Auditor, the ensemble-based algorithm includes over 30 different tests, termed Control Points, which range across rules-based, statistical methods, and machine learning-based techniques. The ensemble specifically includes tests for Unusual Amounts posted to an account, Rare Flows of money between accounts that don’t normally interact, and Outlier Anomalies.

With Ai Auditor, you can visualize the results of these tests in aggregate via dashboarding and drill down to the most granular level of a particular entry to see which Control Points are contributing to a certain score.

Transaction risk levels over time

Control Points displaying risk from high to low transactions

Techniques that facilitate highly efficient “dual-purpose” procedures

The new standard includes an illustrative example where a series of audit data analytical techniques are used as both a risk assessment procedure and a substantive procedure:

A46. An auditor may use automated tools and techniques to perform both a risk assessment procedure and a substantive procedure concurrently. As illustrated by the concepts in exhibit A, a properly designed audit data analytic may be used to perform risk assessment procedures and may also provide sufficient appropriate audit evidence to address a risk of material misstatement.

The exhibit being referred to in the passage above is quite compelling and certainly worth a detailed review (beginning at page 42 here). As an extension of the previous discussion around transaction-level risk scoring, assuming that additional considerations are satisfied, such as the effectiveness of controls over how the information is produced and the auditor’s confidence as to the accuracy and completeness of the information, the ability to “profile” transactions into relative risk buckets using an audit data analytic (ADA) routine is explicitly contemplated here.

If the results of that “profiling” can be used to not only to inform risk but also the nature, timing, and extent of further substantive audit procedures, the investment into building and integrating these types of techniques into your methodology could provide significant ROI in terms of execution efficiencies.

Take the first step towards a modern, data-driven technological approach to audit, contact sales@mindbridge.ai.

Assessing audit risk during engagements

Man using MindBridge to access financial risk

Three ways Ai Auditor strengthens your audit planning

The determination of where audit risks of material misstatement lie is a critical output of the audit planning process. Usually, identifying those risks is based on the auditors understanding of their client and the client’s operating environment. Auditors can now rely on a data-driven approach to better understand that environment. And this will positively impact the nature, timing, and extent of the audit procedures which respond to the identified risks.

Below, we’re exploring three ways that our Ai Auditor solution helps you streamline your audit planning, from start to finish.

How to enhance your audit planning using Ai Auditor: 

1. Conduct thorough assessments for better audit planning

Just looking at a balance sheet or income statement at one point in time isn’t enough. Analyzing more financial data during the planning phase allows for a deeper understanding of the client’s operations.

Auditors have long used analytics to help assess a client’s operations. These tools help them gain insights and identify aspects of the entity that were either unknown or unfamiliar to the auditors. These data analytics essentially help them to better assess the risk of material misstatement, as well as provide a basis for designing and implementing responses to the assessed risk.

Working with Ai Auditor, the auditor can select a view of the ending balance or monthly activity. They can also analyze different transactional relationships within the general ledger to ask better questions and make more precise judgement calls.

For example, let’s say an auditor finds out the accounts receivable (AR) has a 10% change from the prior year to this year. The auditor can explore the AR activity and find out if this change was a normal increase or if there was any unusual activity that could indicate a new large customer or purchase at year end.

Another example would be if there was an account that had no significant change from the prior year ending numbers, but the activity was much different. Having more data would provide the auditor with better insight into the client’s operations.

Using Ai Auditor, an audit team can also look at relationships between accounts to identify if there are any unusual patterns. For example, perhaps they’ll notice that the cost of goods sold (COGS) and inventory trends appear to not follow consistent patterns. The auditor can then include a very specific and strategic task in the audit plan — to pinpoint the time when the trend does not follow expectations and investigate further.

2. Quickly identify unusual transactions across all data

The MindBridge Ai auditor solution automatically scores the risk of each transaction using Control Points. These Control Points include tests based on business rules, statistical models, and machine learning to identify the most uncommon and unusual items in the data set.

The machine learning engine in Ai Auditor looks at each unique data set and analyzes the frequency and amounts of the transaction. The engine also explores relationships between the account’s transactions that are being recorded.

Ai Auditor helps automate the analysis by flagging items that just don’t fit typical transaction patterns. It’s then up to the accountant to focus on the most uncommon and unusual items and dig deeper.

For example, Ai Auditor might flag the write-off of inventory because insurance-related payments seem uncommon or unusual. During the audit, the auditor might learn that this was due to a warehouse fire.

Essentially, the platform gives auditors better visibility on these unique circumstances right from the start of the audit. The auditor can then focus on these higher risk transactions, consider the ramifications of the transactions, and understand how those riskier items might impact the financial statements.

 
reasonable assurance definition

 

3. Retrieve and view transactional breakdown by audit area

Using Ai Auditor, an accountant can filter risks by category. This allows them to breakdown risk by account, branch, program, type of transaction, time, monetary value and more.

With this breakdown, the auditor will gain a better understanding of where relative risk lies across operations. They will also be able to see which control points are being triggered within a specific area and consider how that impacts the overall audit risk.

For example, let’s assume an auditor notices that the accounts payable (AP) entries are triggering a significant amount of risky transactions at year end, specifically in the Southwest branch of the operations. This might indicate cutoff issues. Or, if the sequence gap control point is triggered, perhaps the auditor will assume there are completeness issues.

During audit planning, auditors who think critically about how these control points might factor into the assertions for the various accounts will drive stronger results.

 
internal auditing techniques

 

A stronger audit plan leads to stronger audits

A deeper level of critical thinking in the audit planning stage ensures a more efficient and effective audit. Auditors can leverage our MindBridge Ai Auditor solution as a feedback loop to further their understanding of the client’s operations.

Using the AI auditing platform, accountants can then uncover valuable insights to supplement their discussions with management and existing knowledge of the client. Those insights might include uncommon patterns in transactions, abnormal stratifications, unusual relationships between accounts, and breakdowns of trends or ratios. With this information at hand, auditors can ensure a well-planned and successful audit.

Do you think audit analytics make auditors even more relevant? We do. Read this next blog to find out more.

How AI and data can power an effective audit plan

Moving squares versus circles

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

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. 

internal audit tools

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.

internal audit results

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.

internal audit and governance

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.

reasonable assurance audit

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.

corporate internal auditor

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

Stopping dominoes with foresight

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.”