September 2020

What to expect from audit software in 2021 to 2022

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

An outlook on audit software trends in 2021-2022

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

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

5 audit software trends to keep an eye on:

1. Increased demands for automating routine tasks

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

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

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

2. Growing adoption of cloud-based solutions

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

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

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

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

3. Tackling big data in accounting

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

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

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

4. Greater need for risk assessment and fraud prevention

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

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

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

5. Opportunity for growing advisory services

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

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

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

It’s time to capitalize on AI audit technology

While we can’t predict the future, we do know this— AI audit software will continue to help accountants and auditors gain deeper insights into their client’s financial data, in less time. Overall, the audit software can increase the efficiency of their processes, so they can focus on delivering better results. Those who are forward-thinking and ready to embrace artificial intelligence and audit technology will reap great benefits today, and tomorrow.

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

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

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

May 26, 2022September 20, 2020 by Stefano Colaiacovo, CPA, CA

abstract lines up showing ar-ap procedures

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

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

What does the MindBridge platform do?

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

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

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

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

Trends and patterns

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

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

Key performance indicators

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

Aging

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

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

How MindBridge streamlines detailed testing of AR & AP subledger data

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

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

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

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

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

To learn more, contact sales@mindbridge.ai.

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

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.