internal audit Archives

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?

Accounting software trends have impacted the accounting profession in big ways. And in my view, one of the greatest analogies of this impact, and even of the way our team at MindBridge delivers value to our clients, comes from Sam Daish, Head of AI and Data Science at Qrious.

A story of three types of businesses

In his previous role as General Manager of Data Innovation at Xero, Sam addressed a room full of very traditional big-firm accounting partners. During this talk, he described the evolution of manufacturing in the time when electricity was new. He summarized the journeys of three business types:

Those who thought electricity was some strange wizardry and continued on as they always had
Those who tried to adapt their processes around electricity to make things work
Brand new businesses that sprung up native to electricity

Sam continued to tell the story of how manufacturing evolved in the 1880s. Businesses in the first category simply could not compete. They buried their heads in the sand. Their refusal to adapt was largely due to long-held pride in traditional expertise. The second group worked really hard to re-invent efficient processes—to make electricity bend and work around the way they’d always done things. The third set of businesses built operations with electricity at the heart. What happened to them?

The ‘ostriches’ were completely obliterated by the rest of the market
The ‘adapters’ really tried, but many businesses did not survive
The ‘electricity natives’ absolutely consumed the market. They shifted customer expectations and quickly devoured customer relationships that were long-held by large, big-brand traditional businesses that once dominated the industry

The parallels with the accounting industry’s state of flux surrounding technology adoption are profound.

First comes cloud accounting software, then AI accounting software

At one point, there was so much fear, worry, and apprehension about cloud accounting software. Many believed the accounting software would steal jobs from bookkeepers, graduates, and accountants in general. Yet the only ones who have experienced any negative outcome have been those who failed to adopt and adapt. Accounting firms who have embraced cloud accounting software and the client-centricity of the single ledger, and who have assisted their clients in doing the same, are dominating the market. It is not accounting technology replacing accountants – it’s accountants adopting technology that are replacing those accountants who are not.

So what about AI now?

Most would agree that diversification into advisory services is the key to modernizing accounting firms and aligning with client expectations. During Covid-19 times, we have seen a reversion back to the bread-and-butter of compliance for many accountants. What we will see moving forward is the evolution of compliance; it will feel less like putting numbers in a box and filling out forms (as this becomes more and more automated over time) and more like compliance risk mitigation, or ‘compliance advisory’. So for the future-fit compliance and advisory firm, AI accounting software comes to the fore when we ask ourselves: “So you have access to all this real-time data via cloud—what are you doing with it?”

When we look at accounting software trends, the message to support the adoption of AI is like that of cloud: “AI—it’s about task replacement, not human replacement”. The automation and ‘task replacement’ we now enjoy with cloud accounting software is similar to AI accounting software—these technologies are just doing parts of the job which no one likes anyway. For example, we love presenting insights to clients, showcasing our deep expertise of industry, and offering fancy visualizations that break down the complex into a simple picture. But we don’t like entering or churning through the data to get to the insights. So for this, we have AI. In a recent Accounting Today article titled ‘What AI does for accountants’, the author describes three areas in which accountants can leverage AI accounting technology right now:

Invisible accounting to automate reconciliations for clean, timely data
Active insight to drive better decisions
Continuous audit to build trust through better financial protection and control

Stepping towards success with AI

No matter where accounting firms are in their journey towards adopting new accounting software, one thing is clear—businesses need to, at the very least, start looking at the latest advancements in AI and all the advantages it offers, or risk being left behind. Some may be just jumping onto the cloud accounting software train. Others may begin courageously diving into AI. Regardless, there is a necessity for our established industry of accounting professionals to be deliberate about their re-learning journeys when it comes to accounting software. Those who seek to not only survive, but thrive, must ensure that data literacy and conceptual knowledge of what both cloud and AI accounting software can deliver are key to their business strategy moving forward.

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