Ai Auditor Archives - MindBridge

Auditors have always carried the weight of trust—tasked with upholding financial integrity in an increasingly complex world. That responsibility hasn’t changed. But the environment auditors operate in? That’s evolving fast. Today’s audits don’t just face more scrutiny—they face a new scale of risk. Transactions happen in milliseconds. Data volumes have exploded. Fraud and errors have...

A financial audit is one of the most complex and challenging tasks in modern finance. Audit risk—the risk that an auditor fails to spot mistakes in financial statements—is a problem any auditor, no matter how experienced, can face. Audit risk detection is crucial for businesses as it helps identify inaccuracies and potential fraud in financial...

Financial professionals are inundated with vast amounts of data, making it increasingly difficult for teams to sift through information and identify areas of concern. Our users want a solution that doesn’t just show them the data, they want to be told exactly where risks are lurking and how to act on them. Traditional methods, even...

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KPMG and MindBridge form global alliance, transforming digital audits with enhanced risk detection and next-gen AI audit technology.

ISA 315 (revised) and Data Analytics: Risk assessment procedures reimagined

The revised standard has been published as of December 2020, and you might be wondering what impact it has on your firm’s risk assessment procedures and how you can address the requirements. There are many useful sources of information on the changes, notably the IAASB’s Introduction to ISA 315. IFAC also published a helpful flowchart for ISA 315 during the work programme, which walks through the various steps required to assess risk of material misstatement.

There are a number of improvements to the standard, including an enhanced focus on controls (particularly IT controls), stronger requirements on exercising professional scepticism and documentation, and considerations around the use of data analytics for risk assessment. The new standard comes into effect from 15th December 2021, so now is the time to start planning how you will address the changes in your audit. Below we discuss some key considerations on how analytics can support a strong risk assessment.

Credit: https://www.ifac.org/system/files/publications/files/IAASB-Introduction-to-ISA-315.pdf

So how can data analytics support your risk assessment according to ISA 315? The areas identified above in red show the different procedures that can be supported by the use of these techniques. A key element of the revised standard is that this should be an iterative process conducted throughout the audit. This means using data analytics tools that can be easily refreshed with the latest information will better support this requirement than more traditional approaches.

Identifying risks of material misstatement at the financial statement level

Data analytics can support the risk assessment procedures laid out in ISA 315 by analysing previous and current accounting data to the financial statement level. This allows the auditor to see the material balances in the accounts, and if machine learning is applied, where the concentration of risky transactions lies. This is where the knowledge gained in the blue boxes above can be brought to bear. Comparing understanding gained through observation to the data is a powerful way to sense check and identify areas for further investigation.

Identifying risks of material misstatement at the assertion level

Specific analyses can target assertion risks and show where there are particular problems with an assertion. To do so effectively, several different analytics tests can be applied and combined to develop a good indicator of an assertion risk, for example accuracy. These can then be applied in an automatic way to give the auditor the information needed for their risk assessment.

Determine significant classes of transactions, account balances or disclosures (COTABD)

Combining assertion analytics with the ability to profile similar transactions can help auditors identify significant classes of transactions or balances. Analytics can help to produce similarity scores, but also to identify sets of transactions that are unusual. This can indicate previously unknown business processes that may require a separate assessment of their control environment.

Assess inherent risk by assessing likelihood and magnitude

Following identification of risk, the audit can guide their assessment by understanding the level of unusualness. Data analytics can provide finer grain evaluations of risk rather than simply risky or not. This can help support assessments aligned with the spectrum of inherent risk as defined in the standard.

Assess control risk

Data analytics such as process mining or automated testing of segregation of duties can help to inform or test control risk. These analytics can provide more comfort around the controls risk assessment and help to identify deviations in the control environment that require further examination.

Material but not significant COTABD

Where COTABD has been determined as material but not significant, recurring analytics can ensure that this assessment remains valid. Anomaly detection methods can be particularly helpful here, allowing the auditor to regularly check that nothing unusual has occurred since the initial assessment was undertaken.

Next Steps: ISA 315 and Data Analytics

Audit methodologies will need to reflect the revised workflow, with particular emphasis on the iterative nature of the risk assessment and ensuring that auditors are prompted to exercise professional scepticism and document it at every stage. Data analytics can help to ensure that the information used to continuously conduct risk assessment is timely, appropriate and relevant.

These improvements to the standard will result in a stronger audit approach and an advancement towards industry adaption data and analytics technologies. With AI audit software, accountants and auditors can gain deeper insights into their client’s financial data, in less time. Overall, the audit software can increase the efficiency of their processes, so they can focus on delivering better results, in time for the ISA 315 (revised) December 15th, 2021 deadline.

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.

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.

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.

Our latest release of MindBridge Ai Auditor introduces brand-new support for subledgers, enhanced ingestion workflow, an upgraded user interface, and more. Read on to find out what you can accomplish in this release!

New user experience

We’re proud to unveil a new, modern interface for Ai Auditor that has a stylish look and feel and brings enhanced accessibility with a WCAG 2.0-compliant color palette. To make navigation and access to functionality as easy as possible, we’ve updated the user interface (UI) to a new sidebar that offers a cleaner look and exposes controls that are in context and on demand.

This sidebar can be opened and closed as you need it, and updates dynamically as you work with various capabilities within Ai Auditor. The same space on the screen is used differently depending on the context, making the overall layout cleaner and relevant to what you’re doing.

Subledger support for accounts payable and accounts receivable

The analysis of accounts payable (AP) and accounts receivable (AR) to date has been focused on datasets containing two-sided transactions to determine transaction flows between accounts. This release of Ai Auditor adds the ability to ingest and analyze single-sided subledger information to calculate risk at the financial line-item level.

This enhances your ability to assess risk using data-driven insights and provides trends for the vendor and customer balances to identify significant changes.

Risk overview dashboard

This dashboard contains the key metrics to provide an overview of the risk areas. You can immediately identify notable entries and focus attention on those that pose a heightened risk of misstatement or error.

Ai Auditor uses a combination of rules-based, statistical, and artificial intelligence Control Points to analyze entries in sub-ledgers and put them into buckets: high risk, medium risk, and low risk.

To provide detailed analysis on subledger-specific anomalies and risks, we’ve added new Control Points such as old unpaid invoices and unusual amounts by vendor and customer. We’ve also tweaked some existing Control Points to support single-sided AP and AR subledgers.

Trending dashboard

This dashboard contains a summary view of the top outstanding vendors (AP) and customers (AR), including a comparison of all balances to previous periods to determine any significant or unusual changes. You can filter the page by ending balance or total activity.

In the table below, you can see which customers or vendors require attention by looking at the balances.

Vendors or customers are ranked in order of the current year’s ending balance. Vendors or customers that didn’t exist in previous years will be marked as new.

You can see the change in dollar value between the prior and current year’s ending balance in the variance column and % change and % of total balance. To see what entries make up your current balance with each vendor or customer, you can drill down on the entry in the table to land on the Data table page which has the entries detail.

As illustrated below, you can gain a full understanding of the period-over-period changes by exploring vendors or customers on the accounts receivable side by month, quarter, and period. You can compare multiple vendor or customer balances by selecting multiple vendors/customers from the dropdown list.

You can also zoom into time periods of interest using the slider.

Aging dashboards

These dashboards contain metrics that help you to view each vendor/customer, the amount they are owed, and the amount of time they have been owed.

Based on these dashboards, you are able to:

1. Recreate the aged AP or AR reports to compare to reports provided by the client.
2. Understand the historical distribution of AP or AR balances to identify anomalies.
3. In the AP analysis, identify old invoices outstanding to understand potential cash flow issues or misstatements in vendor balances.
4. In the AR analysis, identify outstanding invoices to determine appropriateness of allowance for doubtful accounts.

The Aging graph allows you to view the amount of money owed to each vendor/customer, and the amount of time that money has been owed. You can view either the total vendor/customer balances or the balances of specific ones. Time can be displayed by month, quarter, or period.

The Aging table allows you to find out the details on each vendor or customer and whether they were excluded from the aging visualization due to missing data.

Data table

You can use the data table and Filter Builder to select the population of entries in the subledger to meet desired criteria and build tests.

Reports

Ai Auditor generates vendor and customer aging detail summaries that list individual invoices, credit notes, and over-payments owed for each vendor or customer, and how long these have gone.

Enhanced ingestion workflow

As our navigation updates continue to improve the experience, this release also brings enhancements to the data ingestion workflow.

The updated Data page (shown below) guides you through each step of the ingestion process. Since uploading different datasets unlocks different analytical capabilities, we list the features along the right side of the page. Clicking a feature will reveal the required dataset(s) for each.

Customers can learn more about this release in our online documentation, or you can book a personalized demo here.

Our latest release of MindBridge Ai Auditor is another leap forward, with plenty of great features to help auditors throughout the audit process. Keep reading to find lots to love about this release.

Task creation and sample selection

Auditors can select samples, either manually or by using our Intelligent Sampler, to stratify the population of financial data by risk rating. Creating tasks in Ai Auditor allows your team members to work collaboratively to investigate anomalies in your client’s data.

In this release, you can create tasks and leverage the Intelligent Sampler on individual line entries. Prior to this release, you could only use the feature on transactions.

You can create filters and select samples manually by creating tasks on individual line entries which will be added to your audit plan. You can also leverage the Intelligent Sampler to stratify sample selection on entry view as described in the next section.

When you go to the Data Table tab, you can click on a transaction and see the individual line entries associated with a transaction. You can switch to the entries view by clicking on the Transactions button on the top right corner of the page. You can also bulk select by checking the box on an individual line item and select the appropriate action item from the Actions drop-down. Once you create a task on an entry, it will be added to your audit plan page.

Intelligent Sampler

Auditors can select samples, using Intelligent Sampler, to stratify the population of financial data by risk rating. In this release, we have improved the Intelligent sampler.

We have added a random sampling option to the sampling method to create a truly random sample from the population generated in the applied filter. Each item in the population has an equal chance of being selected. The default sampling method is risk-rated that uses the entry or transaction risk score to stratify the population generated in the applied filter.

Audit plan

The audit plan is one of our many exports that provide sufficient and appropriate audit evidence to support the audit opinion. In particular, the audit plan provides a summary of items selected using Ai Auditor while preparing to perform a test of details.

On the Audit Plan page, we now provide more filters, allowing you to filter by audit area to understand the areas you have looked into and what management assertions you have selected samples for.

Large transaction support

Transactions that contain a large number of entries can be present in financial data for a number of different reasons.

These types of transactions can limit an auditor’s ability to assess the true transaction risk or identify problematic entries. Ai Auditor now allows the user to apply an operation to the file, the Smart Splitter, to decompose each large transaction down to its matching entries, where each entry pair is identified by matching offsetting debits and credits.

Create engagement

We improved the engagement creation page and added planning Date and Final Analysis Date. The Planning Date field is used to approximate the period available for interim work. This date is the earliest date for which you intend on having data from your client to use in planning.

Enhancements to Libraries

In the May release of Ai Auditor, we introduced the Library feature to provide further flexibility to administrators to tailor the work done based on the industry that your client operates in.

Libraries contain all the business logic needed to perform analysis within a particular industry or market and allow you to customize an analysis based on industry types with different ratios, filters, and Control Points.

Conclusion

This release is driven by our philosophy that it’s important for auditors and accountants to both customize and automate what they are doing. By giving you the proper tools to create and organize, we’re setting the foundations for even more powerful features in the future!