Ai Auditor Archives

AI for government finance: Understanding value & barriers

May 26, 2022January 23, 2020 by Andrew Popp

Klaus Schwab, the Founder and Chairman of the World Economic Forum, shares in his book, “The Fourth Industrial Revolution,” that artificial intelligence (AI) will perform 30% of corporate audits by 2025. While any estimate of change is just that, an estimate, the pace of change is governed by the benefits that result from the application of any given technology, weighed against the forces opposing it.

In the case of government finance and accounting, AI is being embraced at an astounding rate, and may even accelerate if we are able to overcome some of the forces opposing the change.

The benefits of AI to government are being proven out in its early usage today, namely employee efficiency, risk mitigation, and operational insights. These three value propositions are driving the rapid adoption of AI as a financial control, an audit tool, and a forecasting function, and will help ensure that governments at all levels are better managing the public’s finances.

What AI brings to government finance and accounting

Take the case of a large Canadian federal government department. Financial analysts, by policy, are asked to manually review every travel expense that exceeds $1000 CDN. This is the type of task that is ripe for automation via AI, as AI can rapidly analyze all the expenses at once and determine those that are the riskiest to review. AI allows the department to be more efficient, as it can prioritize its resources to review the riskiest expenses, the ones most likely to contain an error, omission, or a violation of the expense rules, and automatically approve the vanilla claims.

While operational efficiency has the greatest monetary value to government organizations, the value of risk mitigation centers around the trust placed in government financial operations. Whether a financial error, omission, or fraud is found to be above or below the material threshold of the organization, the impact on the public’s perception of the competence of its management and staff is always put into question.

With financial data growing at an exponential rate, (PwC estimated that 18 Zettabytes of financial services information was created worldwide in 2018), current audit and control techniques, including random sampling, are constantly failing to detect mistakes and fraud. AI provides a means of reviewing 100% of the data, allowing governments to find risky transactions and the associated parties, before it hits the press.

The operational insights provided by AI offer value to both the controller and the budget analyst. Controllers can use the risk ranking of transactions in a given year and visualize that risk against past years to spot areas of weakness in the control systems. Budget analysts can customize and visualize key performance indicators for the organization and use multiple years of data to predict how those ratios should evolve, and how they are tracking against them in any given quarter. Exceptions are highlighted so that action can be taken to apply additional budget or distribute resources to meet shortfalls.

Overcoming the human barriers to successful AI deployment

While these value propositions are helping speed the deployment of AI in government finance and audit, there are a number of human-centered forces that are putting a brake on wider adoption. Trust and transparency in the deployment of AI is one force against its adoption. How organizations change their processes to integrate AI is another. Lastly, the development of employee skills will ultimately predict the speed at which AI is adopted in organizations.

The Canadian government has proven itself a world leader in its adoption of the Algorithmic Impact Assessment (AIA) as a means of mandating transparency in the AI algorithms and how they are applied in any automated government service deployment. This move lays the basis for government departments to take advantage of AI automation that is explainable to the public, ensuring AI use can grow with appropriate oversight, and allowing trust to develop as a matter of process and not accident. Other countries have taken note of what Canada has done and are either adopting the Canadian AIA or are creating their own similar framework.

While building trust, it is also critical that government processes adapt to integrate AI. In the case of applying AI to reviewing $1000 expenses above, the policy governing the expense review process will have to be adapted to capture AI’s role. Policy, as we all know, doesn’t change overnight. The appropriate groups have to gather and review policy changes in the face of AI. There is also the issue of global and national regulatory standards that govern finance and accounting, particularly how and where AI-driven analysis can play a role. These conversations for change have already started, with the first major AI-driven changes to the audit standards process starting in 2020.

Skills are a huge part of any technology change. Just as blacksmiths evolved into being mechanics with the advent of the motor vehicle in the 1900s, financial officers and accountants are going to evolve into data analytics experts in the world of AI. One critical skill set is going to include the appreciation of numerical algorithms and analytical techniques and how they apply to the financial situation they are assessing. This doesn’t mean they have to become an algorithmic expert, or know how an algorithm is coded, any more than a mechanic needs to know how a motor vehicle is built. However, they need to know when their vehicle is good for driving on a paved road, and when it’s good for going off-road.

Data is the fuel of the future, and algorithms are the engines that will consume it.

It’s not a question of “if” AI will transform government finance and accounting, but “when”. With a strong set of value propositions driving the change, and the barriers of trust, adoption, and skills being diminished with increased awareness, leadership, and training, AI will be well enshrined in government before Schwab’s predicted date of 2025.

For a deeper dive into how AI helps government audits and financial management, watch my on-demand webinar now.

To learn more about our government solutions, including real use cases, visit our government finance page.

Getting ready for AI-powered audit in 2020

December 18, 2020January 14, 2020 by Roy Sarkar

You’re in the minority if you haven’t heard of artificial intelligence (AI). Yet the accounting profession has a long way to go in terms of adoption. AI is a popular conversation piece for industry bodies such as the AICPA, CPA Australia, ICAEW, and PCAOB, and more firms are deploying the technology today than ever before. But most firm leaders still struggle to understand the impacts of AI on their staff, processes, and clients.

What are the implications of AI for your audit practice in 2020?

We’ll break down the answer for two types of firms: Those that are thinking about adopting AI this year and those that are already using MindBridge Ai Auditor.

Thinking about adopting AI in 2020

Based on interviews with our clients, firms consider making the shift towards AI for the following reasons:

We’ve heard about the value of AI from others
We hope AI will create new opportunities to attract and grow clients
We don’t want to be left behind

Firms are less clear on how AI transforms their client engagement process and may not understand that it’s about the people as much as the technology. Firms that are thinking about making the shift to AI need to:

Raise their awareness and understanding of AI for audit
Align their strategic goals on providing more value to clients through AI
Build up their data skills and capacity to get the most out of AI

In other words, as AI and machine learning can extract anomalies in client data (i.e., potentially risky transactions in the general ledger and subledgers) that were previously unheard of, auditors need to build up their data analytics skills and consider new ways of working with clients. With AI, the focus is more on risk-based analysis and audit planning than traditional rules and statistical sampling.

This means that more data leads to more effective results. It’s wise to think about exporting samples of client financial data as early as possible. The level of detail that can be analyzed with AI is likely beyond what was included in your previous PBC requests and it may take your client a few iterations to get the exports required. We recommend getting the sample exports in advance of your fieldwork so your engagement teams can run an interim AI analysis and provide immediate value to clients as fieldwork begins. The up-front information gathered here will be useful throughout the engagement.

It’s also prudent to set realistic expectations for your firm and engagement teams if you’re starting your AI journey during busy season. Focus your first few engagements on clients that are using common ERP systems, such as QuickBooks or Dynamics, to minimize time spent on generating data exports. This enables your engagement team to spend more time interpreting and understanding the AI analysis results and delivering value to your client with AI-expanded insights.

Using AI for audit now

To best prepare for the upcoming busy season using MindBridge Ai Auditor, it’s important to consider these three actions:

Prepare your client and their data. When obtaining client data, know what you need, why you need it, and understand that more data is better. To help you prepare, our knowledge base has an overview of client data requirements, data checklists, and ERP export guides. Remember that the earlier you can get data, the better. Even if year-end data isn’t available, you can load previous year, interim data, and complete accounting mapping ahead of time.

Perform risk assessment and planning. We recommend the following steps:

Once client data is loaded, prepare the audit plan, create the necessary tests, and save them all using the Filter Builder.
Performing a risk assessment of your client’s data will identify the areas to test and using the dynamic audit plan will help assign tasks and facilitate testing procedures during fieldwork.
Reviewing the analytics, ratios, and graphs with current and past data will call out any items that need to be addressed during the audit.
Leverage the trending reports and ratios to enhance your working papers and provide additional value back to your client.

Engage our customer success team as early as possible. When interacting with your Customer Success Manager (CSM), it’s important to set clear timing expectations, including fieldwork dates. Your CSM acts like another member of your engagement team: Your busy season is their busy season. Setting them up for success early helps them be more efficient and effective in treating requests.

Need help? At any time, you can check out our knowledge base or join a live chat with a CSM using MindBridge Assist.

Remember that AI is as much about the people as it is the technology. Whether it’s your own staff, your client, or by working with our CSMs, the successful delivery of AI-based value depends entirely on putting the human at the center of the audit.

As MindBridge founder Solon Angel states:

“The purpose of AI or any new technology is to save time, headaches, and unnecessary effort on humans. Be mindful to invest these savings on your well being as the menial work becomes less burdensome—having a healthy body and mental state allows you to think with higher quality.”

Learn more about MindBridge Ai Auditor here.

New Ai Auditor release: December 2019

May 26, 2022November 18, 2019 by Azalia Shamsaei

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:

Recreate the aged AP or AR reports to compare to reports provided by the client.
Understand the historical distribution of AP or AR balances to identify anomalies.
In the AP analysis, identify old invoices outstanding to understand potential cash flow issues or misstatements in vendor balances.
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.

With the new design, we also give you more freedom when importing files. You’re no longer required to upload the current year’s financial data before uploading any previous year(s) to the analysis.

You can get assistance when uploading files by clicking on the Get Assistance button. This gives you access to articles in our knowledge base and to MindBridge assist to contact the MindBridge customer success team to help resolve any issues.

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

New Ai Auditor release: October 2019

September 28, 2021October 3, 2019 by Azalia Shamsaei

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, as shown in Figure 1.You can switch to the entries view by clicking on the Transactions button on the top right corner of the page, as illustrated in Figure 2.As seen above, you can launch Intelligent Sampler from the Entries View and you can create tasks on a line item by clicking on the button to add details to your task creation, including audit area for account balances or significant classes of transactions, as well as the related management assertion for the test being performed, as shown in Figure 3.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 as illustrated in Figure 4.Once you create a task on an entry, it will be added to your audit plan page.

Intelligent Sampler

Auditors can select samples, by using Intelligent Sampler, to stratify the population of financial data by risk rating. In this release, we have improved the Intelligent sampler, as shown in Figure 5.

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. This method is unique to Ai Auditor, and favors selecting the majority of the sample from the high and medium risk transactions.

You now have the ability to add Audit Area, Management Assertions, and sample name to the sample selection. You can also add multiple samples to the same population. In addition, we provide a visualization that shows the $ value of the selected samples for each risk bucket (high, medium, and low risk) and the total $ value of all the populations for the sample. This can be used to reconcile the total population for created filters to worksheets used for sample size calculation.

You can click on “Add to audit plan” to add the sample to the Audit Plan.

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. The visualization at the top of the page has been updated to highlight the $ value for the selected filter.

All items added to the audit plan can be searched or filtered in various ways to provide a convenient overview of your sample selection.From the Audit Plan page, you can view the revised Audit Plan Export as a Microsoft Excel or CSV document as a source of audit evidence to be saved in your working paper solution. We have also improved the performance of the CSV export for large files.

You can export the entire plan into Excel or CSV format by clicking on the Export Entire Plan button or add filters to create a simplified list of items for your client to prepare for your inspection by clicking on “Export Filtered Plan”.

We have also enhanced the audit export details for this release. We now show a summary of the engagement, date, and analysis name, as well as filters that have been applied to the audit plan, if any, in the Summary tab, as shown in Figure 7.In the Data Tab, there are three buckets that illustrate information about the transaction and/or entries, tasks, and Control Points. We have added the entries to the audit plan, as well as the audit area and assertion names. Another improvement to the audit plan is that we now show the risk scoring of each Control Point for each entry in the audit plan.

Large transaction support

Transactions that contain a large number of entries can be present in financial data for a number of different reasons, for example, a daily point of sale summary where a number of ‘micro-transactions’ are recorded into a larger ‘daily’ transaction.

These types of transactions can limit an auditor’s ability to assess the true transaction risk or identify problematic entries. In Ai Auditor, large transactions are also often given higher risk scores due to the fact that a large number of entries contribute in a disproportionate way to the overall transaction risk score versus other transactions with smaller numbers of entries.

To this end, we now recognize and suggest alternatives for the treatment of large transactions. During the loading of data in the Review Data stage, as illustrated in Figure 9, Ai Auditor displays statistics about the file, including details about the number of transactions and their corresponding number of entries. Through the new design of the Review Data page, you can quickly understand that there are 27108 transactions in your file and 509 of them are very large transactions as illustrated in the transaction length summary visualization.

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. After running Smart Splitter there are no transactions with 501-1000 entries.For example:Each matching pair is then labelled as a new smaller transaction in the file where it can be risk scored along with other transactions in the file.

When selecting the Smart Splitter option, you have the ability to select the fields within the file to use as the primary transaction ID. This selection is then used to process the file and match containing entries, after which a preview is displayed to the user prior to committing to the operation on the entire file.

At the completion of the Smart Splitter operation, all transactions will run through the Smart Splitter, and the user continues with the analysis, where they will see new transactions appearing in the analysis with updated identifiers representing each entry within the transaction.

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. This field is optional but must occur before the final analysis date. Adding a planning date helps our Customer Success team prioritize and time your engagement.

The Final Analysis Date is the date that you intend on receiving the data from your client. This field is now a required field as it helps our Customer Success team plan any intervention work (if required). The Engagement Lead is the primary contact responsible for leading the audit. They will receive any and all proactive communications from our Customer Success team about ERP documentation, support, etc.

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.

Through Libraries, you find the right information at the right time, and this is a big step forward in helping you complete your work faster and with greater transparency. Libraries give you a way to build standardized audit approaches. In the previous releases of the Library feature, you could customize ratios, and filters only.

In this release, we continue to extend the functionality of Libraries by allowing you to edit Control Point weightings within a Library that can be leveraged and reused across an Engagement. As shown in Figure 13, you can change the weighting of each Control Point through the scroll bar and you can save or discard changes.

Activity Report

In this release, the administrator can now track and collect user actions and behavior to get a complete timeline of all user activities to establish the events that have occurred and who caused them. The Activity Report, as shown in Figure 14, answers the ‘who, what, when, where, and how’ of all the user activities in Ai Auditor. With the Activity Report, administrators get precise information on critical events such as user logins, time of file load, time of analysis, and more.

The administrator can also go to Activity Tab and download the Activity Report. They can filter on date range and user, as well as select the event categories such as who created the engagement, organization, ingestion, and more.

Figure 15 illustrates an example of the Activity Report that shows the times when the user:

Created the analysis
Deleted the analysis
Logged into Ai Auditor
Deleted and created some tasks
Created a Library called “Insurance”

New Ai Auditor release: August 2019

December 18, 2020August 7, 2019 by Azalia Shamsaei

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.

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 in Ai Auditor based on the industry that your client operates in.

Through Libraries, you find the right information at the right time, and this is a big step forward in helping you complete your work faster and with greater transparency. Libraries give you a way to build standardized audit approaches. In the first release of the Library feature, custom ratios could be built, edited, and saved within a Library.

In this release, we continue to extend the functionality of Libraries by allowing you to define and store custom filters within a Library that can be leveraged and reused across an Engagement. Your firm can build up a suite of standard audit tests, focusing on particular transactions, and reuse them from client to client. To get you started, we’ve provided some predefined filters as illustrated in Figure 1. You can also construct and add your filters as illustrated in Figures 2 and 3.

Figure 1: Predefined filters in Ai Auditor

Figure 2: Adding and editing filters in Libraries

Figure 3: The Filter Builder

Enhancements to ratios

During the creation of an engagement, users can ensure the data is being correctly analyzed by selecting a Library that contains the proper financial ratios. The ratios correspond to the Library and are made available on the Trending tab in Ai Auditor. The administrator in a Library sets all default ratios.

We’ve recognized that the engagement team may need to define specific ratios for the engagements under review that are not defined in the selected Library. Therefore, in this release, we allow anyone who has access to the engagement to define a ratio through the Ratio Builder (Figure 4) and save it to the engagement. You can also customize the Trending tab by deleting or editing the custom ratios in the engagement as illustrated in Figure 5.

Once the engagement team has found a particularly useful ratio for a single client, an administrator can promote this ratio into the Library so that it is available immediately across all the clients sharing that Library.

Figure 4: Ratio Builder

Figure 5: Modifying custom ratio from an engagement

We have also made ratios easier to explain and understand by making a ratio’s calculation and values visible from the Trending tab’s ratio graph. As illustrated in the figure below, you can hover over a data point to see more information. Clicking on the data point now allows you to toggle between viewing a “value view” or “formula view” for each data point.

Figure 6: Viewing a data point’s information

Figure 7: Toggling between formula and value views

Custom account groupings

Have you ever wanted to leverage and see your own client account numbers and account groupings in Ai Auditor rather than using the MindBridge Account Classification (MAC) codes?

In this release, we are thrilled to provide this capability in the product. Let’s elaborate more on the details and explain where you’ll see and make use of your own account codes instead of MAC codes.

MAC codes in Ai Auditor

Prior to this release, when mapping your accounts, Ai Auditor would automatically map your accounts to the MAC codes. This meant that everyone had to use these codes everywhere in the platform. For example, the Filter Builder illustrated in Figure 8 is based on MindBridge Account Classification codes.

Figure 8: The account hierarchy in Filter Builder – Old style MAC-based

Custom account codes in Ai Auditor

With this update, Ai Auditor now supports custom account groupings. Administrators can upload their custom grouping using the MindBridge Microsoft Excel template. If using CCH Working Papers, administrators can upload a group trial balance via the ACCOUNT GROUPINGS section under ADMIN, as illustrated in Figures 9 and 10.

Once the account grouping is loaded into Ai Auditor, it can be referenced by a Library and used to create filters and ratios that can be used in an engagement. All the users who have access to that engagement see the new account grouping everywhere throughout the product. Figure 11 illustrates an example of the custom grouping in the Filter Builder.

Figure 9: Import Custom Account Grouping

Figure 10: Managing custom Account Groupings

Figure 11: Using a custom account grouping in Filter Builder

Improvements to Control Points

As the year progresses, you will see a lot of new Control Point enhancements making their way into Ai Auditor. One popular piece of feedback received from customers is to provide more detailed explanations for Control Points. Our users want more insight as to why a certain transaction fired a certain Control Point, or how the Risk Score was determined.

In response to this, we’ve added more details pertaining to each Control Point, and why a transaction would trigger it. This helps you better understand our Control Points, how they function, and the associated risk.

We’ve added more detailed information for each Control Point with new tooltips. Clicking the tooltip icon in the upper right of each Control Point tile, as shown in figure 12, provides you with more information on each Control Point.

Figure 12: Control Point tiles

We have included a “Learn More” link within each tooltip that takes you to more documentation for each Control Point. We’ve also added a robust chart for the Benford Control Point (Figure 13) that is based on your actual data, providing more context and identifying numbers appearing at higher frequencies. This new visualization gives auditors an intuitive sense for why a Control Point has triggered and provides greater insight to auditors examining transactions that may contain an outlier.

Figure 13: Control Point detail

Not-For-Profit analysis improvements

Interim analysis support for Not-For-Profit (NFP)

In the May release of Ai Auditor, we introduced support for Not-For-Profit (NFP) General Ledger (GL) analyses. In this release, we’ve expanded this functionality to support a General Ledger (Interim) analysis, allowing you to upload a partial GL file and analyze the available data.

An example of an interim analysis would be 9 months of GL activity. You can return to this analysis at a later point once you have the remaining GL data, facilitating a continuous and seamless workflow.

You can also add data from previous years to the analysis, allowing you to view and identify trends. Currently, you can compare your current GL against up to five years of historical data in the year-over-year trended view for your NFP analysis.

Figure 14: General Ledger (Interim) support for NFP analysis

Figure 15: Support for previous years in a NFP analysis

Improvements to data ingestion

In this release, column mapping settings are available to view after completing a data ingestion. These changes make finding missteps in the mapping process much easier, resulting in higher quality support for issues or errors regarding column mapping.

We’ve also made improvements to the transaction ID generator in the review data stage. When performing a GL analysis, if you’re carrying forward the interim analysis, you no longer need to create a transaction ID. Ai Auditor recommends the appropriate transaction ID based on the interim analysis previously performed.

Figure 16: Column mapping

Figure 17: Carrying forward the interim analysis transaction ID

New Ai Auditor release: May 2019

September 21, 2021May 24, 2019 by Azalia Shamsaei

Welcome to the “post-busy season” release of Ai Auditor, just in time for your Not-for-Profit audit engagements! Our new release of Ai Auditor includes a host of new features that enable more use cases, such as Accountant Reviews. This release includes several great features including:

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

Ratio Builder: Enables you to build and save ratios that are relevant to your clients and industry. Analyzing finances with these custom ratios can help you identify trends and other data that inform essential business decisions.

Not-for-profit: We have introduced not-for-profit (NFP) General Ledger analysis support, allowing you to perform financial analysis for NFPs with and without a fund-based structure (fund accounting).

Annotations and exporting analytics: Allows you to document and store insights gained during the planning of an audit or an analytical review to create supplementary documentation without the need for other tools.

Let’s dive deeper into each area.

Libraries

Do you make use of specific filters when you perform an audit for a healthcare client vs. a construction engagement? Do you have particular ratios that you track during the planning phase for different industries? Do you wish you could customize Ai Auditor based on an industry type with different ratios?

In this release, we’re introducing the concept of a Library. Libraries help you tailor the work done in Ai Auditor based on the industry that your client operates in. Libraries are used to manage and maintain the financial ratios needed for a specific type of analysis. In our next release, the ability to change filters and Control Points will be added to Libraries to simplify the selection of settings for different types of analysis. We’re making sure that you’re always doing the most relevant work possible so that you can differentiate your firm and drive profits. Libraries also allow you to reuse configurations across different users and departments within your firm.

An Administrator can create a new Library under the Libraries tab in the Admin section of Ai Auditor. Creating a library allows admins to define their own custom ratios, filters, or Control Point weighting so that you’re always seeing the right information.

To get you started, we’ve released two new NFP libraries along with the for-profit library, our original analysis type.When creating a new engagement, there is now a selection available in the engagement creation section where you can choose from the available Libraries. Choosing a Library selects the financial ratios that you’ll see in your analysis.You can construct and add ratios to Libraries. In this release, filters and Control Points will be read-only. In a future release, filters and Control Points will be included in Libraries for additions and configuration.

Through Libraries, you will find the right information at the right time, and this is a big step forward in helping you complete your work faster and with greater transparency.

Ratio builder

We recognized that the ratios presented were not always the most relevant for your client, and in many cases, alternate ratios or different types of calculations were required. Administrators now have the ability to define new ratios at the Library level. If, for example, the ratio of “Staff cost to Revenue” is particularly important for your client, we want to make sure that information is always at your fingertips.

When creating a ratio, users can select from any account or account level within the account structure, as well as specify different balance amounts such as the closing balance, opening balance, or the monthly movement. They can string together multiple accounts to create complex ratios, which can be saved for reuse in the Library.

Each custom ratio, as illustrated in the image below, contains a ratio name, a category (i.e., Activity, Profitability) and numerator and denominator for the ratio formula. Account amounts, specific values, and constants within the ratio builder can be included in ratio formulas. These values are displayed to the user for selection in the Add drop-down.When an engagement is created, a Library is selected from the list of available Libraries. The ratios corresponding to the Library, are made available on the Trending dashboard.

Not-for-profit support

NFP organizations are often complex, with restrictions in place that dictate the way they can spend and the way they handle income streams. NFPs can often be risky entities to audit and their books can be complicated and sometimes messy.

To support NFP analysis, we’ve updated our account grouping hierarchy to include NFP concepts such as contributions, pledges, grants, funds, and restricted and unrestricted net assets. You can leverage the new NFP-augmented account grouping through the selection of supplied Libraries.

To complement NFP analysis, we have added specific NFP-based ratios such as Liquid Funds Amount, and Defensive Interval, along with many more. With the new Ratio Builder feature, you’re also free to add your own as well.

With NFP analysis, we’ve also developed new Control Points to support specific types of industry analysis for NFP.

In this release, we’re providing two new libraries for NFP.

NFP Library

The NFP Library has an updated account grouping structure to suit NFP concepts such as contributions, grants, and new ratios specific to NFP, such as Operating Reserve, Change in Net Assets, and Operating Margin. We’ve also added a new Control Point that looks for abnormal amounts of expense activity.

NFP-Fund Library

The NFP-Fund Library supports fund-based accounting. If your client uses funds to separate accounts for tracking and reporting purposes, this is the Library to use.

This Library comes with an updated account grouping structure to suit NFP and fund concepts. When you create an engagement, you can choose this Library and upload an augmented Chart of Accounts (COA). For the fund-based NFP analysis, you are required to include a Fund ID and optional Fund Description column in your COA file.

Note that for a Wolters Kluwer CCH fund-based NFP analysis, Fund ID details will be ingested automatically from the CCH General Ledger file and do not need to be added to the COA file. When ingesting a CCH General Ledger file, Fund ID details will be pulled from the “Group” code in the CCH General Ledger file.

For fund-based NFP analysis, we’ve introduced several new Control Points, such as Fund Expense Flurry, Interfund Transfer, and Split Expense that offer quick and easy ways for you to identify transactions of interest in an NFP’s General Ledger.

Annotations and analytics export

This feature provides the ability for you to document and store insights observed during audit planning or analytical review to assist in getting that information into audit documentation without recreating in other tools.

As you are exploring trends and ratios, you can add annotations to each graph under the Trending tab. Annotations, graphs, and the underlying data can then be exported into Microsoft Excel for inclusion in final reports and papers.

In addition to having a list of annotations on each graph on the Trending tab, you can view a list of all annotations on the Annotations tab and filter by account to find all annotations related to an account as well as the corresponding charts and visualizations.

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!

The auditor’s fallacy: The law of small numbers

December 18, 2020May 13, 2019 by Erik McBain, CFA

Humans have used simple statistical sampling for millennia to make generalized sense of the world around us. Living in a resource-constrained world, statisticians gave emperors, surveyors, and accountants a simple workaround to the prohibitively intensive process of counting, checking, and validating everything. Sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of a much larger population.

Random sampling is an old idea, mentioned several times in the Bible with the word “census,” derived from the Latin word censere – “to estimate”. One of the world’s earliest preserved censuses was held in China in 2 AD during the Han Dynasty and appeared later in Ancient Egypt and Greece as a means of tallying or estimating population characteristics and demographics. Historically, the immense benefits of sampling’s simplicity outweighed any cost to accuracy. “Close enough” was good enough.

Fast forward to 2019 and we’re living in a tremendously different world with exploding data volumes and complexity. One domain where this is particularly problematic is the world of audit and assurance, where achieving a passable level of reasonable assurance is increasingly challenging.

For MindBridge Ai, the most obvious place to apply our advanced analytics and breakthroughs in machine learning is the audit world. To help everyone move toward a more wholesome and comprehensive risk analysis, enabling more informed decisions.

Simply, MindBridge Ai Auditor can be thought of as an advanced transaction analysis platform and decision-making tool that amplifies our ability to make sense of the complex and data-saturated world around us. Within our digital world, it’s now possible to pivot from reliance on sampling to algorithmically analyzing everything in a population.

Why is this evolution a good idea?

Why audit sampling doesn’t work

In Daniel Kahneman’s seminal work, “Thinking, Fast and Slow”, the author deals with problems related to “the law of small numbers,” the set of assumptions underlying prevailing statistical sampling techniques.

People have erroneous intuitions about the laws of chance. In particular, they regard a sample randomly drawn from a population as highly representative, that is, similar to the population in all essential characteristics. The prevalence of this belief and its unfortunate consequences for the audit and assurance business are the countless high-profile audit failures. The mounting issues related to outdated standards and problems related to transparency and independence have prompted regulators to go as far as tabling legislation for the break-up of the dominant Big Four firms.

Kahneman makes the point that we’ve known for a long time: The results of large samples deserve more trust than smaller samples. Even people with limited statistical knowledge are intuitively familiar with this law of large numbers but due to human bias, judgmental heuristics and various cognitive filters, we jump to problematic conclusions/interpretations:

Humans are not good intuitive statisticians. For an audit professional, sampling variation is not a curiosity, but rather it’s a nuisance and a costly obstacle that turns the undertaking of every audit engagement into a risky gamble.
There’s a strong natural bias towards believing that small samples closely resemble the population from which they are drawn. As humans, we are prone to exaggerate the consistency and coherence of what we see. The exaggerated faith of auditors in what can be learned from a few observations is closely related to the halo effect. The sense we often get is that we understand a problem or person or situation when we actually know very little.

This is relevant for auditors because our predisposition for causal thinking exposes us to serious mistakes in evaluating the randomness of a truly random event. This human instinct and associative cognitive machinery seeks simple cause and effect relationships. The widespread misunderstanding of randomness sometimes has significant consequences.

The difficulty we have with statistical irregularities is that they call for a different approach. Instead of focusing on how the event came to be, the statistical view relates to what could have happened instead. Nothing, in particular, caused it to be what it is – chance selected from among its alternatives.

An example shared by Kahneman illustrates the ease with which people see patterns where none exist. During the intensive rocket bombing of London in World War II, it was generally believed that the bombing could not be random because a map of hits revealed conspicuous gaps. Some suspected that German spies were located in the unharmed areas. Careful statistical analysis revealed that the distribution of hits was typical of a random process and typical as well in evoking a strong impression that it was not random. “To the untrained eye,” the author remarks, “randomness appears as regularity or tendency to cluster.” The human psyche is rife with bias and errors in calculation, that have meaningful consequences in our work and lives. Algorithmic and computational tools like MindBridge Ai Auditor stand to improve the human ability to make better and less biased decisions.

Minimizing risk exposure

In Kahneman’s article “Belief in the Law of Small Numbers,” it was explained that intuitions about random sampling appeared to satisfy the law of small numbers, which asserts that the law of large numbers applies to small numbers as well. It also included a strongly-worded recommendation “that professionals regard their statistical intuitions with proper suspicion and replace impression formation by computation wherever possible”. As an example, Kahneman points out that professionals commonly choose samples so small that they expose themselves to a 50% risk of failing to confirm their true hypothesis. A coin toss.

A plausible explanation is that decisions about sample size reflect prevalent intuitive misconceptions of the extent of sampling variation. Technology such as machine learning and pattern recognition are removing this bias to the enormous benefit of practitioners currently at the mercy of mere sampling luck to find what is important.

Thanks to recent advances in cognitive psychology, we can now see that the law of small numbers is part of two larger stories about the workings of the human mind:

Exaggerated faith in small numbers is only one example of a more general illusion – we pay more attention to the content of messages than to information about their reliability. As a result, we end up with a view of the world around us that is simpler and more coherent than the data justifies. Jumping to conclusions is a safer sport in the world of our imaginations than it is in reality.
Statistics produce many observations that appear to beg for a causal explanation but do not lend themselves to such an explanation. Many facts of the world are due to chance including accidents of sampling. Causal explanations of chance events are inevitably wrong.

We are at an important crossroads where we must reconsider traditional approaches like audit sampling in the context of the incredible technology that is now available. For companies that are struggling to interact with huge volumes of digital transactions, detect risk, and extract meaningful insights, MindBridge Ai Auditor is an elegant and powerful solution.

New Ai Auditor release: April 2019

December 18, 2020April 8, 2019 by Azalia Shamsaei

MindBridge Ai Auditor continues to make improvements to deliver the best experience possible. For this release, we’ve made improvements to data ingestion, making it easier to get client data into the platform.

User file format confirmation

Data is the fuel that powers our machine learning, statistics, and rules-based analysis in Ai Auditor. Data loaded into the product must be free of syntactical errors and be in formats that allow for data analysis. We’ve automated this process as much as possible, however, in some rare cases, we’re unable to detect the proper file format without manual intervention. In this release, we’ve improved this process.

We’ve added the ability to confirm the format of a data file before proceeding to the column mapping and review data stages. You’re presented with a drop-down (see below) on the Select Data page that’s pre-filled with the format that we’ve detected. If desired, you can change the format.

Page after uploading a file

Example of options in the drop-down

Multiple date formats

Another improvement to the data ingestion process is added support for multiple date formats within a single column of a data file.

Prior to this enhancement, if a file contained more than one date format, manual intervention was required to adjust the data into a supported format.

In this release, you’re prompted for date format clarification after columns are mapped to ensure that it’s correct. This capability automatically recognizes a variety of formats to help save time during the data ingestion process.

Democratizing financial and audit analytics with AI

December 18, 2020February 20, 2019 by Andrew Popp

PwC recently shared that in 2018 alone, 12 zettabytes of financial services industry (FSI) information was generated, but less than 0.5% was actually leveraged by businesses. This financial data explosion comes from an ever-increasing number of ERP and CRM systems employed by businesses and their partners, gathering and consolidating different payment, expense, inventory, and maintenance cost ledgers across the organizational landscape.

Contributing to this low rate of analytic engagement is the fact that current methods for analyzing financial data are slow; limited by time, capabilities and skill sets of the people and the software available to support the auditors and financial analysts.

Current analytics tools can’t keep pace

Let me illustrate this problem with a reasonably routine example. An internal auditor is asked to perform a risk analysis on a general ledger with five million rows. The Microsoft Excel limit for analysis is one million rows, preventing the auditor from using what is considered the world’s most popular analytical software. This means the auditor must engage their internal data analytics team, made up of specialized resources trained in the use of one the popular Computer Assisted Audit Tools (CAATs). This team in turn will write a script to perform the required analysis of the entire general ledger, or to save time, sample the data and attempt to extrapolate the risk.

The problem is, once the script work is scheduled and completed, the analysis might be too late for the audit process, or the priorities of the organization may have shifted to the point where the analysis is moot. If the team decided to use a sample and extrapolate the risk, they may have significantly less than a 100% risk analysis, putting the organization at risk.

It’s therefore no wonder that businesses are not making use of analytics; a world running on machine generated data, human-dependent analysis cannot keep pace.

AI enables analytics for all

This puts artificial intelligence (AI) front and center as the means of breaking this dependency on specialized resources and human-speed analysis. MindBridge is focused on developing the MindBridge Ai Auditor for auditors and finance managers to load financial data and analyze it on their own.

This allows auditors to bypass these specialized data science resources and tap directly into the power of their own data, effectively democratizing the access to financial analytics.

Microsoft CEO Satya Nadella shares that,

The core currency of any business going forward will be the ability to convert their data into AI that drives competitive advantage.

MindBridge is in complete agreement with Satya’s view, and I look forward to sharing more with you on this.

Top 3 actions to take before the 2019 busy season

December 18, 2020January 14, 2019 by Roy Sarkar

To best prepare for the upcoming busy season using MindBridge Ai Auditor, we’ve prepared this list of key actions to take, based on feedback received from users and their clients. Take note, plan your actions, and if you have any questions, please don’t hesitate to contact us for help.

Key actions

Prepare your client and their data
Risk assessment and planning
Engage the MindBridge customer success team as early as possible

Prepare your client for AI-based audit

Planning and communicating with your client during busy season is always a good practice and it’s even more critical for firms going through their first artificial intelligence-based audit. Most of the steps will be familiar, some are new, so you want to make sure that everyone’s on the same page as far as activities, expectations, and goals.

Key to working with Ai Auditor is letting your client know that you’re moving to a data-driven audit approach this year and that the earlier their data is submitted, the more effective the audit. Often, clients aren’t as prepared as auditors would like them to be, so consider these initial activities to get them up to speed:

Have the conversation that this audit makes use of artificial intelligence technology to provide 100% transaction coverage and to identify anomalies and risk areas in their data that may have been missed by their accountant or controller. Not only is this better for your firm’s brand, it boosts your client’s credibility.
Obtain and ingest your client’s data into Ai Auditor as soon as possible. Often it takes a few times to get the right data from your client’s IT team (such as knowing what fields to export) and different enterprise resource planning (ERP) systems require different amounts of effort to extract the information. Our Customer Success Managers (CSM) are always available to help and by engaging their expertise early, any issues in getting the data from client systems into Ai Auditor can be resolved before it’s too late, better preparing all teams for a more efficient audit later.
To support the analytics/graphs/ratio and forecast and trending data in Ai Auditor, we recommend that you obtain the current year plus four prior years worth of client data. These insights will be something new for your client and provide much-needed value.

These critical early efforts during busy season will ensure that the transition to a data-driven audit will be smooth for both you and your client.

Risk assessment and planning

Planning for an audit is often performed in a black box, where the auditor has very little insight into client operations until the data is received and even then, assessment can be a difficult process. Using Ai Auditor gives you a deeper level of insights into client data than traditional methods and makes planning more effective, so consider these actions:

Prepare for your initial discussions with the senior finance official by running their data through Ai Auditor to better understand their profile and identify areas of interest to have conversations about. Not only does this demonstrate your knowledge of the client’s operations, it helps to have any difficult conversations early rather than waiting until the rush of the audit process.
Once client data is loaded, prepare the audit plan, create the necessary tests, and save them all in Ai Auditor using the Filter Builder feature. Performing a risk assessment of your client’s data will identify areas to test during the audit and helps create the test plans to execute. Reviewing the analytics, ratios, and graphs with current and past data will call out any items that need to be addressed during the audit. Using the Filter Builder feature allows you to create any standard tests, such as Journal Entry testing, selection of AP and AR confirmations, etc., and save them to be used once the final data is ingested – saving a tremendous amount of time. It’s also good to know that any sample selected for existence (also known as selecting from the system) can be chosen within Ai Auditor.
From a fieldwork perspective, having client data within Ai Auditor allows you to do all your audit tracing through to the platform, saving you time and the need to go back to your client for additional clarifications.

Engage our customer success team early

A common theme here is to contact your assigned CSM as early as possible, to ensure a smooth data export and import, understand the features available to you in Ai Auditor, and to best prepare for client conversations and reporting. The first step is to let your CSM know when to expect your client’s data, to help with planning during busy season.

We’re here to help and we have plenty of experience across different ERP systems, environments, and types of clients, so to avoid any pain down the road, engaging earlier helps us all.Contact us now to get started with your busy season planning.

How accountancy can thrive in the age of AI

December 18, 2020October 31, 2018 by Roy Sarkar

The world is changing at a faster pace than ever, leading chief economist at the Bank of England, Andy Haldane, to state that the disruption caused by the ongoing fourth industrial revolution would be “on a much greater scale” than that experienced during the Victorian industrial revolution. Technology is evolving and infiltrating different industries each day and the era of artificial intelligence (AI) is very much upon us. But do employees risk becoming “technically unemployed” with this rise of technology? Or instead, could accountancy thrive thanks to the rise of AI?

Change is in the air

The adoption of new regulations around mandatory audit firm rotation has stimulated competition in the market and caused real drive for the accountancy industry. The most progressive firms have identified AI capabilities as an important differentiator, but still appreciate that the best practice is a collaborative approach, one that augments human and artificial intelligence.

In the same way that the human brain cannot compute hundreds of thousands of data points in a split second, a machine cannot always understand the and context of real-world accounting. In combination, an accountant fueled by AI is turbo-charged to make faster, more accurate decisions, while having more time to focus on providing guidance, value, and insights.

Enhancing the practice

Although proactive firms are deploying AI to help drive efficiency, reduce risk, and increase quality in their compliance processes, there still remains caution in some parts of the market. Implementing AI to augment and support the practitioners in the accountancy world has shown how this technology can benefit the industry, so why is there still hesitancy? It’s a caution that’s driven by myth, misunderstanding, and misconception regarding the perceived black-box nature of artificial intelligence. Each is an unnecessary barrier to the progress all companies need to make if they’re to compete in the modern marketplace.

Often the adoption of AI tools remains hamstrung by the idea that they cannot integrate with existing technology and are complex to use, and this comes down to a misunderstanding of what’s available. The most effective solutions are affordable and designed to work easily alongside people. They’re designed to demystify AI and make them intuitive to use. Moreover, as regulators take an increasingly tough stance on audit failures, AI solutions are a long-term investment that can reduce risk, increase efficiencies, and improve the quality of financial analysis.

Collaboration, not isolation

In the age of AI, each company must become a technology company in order to defend and grow their market, including the financial industry. It is no longer a question of if the role will change, but how can accountants equip themselves with the necessary skills to thrive in the changing world. It’s time to forge forward and recognize that accountancy actually benefits from the rise of artificial intelligence, unearthing more of the risk in financial data, and providing greater assurances than ever before.

AI is not something for accountancy to fear; it’s something for the industry to embrace in order to enhance auditing practice, increasing accuracy and efficiency.

Click here to find out more about the world’s first and only AI-powered auditor platform.

Answering questions about Ai Auditor

December 18, 2020October 22, 2018 by Roy Sarkar

As practical applications of artificial intelligence (AI) are new to the finance space, especially with regards to audit, it’s no surprise that the same questions come up across our expert-led webinars. To help you understand how AI is applied to audit, we’ve collected the most common questions and answers here, as provided by our V.P. Growth, John Colthart.

Q: What programming skills or training are needed to use Ai Auditor?

Our goal is to minimize training to make the platform easy to use – a different philosophy from some of the old audit tools you may have used in the past. We designed Ai Auditor to be as user friendly as possible to help you get to maximum value as quick as possible, which means you need no programming or scripting skills to get things done. It’s all drag and drop actions, mapping your data, running the analysis, and viewing results in as easy a manner as possible.

Of course, we do recommend and include training on using the platform itself. Typically, that’s a kickoff with our customer success team to show you around the platform and help you load in that first data set. We give you a few days to play around with the data and reports, then set up a more focused discussion to help you get the most out of the results, such as understanding what control points do and what the machine learning algorithms are hunting for.

Q: Will Ai Auditor replace our existing audit tools or is it in addition to what we use?

The honest answer is that it depends on what you want to accomplish. If you’re just using a working paper solution to gather data to do quick assessments of a trial balance, our platform would absolutely be an addition to what you’re already using. You would use it to go even deeper into the analysis of the data and bring all our reports back into your working papers to have a much higher level of confidence. On the other hand, if you’re using a data analytics tool, especially a visual tool that doesn’t have machine learning built into it, Ai Auditor could potentially be a more effective and easier to use replacement.

We never say it’s one way or the other because every firm we work with has a different view of how technology supports their people and engagements and how they look at things from a line of business perspective, for example M&A, or all the way through to assurance audit and taxation.

At the end of the day, it really depends on the use case but one thing is certain, Ai Auditor is a tool used to help people be more effective at understanding data and gathering evidence, in the capacity that best suits their needs.

Q: Where does all the data that’s being analyzed come from?

We provide a drag and drop interface to load your data and integrate with the most common ERP systems used today, things like CCH Engagement, QuickBooks, Thomson Reuters AdvanceFlow, NetSuite, Sage Intacct, and more, to pull the various types of data we need. For something like accounts payable, for example, we use information from the ledger itself, including the payables register at the end of the period so we can see what’s outstanding and things such as the vendor name and the user hierarchy.

We also eliminate the need to spend time or IT resources on data extraction, manipulation, and ingestion – we take care of all the data heavy lifting so you can focus on the analysis and results.

Q: Does Ai Auditor help with audit planning?

This one is critical to understand: Our platform isn’t just for performing year-end audits, rather it plays an important role throughout the year, including planning. Our interim analysis is always available, going back to whatever period is available from the data, to help you see and understand how the business is transitioning at various points in time.

We support planning in different ways, such as looking at the data to identify and prioritize where you should be spending more time. It could be potential risk in inventories or accounts payable, or really anything that could influence your thinking around how the business is performing. Additionally, we also give you all those control points to show exactly what’s going on in the business and we can help you derive insights from the available data.

We want you to see and drill down into where the risks are at any point in the year, all for the same price as doing a single engagement at the end of the year.

Q: How and where is your data store?

MindBridge Ai cloud services are hosted on a secure cloud infrastructure, with our primary and backup providers fully ISO 27001 and SSAE 16 compliant. Our software stack is designed for defence in depth, deploying redundant controls in the infrastructure, network, platform, and application to ensure no single point of failure.

Q: How secure is the data?

Customer data is always protected, using NIST-approved algorithms (AES 256) and the most secure protocols and implementations available. All network connections are encrypted and all data stores, including primary and backup, are encrypted at all times.

Q: How do you control who has access to what data?

MindBridge Ai has zero access to your client’s data. We maintain SOC 2 compliance and we build in very high security around who can see and perform operations on various types of data, with different levels of hierarchical security. Each Ai Auditor customer has their own dedicated database and storage and there’s no interaction between customers or mixing of data.

At the end of the day, securing your client’s data is paramount and being able to secure that internally – who gets access to what pieces – is also of paramount importance for us.

Q: How do we use the results we get from Ai Auditor and include them as part of our overall processes?

Every report is available in downloadable format, whether it’s images from a screen or some form of data tables. For example, our data can be exported to a Microsoft Excel file and attached as a supporting document to your audit report. In fact, we highly recommend taking all the data we provide and showing them to your client, where it won’t cross independence lines, so they see you as the expert, trusted partner you should be along with the evidence to back it up.

You can also produce reports to share with your end clients including income statements, financial trending analysis, financial analysis, and more.

For more information on Ai Auditor or to book a demo, visit mindbridge.ai.