AI for government finance: Understanding value & barriers

audit sampling method

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.

artificial intelligence in accounting

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.

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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.

technology in auditing

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.

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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

function of internal audit

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 AICPACPA AustraliaICAEW, 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:

  1. We’ve heard about the value of AI from others
  2. We hope AI will create new opportunities to attract and grow clients
  3. 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.