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The public’s trust of governmental budgeting, fiscal management, and reporting is at an all-time low, especially in the aftermath of the 2008 financial crisis, where only four out of ten people in OECD countries expressed confidence in their government. Cases of fraud, bid-rigging, and pay-to-play are never far from the headlines, and have continued to undermine trust in the public servants and elected officials tasked to oversee the complex work of managing government finances.

A large portion of this mistrust can be attributed to the struggle that government finance managers and auditors are facing in analyzing the increasing amount of financial data. Current financial control and audit techniques, including legislated audit requirements, are not able to scale to keep pace with the massive data explosion coming from their own accounting, payroll, and expense management systems. One government response to this issue, open data, enables a sense of fiscal transparency with the public but it doesn’t replace the rigorous professional analysis required to identify fraud, errors, and omissions in large amounts of data.

Enter artificial intelligence (AI). Leveraging a mix of machine learning and natural language processing (NLP) techniques, AI can help government auditors and finance officials deal with the massive amounts of data they are required to professionally process in a timely fashion to meet their fiduciary responsibilities to their taxpayers, which in turn will help restore the public’s trust in government.

The financial data explosion in government

Imagine you are running a government department that has 150 different operational entities but only have the resources to audit four or five a year. That’s a massive financial blind spot that will make your comptroller or CFO lose sleep at night. It’s not a question of whether fraud or errors are occurring, it’s a question of when the news will break that they have happened.

Equally challenging is the government audit department that must perform 150 audits per year by legislative mandate. With so many audits required, where is the time to dive into different areas of analysis and reveal insights that can help lead to improved service to the public?

PwC estimated that 18 zettabytes of financial information was created globally last year. Visualize a standard pitcher of water as a byte. One zettabyte is all the water on planet earth. PwC also estimates that only 0.5% of that data is analyzed, and it’s in this unreviewed data that the errors, omissions, and frauds that the press reports on is occurring.

Let’s also be clear on what governments are catching in the data they are analyzing. Current financial controls, audit methodologies, and analytics catch about 3% of the total global fraud as estimated by the Association of Certified Fraud Examiners. Tips, on the other hand, uncover 50% of the major corruption cases.

Current government processes and professionals are not catching the errors faster than whistle-blowers are reporting it. So it’s not surprising that governments are perceived as being ineffective in how they deal with the detection of fraud and errors in their financial statements.

Fraud hotlines & open data: First steps

Governments have responded to this issue in a number of ways. One of the first has been the rapid rollout of fraud hotlines, driven by statistics on how fraud has been uncovered to date in the majority of cases.

The other approach has been to broadly publish budget, financial, and audit reports to the public. This approach has been tied to the open data/open government movement and has been seen by many as a more citizen-inclusive approach to solving this problem. The idea is, by releasing all the data to the public, concerned citizens can dig into finances and find errors and mistakes to help share the burden of analysis.

This refreshing approach to transparency in government has its benefits, including seeing governments become better stewards of their own data, and being more open to feedback. However, the open data movement hasn’t been able to put an end to the public’s mistrust as first promised. Missteps in areas such as the standardization of data formats and APIs, the frequency of updates to the released data sets and the scope of the data released has limited analysis by external parties.

In addition, as larger data sets are released, individuals are no longer able to perform a full analysis of the data in a single pass. Microsoft Excel, the world’s most widely available financial analysis tool, has a million-row limit for data processing, and other available data science and accounting tools and resources are out of reach for the majority of citizens. The data might be readily available, but the professional tools and skills are not.

Artificial intelligence: Creating government efficiencies

Artificial intelligence is part of what is being dubbed the Fourth Industrial Revolution and has the ability to dramatically improve the efficiency of organizations. In the financial and audit worlds, AI offers an approach that includes:

Continuously ingesting large amounts of financial data from different sources
Risk assessing 100% of all transactions against all current and past data
Indexing all transactions in a way that can be interrogated with common language questions, such as “Show me all transactions with high risk at the end of 2018”
Producing reports that allow auditors and financial officers to extract insights into how their government is operating, and take corrective or reinforcing actions accordingly

In this way, AI builds on what the open data movement has started, as it offers a means of democratizing the application of complex analytics to governmental financial review. Governments can now load and analyze all their financial data, applying the open government standard of transparency to both the data and the algorithms they are using, and then releasing the results for the public to review when completed.

AI solves the problem of the department with 150 potential audits and having the resources to run 4-5 audits only. AI can run continuously on every department and help to direct the limited resources to the departments that exhibit high risk instead of burning resources using round-robin audit approaches and random sampling of transactions to review. For the departments not chosen for an audit, financial managers can be sent risk reports in each department allowing them to take corrective measures in advance of a future audit.

And for the organization facing 150 mandatory audits, AI can drive cost efficiencies as standard procedures can be automatically performed, freeing up auditor time for deeper interrogations of the data.

AI can also make fraud hotline tip review more efficient, namely the requirement that anonymous tipsters have to be convinced to give up their anonymity to prove the claim being made is true. Upon receiving a tip, an AI tool can be directed to review the data claimed at risk. This allows the financial data to speak for itself, relieving the tipster of having to reveal their identity early on in the risk assessment of the tip.

Artificial intelligence: Finding financial anomalies

So how does AI find anomalies in financial data and allow auditors and financial officers to search the data quickly? These capabilities are found in the application of machine learning and NLP.

Machine learning is a sub-field of artificial intelligence that focuses on the application of algorithms to large amounts of data to enable further insights. MindBridge Ai uses both supervised and unsupervised algorithms to risk rank all the financial transactions loaded into platform. Supervised algorithms are based on training data, and we developed an algorithm based on known patterns of fraud that was provided to us by forensic accountants.

Unsupervised algorithms are special, because they are developed to allow the data to speak for itself, meaning that transactions are clustered into neighborhoods of numbers that are interesting to accountants, such as rare connections between two accounts. These algorithms can also identify transactions that fall outside neighborhoods of numbers, called outliers.

These algorithms, run in concert with standard accounting rules and statistical techniques, such as Benford’s Law, allow us to risk score every transaction in a financial ledger. While this is done, the data is also indexed for rapid search capabilities, which brings us to the application of NLP.

NLP is another sub-field of AI and, in the accounting context, allows auditors and financial officers to ask questions of the data that has been risk ranked using machine learning, returning a list of risky transactions that fit the criteria.

Together, machine learning and NLP have been empirically tested to show that we can conservatively detect financial anomalies 10-30x better than current audit and financial analysis methodologies.

Artificial intelligence: Restoring public trust

The inability of government audit and financial departments to analyze 100% of the data in the government’s trust has been a major factor in its inability to spot financial anomalies. While fraud hotlines and open data techniques have been a step in the right direction, AI offers an opportunity for the government to actively pursue the detection of financial anomalies before whistle-blowers think to act on ethical and moral grounds.

Private sector audit firms are already turning to platforms such as MindBridge Ai Auditor, and over 40% of the top 100 audit firms in North America are currently engaged with us. In the public sector world, the auditors and financial officers in the Canadian and UK federal governments have already tested the MindBridge platform and measured the advantages of using AI against current techniques.

Students in accounting programs at over 60 universities in Canada, the UK, US, and Australia have also engaged in using Ai Auditor in their accounting courses. As AI-ready individuals move to becoming workers and taxpayers, they will demand that AI be employed by all levels of government to help in the detection of fraud, errors, and omissions in financial data. Citizens are already becoming aware of the capabilities of AI, as they hear about it every day in its application to autonomous vehicles and other government services, such as automated government decision making.

The Fourth Industrial Revolution is upon us, and there are significant benefits for governments in the early application of AI to detect financial anomalies to turn the tide on fraudsters and bad actors. Beating whistle-blowers to detect and mitigate fraud is an achievable goal through AI and will make major leaps towards restoring the public’s faith in a government’s ability to manage the public’s finances.

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

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.