Stock illustration: Robot analyzing data

High Impact AI Use Cases for Treasury | Cash and risks

Stock illustration: robot analyzing data

Digital transformation has left few business areas completely untouched. Almost every business function, in every type of business, has seen productivity gains from technology over the past decade. And the pace of change in business could only accelerate from here!

Indeed, as companies increasingly develop intelligent ways to take advantage of the vast amounts of data they generate in their day-to-day operations and determine how best to automate business processes to improve efficiency and secure competitive advantage, digital transformation is likely to continue to win. momentum.

The finance function is certainly no exception to this trend. Treasury and finance workloads, which are often data-heavy and repetitive, lend themselves perfectly to improvement through modern analytics and advanced automation. Most finance functions have been collecting data and automating processes for years, with good results in most cases. But the idea of ​​automation in finance tends to conjure up simplistic tasks that can be easily delegated to machines, while humans continue to do any work that requires judgment or thought.

Now, thanks in large part to artificial intelligence (AI) and machine learning (ML), finance can go beyond automating simple, repetitive tasks and move towards an environment in which people and machines collaborate to transform financial and business capabilities. Gartner calls this “autonomous finance”.

As with all emerging technology trends, AI is surrounded by a lot of hype, which casts a shadow over the market. This makes it difficult for treasury and finance teams to identify the right innovations for their organization. Blindly accepting lofty claims about the benefits of AI, without understanding the costs, can lead to disappointing projects that fail to deliver on their promises.

To help customers on their journey to autonomous finance, Gartner experts analyze the many use cases for AI in finance and evaluate them, broadly, in terms of potential business value and feasibility. In the area of ​​treasury and risk, three use cases stand out as providing the most value, while being able to be implemented in a typical enterprise finance function:

1. Detection of anomalies and errors. Accounting, finance and treasury teams face the ever-present risk that recorded transactions or balances are in error or violate accounting principles or policies. Problems like this can be nearly impossible to eliminate, largely because the increasing complexity and volumes of data make it very difficult for staff to find errors manually. New and ever-changing accounting rules, regulations, and policies compound the problem, increasing the chances of embarrassing accounting errors.

If programmed correctly, the machines are inherently more capable of identifying unusual or worrisome activity in very large datasets than the typical finance staff member. Simple automation tools can speed up the error detection process by using predefined business rules to analyze a company’s records for known types of issues. This greatly increases the likelihood of a problem being detected, due to the amount of data a software tool can examine in a given amount of time. But even greater benefits can be achieved by leveraging AI and ML.

The anomaly detection software uses a series of ML models to identify transactions or balances that may be incorrect or non-compliant. These tools analyze huge amounts of data, using AI algorithms to detect correlations between seemingly unrelated data points. They look not only for known problems, but also for “unknown” patterns that have not been specifically defined in advance by humans. And they can compare activity across functional boundaries such as shipping, receiving, attendance records, accounts payable, and accounts receivable.

When they detect an anomaly or error, the systems can provide an alert, either in real time or through periodic batch processing, allowing designated users to take appropriate investigative or corrective action. A complete anomaly detection solution will also include real-time analysis during data entry to prevent errors from entering the workflow and requiring costly corrections downstream.

Anomaly detection systems help improve data quality and reliability. They also identify unusual activity that may indicate theft or fraud, potentially flagging up irregularities that the finance and accounting team never thought to look for. Automating the identification of anomalies and errors in a way that leverages AI can allow a treasury or finance team to spend less time resolving issues and responding to audit findings , and more time supporting business goals.

2. Cash forecast. Increasing visibility into a company’s cash positions and correctly projecting future cash flows enables better business decision making. Capital investments can be made with more confidence and short-term financing options can be optimized. Improving the speed and accuracy of cash flow information for treasury managers is especially important in today’s volatile economic landscape.

AI can help provide this insight by combining data on sales, production, expenses, and collections, and then forecasting cash flow based on this consolidated information. It is essentially an extension of cash recoveries, in which AI tools use patterns in data from past periods to predict the net impact of cash on the organization of future internal and external events. .

For treasury and finance teams developing their 2023 operating plan amid unprecedented uncertainty in global markets, the benefits of better forecasting should be obvious. Those who continue to rely on outdated or overly optimistic forecasts risk unexpected setbacks during periods of economic turmoil, such as the one we are currently experiencing. This is an area that treasury leaders should currently assess for AI support.

3. Compliance and risk monitoring. Similar to anomaly detection tools, compliance-focused AI systems can examine transactions or groups of transactions for issues related to compliance with regulations or internal policies. ML algorithms can highlight areas where business practices may introduce an unacceptable level of compliance risk.

The rapid proliferation of regulations, in addition to the growth in volume and complexity of data, which is exponential in many cases, makes it almost imperative to involve machines in monitoring compliance violations and risks. With automation supported by machine learning, a financial group can validate that the company has followed compliance rules, while using far fewer resources than human oversight would require.

An AI-based approach has the added benefit of minimizing the uncomfortable and daunting disruptions of compliance audits. Additionally, ML capabilities can help treasury and finance teams measure changes in the likelihood of the business facing specific risk scenarios, make business threats easier to quantify, and indicate whether counter-actions or response measures are necessary.

Find the right solution for your organization

There are certainly other ways treasury and finance teams can harness the power of AI and ML. These three use cases sit at the intersection of high potential for benefit and reasonable feasibility for a typical enterprise treasury function, but applicability may vary across organizations and industries.

It should be noted that the most valuable use case leverages a company’s unique strengths and allows that organization to further differentiate itself. Specific companies may well find better potential AI use cases for their unique context than these three. Whichever path your organization takes on the AI ​​journey, it will be important to carefully select use cases that fit your organizational needs, ideally as part of a financial technology roadmap that defines short and long term goals.

Start with small steps and lower risk iterations, then grow from there. A phased approach not only helps avoid big mistakes, but also gives staff enough time to keep pace with technology. As time passes and the potential of the solutions are realized, iterative cycles of improvement will begin to cover a wider range of processes and responsibilities.

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Mark D. McDonald is senior research director in the financial practice of Gartner. Combining decades of experience in corporate finance and a degree in software engineering, Mark is focused on the practical adoption of AI and machine learning in finance and accounting processes.

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