Can AI Give Superpowers to Treasury Teams?

Transaction Categorization

The basis for any cash flow insight is understanding what drives cash inflows and outflows. Finance and treasury teams are used to downloading bank statements and manually categorizing each transaction to create the cash database that can then be used for various analyses. Rule-based categorization saves significant time and reduces some of the manual effort. But it still requires initial, time-consuming setup and ongoing maintenance.

AI-based models are changing the game for transaction categorization. From automatically identifying certain types of transactions (e.g. intercompany transactions) to enhancing categorization by identifying similarities between uncategorized and previously-categorized transactions, AI makes the job faster, easier and more efficient. 

Additionally, AI enables companies to leverage a broader knowledge base (such as how bank fees are charged by different banks). Bottom line - AI is reducing the time needed to categorize transactions by >90%, without compromising on (and even improving!) categorization accuracy. 
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Trends, Anomalies, and Insights

AI is transforming treasury operations by automating routine tasks and providing predictive insights that empower teams to make smarter, more strategic decisions.

Once the team has a robust database of categorized transactions, it’s time to analyze and make decisions. All the data is there, you just need to find the interesting insights. Sounds easy right? In fact, every finance or treasury analyst knows that's actually the toughest, most time-consuming part.

Thinking of which queries to run, technically building the reports, and identifying the trends and anomalies that are insightful, are all tedious tasks that require a lot of trial and error and repeated number crunching.

AI is revolutionizing cash flow analytics. New generative models are able to identify trends in a contextual manner, and surface only those insights that can potentially have an impact on the business. Anomaly detection can be easily calibrated to surface suspicious transactions and high-impact inflows and outflows that require the team’s attention.

Finance and treasury professionals’ shortage is a huge problem that’s only going to become worse. The ability to leverage AI empowers lean teams to focus on the insights and on devising effective action plans, rather than on manual, less value-add number crunching. We have entered a new reality, one that makes finance and treasury teams much more strategic. 

Cash Flow Forecasting

This is one of the most sought after AI features when it comes to cash flow management. It is also one of the most over-hyped. Yes, it would be amazing if a magical AI could tell the future of cash flow and accurately predict where a company’s cash balance is headed. But, this is unfortunately not a realistic expectation (at least not until AGI takes over all of our lives and makes all decisions).

Even the most advanced AI-based models use historical data to forecast the future, and the reality is that for complex companies, the future never behaves exactly like the past. There are always changes that need to be factored in and taken into account when trying to build and roll an accurate cash flow forecast. Moreover, the goal of a cash flow forecast isn’t just to get to the bottom line of ending cash balance, it’s also to understand causes and effects and respond to cash changes effectively so as to prevent problems. If someone offers you a click-of-a-button automated AI cash forecast, run as fast as you can! 

Nevertheless, when used correctly, AI can have tremendous value for accurate cash flow forecasting. While it’s not a fortune teller, it can effectively identify trends and suggest optimal formulas to use, provide basic root-cause analyses, and suggest actions and mitigations that can dramatically improve both the forecast building process and the decision making process that follows. 

Dynamic, Natural Language-generated Reporting 

Building reports is hard. Whether it’s in Excel, a BI tool, or a dedicated treasury platform, inputting the relevant syntax that will spit out the analysis you’re trying to perform requires some technical skills and tedious trial and error until you get it right. For example, SQL queries such as w​​indowing, aggregation, and grouping don’t come naturally to most treasury analysts. 

This is one area in which generative AI is creating a completely new reality. LLMs (large language models) that tap into companies’ cash data can automatically generate any report that crosses an analyst’s mind, based on natural language input. Want to know what were the three biggest expenses last week? Just ask. Want to understand how fast collection is growing in a specific geography? No problem. The two caveats here are (1) the data needs to be properly categorized, otherwise it’s a classic garbage-in-garbage-out situation (AI can help with that too – see #1), and (2) that you need to know what’s interesting to explore (see #2).

Summary

AI really is changing the world, and finance and treasury operations are no exception. It can leapfrog a few of your team’s abilities, including some important ones:

• Quickly and accurately categorize transaction
• Surface important and insightful trends and anomalies
• Build accurate and explainable cash flow forecasts
• Empower you to easily answer questions regarding your company’s liquidity. 

However, it’s important to remember that it’s not like a magic device that transports you instantly from one place to another. A more fitting analogy is that it’s like a bike you can ride to get from point A to point B – you still have to pedal, but you will get where you're going faster and less sweaty.  

 Are you interested in learning how your company can benefit from AI powered cash flow management? Let's talk.AI is a term that’s been thrown around a lot recently – AI forecasting, AI automation, AI finance operations, AI reconciliation, AI EVERYTHING. While the progress that’s been made in the field is undoubtedly a game changer for treasury teams, there’s also a lot of buzz and hype that’s not always backed by actual value. The goal of this post is to demystify AI and shed light on some areas where it can truly give finance and treasury teams superpowers. Let’s get to it.