Cash application is one of those processes that seems ripe for automation using AI. It involves matching incoming (and outgoing) payments—whether through bank statements, payment advices, or lockboxes—with the correct invoices. Traditionally, this task is handled by a series of rules-based automations designed to automate the matching process, reducing manual effort and increasing efficiency. The rules, however, take time to set up and to maintain, and if there are too many rules it easy to lose track of them.
Enter Artificial Intelligence (AI), a technology that promises to revolutionize how we approach cash application. AI excels at recognizing patterns, adapting to new information, and handling vast amounts of data—attributes that make it seem like the perfect solution for the intricacies of cash application. Indeed, AI has proven to be exceptionally good at matching payments with invoices, often achieving accuracy rates as high as 97-98%. This level of precision is impressive, especially when compared to older methods. However, as with all technological solutions, there’s a catch.
The Catch: The 2-3% Error Rate
In most business processes, a 2-3% error rate might be considered acceptable, or at least manageable. But in accounting, where precision is paramount, even a small error rate can have significant repercussions. When an AI system incorrectly matches a payment to the wrong invoice, it sets off a chain reaction of issues. The incorrectly matched invoice is cleared from the system, making it unavailable for the correct payment. This error can snowball, leading to a cascade of mismatches that grow more problematic over time.
These errors can be particularly troublesome in large organizations where the volume of transactions is high. A 2-3% error rate might not seem like much on a small scale, but when applied to thousands or millions of transactions, the impact can be substantial. Moreover, correcting these errors can be incredibly time-consuming and therefore costly, often negating the efficiency gains that AI was supposed to bring in the first place.
Why Does AI Make These Mistakes?
AI’s strength lies in its ability to learn from data and make predictions based on patterns. However, this also means that AI systems can be tripped up by anomalies, unusual patterns, or incomplete data. In the context of cash application, an AI might struggle with payments that don’t fit the expected patterns or with invoices that have unusual terms. Additionally, if the training data the AI was built on wasn’t comprehensive enough, it might not perform well in real-world scenarios that differ from its training environment.
Furthermore, AI lacks the contextual understanding that a human accountant has. For example, a human might recognize that a payment marked with a certain reference number should be applied to a specific invoice, even if the AI model hasn’t seen that pattern before. This kind of nuanced judgment is something AI is still working towards, and it’s why errors can creep in despite high overall accuracy.
The Way Forward: Human Oversight and Continuous Improvement
Given these challenges, it’s clear that while AI is a powerful tool for cash application, it’s not a silver bullet. Organizations looking to implement AI in this area must do so with caution. The key is to combine AI’s strengths with human oversight. Accountants should be involved in the process, not just to catch and correct errors, but to continually train and refine the AI system. This ongoing improvement process is crucial to reducing the error rate and ensuring that the system adapts to new patterns and anomalies as they arise.
Moreover, organizations should have robust error-handling processes in place. This includes flagging potentially mismatched payments for human review and having a system for easily correcting errors when they occur. By integrating AI into a well-designed workflow that includes human intervention, companies can enjoy the benefits of automation while mitigating the risks.
Conclusion: AI in Accounting – Powerful but Imperfect
AI has the potential to transform cash application, bringing new levels of efficiency and accuracy to a traditionally manual process. However, as with any powerful tool, it must be used carefully. The 2-3% error rate, while seemingly small, can have significant implications in the world of accounting, where precision is crucial. By combining AI with human oversight and continuous improvement, organizations can harness the power of AI while safeguarding against its limitations. In accounting, as in many other fields, AI is a game-changer—but it’s not a replacement for careful, thoughtful management.