AI has quickly become a priority for many financial institutions. The pressure to figure out where AI fits is real, but that can lead teams to start with the wrong question.
Many simply ask, “Where can we use AI?” A better approach is to start with, “Where is work harder or more manual than it needs to be?”
A practical starting point is to identify repetitive work, recurring exceptions, fragmented data, and manual handoffs before deciding which technology belongs in the process.
Payments and receivables are a good place to have that conversation. Every day, teams are accepting and processing payments, researching exceptions, reconciling information, moving files, answering payment-status questions, and tracking activity across systems.
Some of that work requires experience and judgment, but some of it follows repeatable patterns. Understanding the difference can help financial institutions identify where AI and automation could have a practical impact.
Get the checklist: Practical AI in Payments and Receivables
Financial institutions have plenty of potential AI use cases to consider, but not all of them are worth pursuing.
Before evaluating a new tool or launching a broader AI initiative, get specific about the problem you’re trying to solve. For example: Where are employees spending time on repetitive work? Which processes consistently create bottlenecks? Where are teams researching the same types of exceptions or moving information manually from one step to another?
These questions make it easier to determine what success should look like. Success might mean fewer manual touches, faster exception resolution, better visibility, fewer payment-status inquiries, or more employee capacity for work that requires judgment.
In a typical payment operation, what tasks do employees perform repeatedly? Where are people still reviewing, routing, researching, correcting, posting, or reconciling information before a transaction can move forward?
Maybe employees repeatedly correct the same type of payment exception. Maybe files need to be reviewed and routed manually. Or teams may be spending time sorting through a large volume of activity simply to determine what needs their attention.
AI tends to dominate the conversation, but some operational problems have much simpler solutions. For example, consider a process where an employee repeatedly receives the same type of information, checks it against a known rule, and sends it to the same next step. You may not need AI for that; rule-based automation could be the better fit.
The same applies to workflows involving predictable handoffs, approvals, posting steps, or exception queues. If the process already has clear decision points, workflow automation may be able to move the work forward more consistently.
AI may be more useful when the task requires help making sense of the work. For example, AI could help teams identify patterns within a high volume of payment activity, recognize recurring issues, prioritize which items need attention first, or surface exceptions for review.
The goal isn’t to apply the most sophisticated technology available but to match the technology to the work.
A promising AI use case can quickly become less promising if the underlying information is fragmented or inconsistent.
Before automating a workflow, look at the data feeding it and ask a few questions: Can teams see the payment activity they need? Is information consistent across systems and channels? Are exceptions, returns, and posting information readily available? Does the data provide enough context to reliably support the decisions or recommendations you expect technology to assist you with?
Sometimes the first step toward using AI effectively isn’t implementing AI at all but improving visibility, connecting workflows, or standardizing the information the process relies on. That groundwork can make future automation more useful and reliable.
Exception management is one area where automation becomes particularly relevant.
Not every payment follows the expected path. For example, information may be missing or incorrect. A transaction may need research. An account number may have changed. A payment may require someone to determine what happened before it can move forward.
The occasional exception is expected in payment processing, but recurring exceptions deserve a closer look.
If employees are correcting the same types of issues month after month, ask why. Could those exceptions be categorized more consistently? Could known issues be recognized earlier? Could the highest-priority items be surfaced first instead of requiring employees to sort through an entire queue?
Those are the kinds of operational questions that can reveal meaningful opportunities for automation or AI assistance.
Reducing manual work doesn’t mean removing people from payment operations. There are plenty of situations where human judgment should remain part of the process, particularly when a decision involves compliance, risk, client impact, approvals, or an exception that doesn’t fit an established pattern.
A more practical goal is to reduce the repetitive work surrounding those decisions. If technology can organize information, recognize a known pattern, prioritize an exception, or surface the right information for review, employees can spend less time sorting through work and more time resolving the issues that actually require their expertise.
The opportunity isn’t simply to automate more but to be more deliberate about what should be automated and what shouldn’t.
If you’re evaluating where AI and automation could make a practical difference in payments and receivables, use CheckAlt’s Practical AI in Payments and Receivables checklist to identify where manual work, repeatable workflows, recurring exceptions, and human judgment may point to opportunities for improvement.