The Green Sheet Online Edition
October 26, 2026 • 26:10:02
AI won't fix a broken payments back office
Artificial intelligence is rapidly changing the payments industry. AI is being used to detect fraud, automate customer interactions, analyze transaction patterns, assist with investigations and identify opportunities for greater operational efficiency. As organizations look for ways to reduce costs and manage increasingly complex payment activity, the potential of AI is difficult to ignore. But there is an important question payment organizations should ask before putting AI to work in their back office: Is the underlying operation ready for AI? AI can make a good operation better. It can automate a bad process. But it can't fundamentally fix a broken foundation.
The AI opportunity is real
There is enormous potential for AI in payment operations. Imagine a back office where AI can help identify unusual transaction patterns, prioritize reconciliation exceptions, assist employees in researching transactions, identify potential fee discrepancies or help determine why a settlement position doesn't balance. These applications could save significant time and allow payment professionals to focus on higher-value work. But each of them depends on something that is often overlooked: access to reliable, connected and meaningful data.
AI is only as useful as the information it can access and the processes surrounding it. If the data needed to answer a question is scattered across multiple systems, AI has a more difficult problem to solve. If transaction information is incomplete or inconsistent, the quality of the resulting analysis can suffer. And if the underlying workflow is inefficient, AI may simply make that inefficient workflow faster.
The data problem behind the AI problem
Payment organizations generate enormous amounts of data. Every transaction can produce information related to authorization, reconciliation, adjustments, fees, settlement, disputes and more. The challenge isn't necessarily a lack of data. It is often where that data lives and how easily it can be connected. In a fragmented environment, transaction information may reside in multiple applications, databases and operational systems. Different systems may have different data structures, identifiers and processing schedules. An employee investigating a transaction may have to move between several applications just to understand what happened.
Now consider asking AI to perform that same investigation. AI doesn't eliminate the underlying fragmentation. It has to navigate it. A modern AI strategy therefore begins with a fundamental question: Can your organization provide AI with the right information in the right context?
You can't automate what you can't see
Visibility is one of the fundamental requirements of effective payment operations. Organizations need to understand what is happening across their transaction environment, including:
- Which transactions have processed?
- Which transactions haven't reconciled?
- Which exceptions require attention?
- Where are financial differences occurring?
- What fees were assessed?
- Which transactions have settled?
- Which transactions are associated with a dispute?
If answering these questions requires assembling information from multiple disconnected systems, there is a fundamental operational problem to address before AI enters the picture. AI may eventually help employees find the answer, but a better-designed back office can make the answer accessible in the first place. The goal shouldn't be to use AI to compensate for poor visibility. The goal should be to create the visibility that allows AI to deliver even greater value.
AI needs transaction context
Payment transactions don't exist in isolation. A transaction that begins with an authorization can continue through a complex lifecycle involving reconciliation, adjustments, fees, settlement and potentially a dispute. Understanding one part of that lifecycle often requires understanding the others.
For example, identifying a reconciliation exception may require more than comparing two amounts. It may require understanding the original transaction, associated fees, settlement activity, currency, adjustments and other attributes. Likewise, investigating a dispute may require connecting the dispute to the original transaction and the activity surrounding it. I can be extremely helpful in analyzing this information, but first, the information needs to be connected. A back office that maintains a connected view of the transaction lifecycle provides a much stronger foundation for intelligent automation than one where information is isolated in functional silos.
Don't put AI on top of bad processes
There is another temptation when organizations adopt new technology: automate the process they already have. If employees manually review thousands of exceptions, the instinct may be to use AI to review those exceptions faster. But perhaps the more important question is: Why are so many exceptions being generated in the first place?
If reconciliation requires excessive manual intervention, organizations should examine the underlying matching rules, data quality and process design. If employees spend hours researching transactions across multiple systems, AI might help them search faster, but a centralized transaction repository and better lifecycle visibility could eliminate much of that research in the first place.
Similarly, if fee management requires extensive manual analysis, AI might identify patterns, but flexible and configurable fee rules can prevent many problems before they occur. AI should not become a substitute for process improvement. It should become an extension of it.
The modernization sequence matters
For payment organizations considering AI, modernization doesn't have to mean replacing everything at once. But there is value in thinking about the sequence:
- Connect the data. Create a consistent view of the information needed to manage the payment lifecycle.
- Improve the processes. Identify unnecessary manual steps, disconnected workflows and sources of recurring inefficiencies.
- Automate what makes sense. Use technology to eliminate repetitive work and reduce opportunities for error.
- Apply intelligence. Once reliable data and effective processes are in place, AI can help organizations identify patterns, make recommendations, prioritize work and support decision-making.
This creates a stronger progression:
Connected data — Better processes — Automation Intelligence
Rather than:
AI — Hope it fixes everything
The back office is an AI opportunity
None of this means payment organizations should wait to explore AI. Quite the opposite. The back office may be one of the most promising areas for practical AI applications because it contains enormous amounts of structured transaction data and numerous processes that involve research, analysis, matching and decision-making.
Potential applications include:
- Reconciliation and exception management
- Transaction research
- Fee assessment
- Settlement processing
- Disputes management
These are all areas where intelligent automation could potentially create significant value. But the organizations that benefit most may not be those that simply adopt the newest AI technology. They may be the organizations that first create the operational foundation that allows AI to work effectively.
The foundation matters more than the hype
Every new technology cycle creates pressure to adopt the latest innovation. Today, that innovation is AI, and there is good reason for the excitement. But payment processing is too critical to approach technology as a collection of disconnected solutions. A payment organization can have sophisticated AI capabilities and still struggle with basic operational problems if its transaction data is fragmented, its processes are overly manual and its systems cannot provide a connected view of the payment lifecycle.
AI doesn't eliminate the need for a strong payments back office. It makes having one even more important. The future isn't about choosing between a modern payment infrastructure and AI. It is about building payment operations where the two work together. The organizations that get the most value from AI will likely be those that recognize a simple principle: Before you make your payments back office intelligent, make sure you make it connected, visible and operationally sound. 
Michael Meeks, chief technology officer for BHMI, is responsible for the company's strategic technical development, software development standards, and software management procedures. BHMI is a leading provider of software solutions focused on the back-office processing of electronic payments. The company is best known as the creator of the Concourse Financial Software Suite, an integrated collection of back-office products that allow companies to adapt to the rapidly changing world of payments. Michael can be reached at mike.meeks@bhmi.comor at 402-333-3300.
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