The next chapter in payment orchestration: payment intelligence

As ecommerce expanded across channels and geographies, customers gained more ways to pay, from cards to digital wallets, bank transfers, BNPL, and real-time account-to-account options. The payments ecosystem grew more complex than ever, and that complexity now shapes how merchants build and manage their operations. Payment orchestration platforms emerged to manage that complexity: a single integration point connecting a merchant to acquirers, banks, gateways, and alternative payment methods, handling routing, retries, and reconciliation behind the scenes.  

For a while, that was enough. But the market is becoming increasingly crowded, so orchestration is no longer just about connecting PSPs and routing transactions. The biggest challenge now is proving measurable value beyond connectivity. Improvements in approval rates, reduced costs, automated operations, and help in expanding across borders are some of the things merchants now expect from orchestrators. But those expectations are growing against an increasingly fragmented ecosystem that keeps getting harder to navigate across local payment methods, Open Banking, wallets, and regulatory requirements.  

This is where the next battleground is emerging: intelligence. How does this look in practice? If an orchestration platform makes sure a transaction runs smoothly, the intelligence layer is what adds value on top, making the process and its decisions automatic by using data and AI in the merchant's favour.  

Recent developments in the market support this premise. In October 2025, IXOPAY acquired Congrify, with the goal of bringing AI-powered insights to global payment orchestration, tokenization, and compliance. In 2026, London-based fintech Primer has raised USD 100 million in a Series C round to accelerate AI development, while BR-DGE secured a GBP 10 million funding round for global expansion, focusing on building an intelligence and optimisation layer that helps merchants improve performance. These moves point to a broader shift, where orchestration providers are looking for value by making better use of the data they already hold. 

One concept, many definitions: what is payment intelligence? 

Every orchestration platform already sits on a wealth of raw data: issuer, route, cost, timing, outcome, retry behaviour, all logged transaction by transaction. What is changing is the expectation that this data should do more than explain what already happened.  

Orkhan Abdullayev, CEO and Co-Founder of Payrails, argues that the challenge has shifted from simply generating intelligence to ensuring that the intelligence is built on reliable data and can be acted upon.  

Historically, merchants struggled to establish a clean view of their payment performance. Data sat fragmented across multiple PSPs, acquirers, and internal systems, each with its own reporting formats, decline codes, and definitions. This made having a unified picture nearly impossible.  
Recent progress in AI has flipped this dynamic: generating intelligence itself is no longer the hard part. The focus has shifted to two things. First, what is the intelligence based on? Solving data fragmentation and quality to ensure insights are based on complete, normalised, trustworthy data rather than partial snapshots from different systems. 
Second, how to act on recommendations. Turning intelligence into solutions that actually unlock business value, whether that’s adding new processors or payment methods, optimising checkout flows or routing transactions in a certain way. Payment intelligence is therefore evolving from a reporting exercise into an operating capability: unified data in, automated action out. 

That distinction between knowing what happened and knowing what to do next runs through much of the industry's thinking about payment intelligence. It marks a shift away from treating payments as a purely operational function, and toward treating them as a source of ongoing insight, one that shows how providers are performing, where approval trends are shifting, and how behaviour differs across regions, so routing can follow real-time performance instead of static rules or old assumptions. What actually changes isn't the data itself, but the speed at which merchants can understand it and act on it.  

Payment intelligence tends to be one of those industry terms that everyone uses, but not everyone defines in the same way. For merchants, what it is supposed to do is shape decisions as they happen: picking the right PSP, adjusting routing, triggering authentication, setting fraud thresholds, or even matching the payment method to a given customer in a given moment.  

One complaint keeps surfacing from merchants: reporting tools from acquirers and PSPs are often descriptive rather than diagnostic. It is not enough to know that authorisation rates declined; merchants want to know why, and what levers are actually available to fix it. Others put it more sharply, framing a real distinction as one between orchestration with reporting and one with intent, as a merchant can have every processor connected and still be unable to answer something as basic as which one is dragging down approval rates in a specific country. 

BR-DGE introduces an important part of the conversation: benchmarking. 

Most large merchants run multiple PSPs, acquirers, and payment methods across markets, with each provider reporting performance separately and in their own way. The fragmentation this creates makes it incredibly difficult for merchants to really understand their payments performance at every level. 

That’s compounded, in BR-DGE’s view, by a lack of external benchmarks:

Merchants can usually see their own numbers, but not how those numbers compare to their sector - so it's difficult to know what 'good' looks like, where the issues are, and where the optimisation opportunities lie.
This gets harder during peak or unusual trading periods, when customer behaviour shifts fastest. Merchants need to build intelligence around normal trading patterns and what changes during peak trading, across payment method preference, fraud patterns, and provider performance. All behave differently under pressure, and merchants need to anticipate and proactively adapt their authentication, fraud, and routing strategies to keep the checkout flowing seamlessly. Payment intelligence, through centralised data, solves this by consolidating fragmented data into one benchmarked view, turning reactive firefighting into proactive, evidence-led decisions.

What it’s actually changing  

Payment intelligence changes what the industry can do with transaction data. IXOPAY, for instance, frames its own payments intelligence offering around this shift: moving merchants away from static, manually stitched-together PSP reporting toward a centralised intelligence layer that continuously unifies data, monitors for anomalies, and turns that into optimisation recommendations.  

Merchants make the same point: what makes the difference is the analytical layer offering transactions the best chance of approval, making a shift towards decisioning that adapts in real time. 

None of the underlying tools are new. Intelligent routing, performance monitoring, decline analysis, and reconciliation all exist already. What's actually been missing is the layer that connects them into something coherent. That's the gap AI fills: the ability to spot what would otherwise stay buried across millions of transactions and act on it immediately.  

When talking about payment intelligence versus traditional reporting, ProcessOut makes a related, more technical point about why static rules fall short in the first place: 

When first establishing a multi-PSP setup for redundancy and optionality purposes, merchants often experiment with building routing decisions based on traditional payment reporting. However, this approach can only ever drive static transaction routing rules, which entails a high exploration cost, does not adapt to real-time changes, and has to be repeated indefinitely to remain relevant. Payment intelligence applies an adaptive algorithm to make real-time routing decisions at the individual transaction level. By weighting near- and long-term historical patterns, the algorithm can exploit deeply nested performance differences. With this approach, merchants can make cost-based routing decisions without leaking performance. For instance, merchants can automatically prioritise low-cost local acquirers only where transaction profile performance predictions are evenly ranked, while reserving more expensive global gateways only when a transaction's complexity demands it. Ultimately, this approach elevates a multi-PSP strategy into a live auction house for trading performance against cost. 

Put those perspectives side by side, and the patterns hold regardless of who's describing it: the fragmentation and benchmarking gaps, the reporting-versus-intent distinction, all lead to the same place: payment intelligence moving beyond dashboards and into an active role in how payments perform. That shift is also increasingly commercial rather than operational: merchants are starting to treat payments as something that directly affects the bottom line, not just as a function.  

What’s coming next 

The distinction merchants keep coming back to is simple: analytics vs. intelligence. If the data isn't shaping a decision in real time, or at least feeding into a better one down the line, it's analytics. What's expected to change is the balance between the two: less time spent explaining what already happened, more spent shaping what happens next. The ones who win are the platforms that actually turn data into a decision automatically, rather than just reporting on it after the fact.  

As payment ecosystems stay always-on and merchants juggle speed, cost, and regulation simultaneously, payment intelligence looks less like a nice-to-have than a piece that's been missing. Orchestration shouldn't just connect the ecosystem: it should understand it, and adapt to what merchants actually need. Evaluating any vendor's claim here comes down to specifics, not labels: what does the system decide, on what data, and how is it measured? The same point comes up from the buyer side too: merchants increasingly want transparency on performance and total economics, and providers willing to be measured on results, not feature lists. 

About Paula Albu

Paula Albu has experience in content writing and editing, as well as being a creative storyteller. As a Junior Editor at The Paypers, she investigates Web3 technologies along with the latest trends and regulations in banking and fintech. Paula is committed to turning complex industry topics into engaging, accessible content that resonates with readers and creates a meaningful connection. She is available via LinkedIn or at paula@thepaypers.com.