Have you ever sat at your desk, squinting at siloed spreadsheets from multiple gateways, acquirers, and wallets? You scroll, cross-check, and calculate, yet you still don’t know why your authorization rate dipped last week.
Well, you’re not alone.
For enterprise and mid-market merchants, payments leaders, and finance teams, fragmented systems and inconsistent data are making it harder than ever to track performance and control rising scheme fees.
In this PaymentsEd webinar, we unpack how modern AI can transform chaotic, multi-provider payment data into clear, actionable intelligence.
We explore the realities of modern payments data with practical insights from Andrew Sjogren, Director of Product Marketing at IXOPAY, and Marco Conte, Founder of Congrify. Together, they explain how data and AI can reduce manual effort, helping payments teams make smarter and faster decisions.
The Merchant Reality Check: Payments Data Is More Complex Than Ever
Merchants consistently want two things: standardized performance reporting and clear visibility into their total cost of payments.
What they struggle with most is getting actionable insights from raw, disconnected data. And across the board, the heaviest operational burden remains manual, resource-intensive reporting and reconciliation.
Andrew Sjogren notes that this complexity is accelerating with time. As merchants expand their payment stacks, the underlying data becomes harder to unify.
Each new connection adds its own data schema. It also brings its own response codes and its own reporting cadence. This is true whether you integrate a gateway, an acquirer, a wallet, or a fraud tool.
Over time, these layers of mismatched formats create silos that resist simple mapping or interpretation. Marco Conte points out that even a single provider may deliver multiple files per day, each structured differently, making reconciliation and performance monitoring increasingly cumbersome.
The result is a landscape where payment data is abundant but difficult to trust, compare, or act on at scale. Which is precisely the gap AI now aims to close.
Where All This Data Actually Comes From (and Why It’s Hard to Wrangle)
When you trace a single transaction end to end, you see how many touch points feed your payments data. For example:
Checkout: customer metadata, device signals, loyalty details
Fraud tools: device fingerprinting, velocity checks, risk scores
3DS flows: issuer responses, challenge data, authentication results
Gateways, acquirers, and processors: authorization fields, response codes, routing details
Settlement files: payout data, FX details, fees, timing variances
Each source contributes its own structure and cadence, making even a single transaction far more complex than it appears.
Marco Conte notes that 750 columns of data isn’t unusual for a merchant managing several providers.
This is where the real operational strain begins. You might think you’re dealing with one transaction, but you’re actually dealing with inputs from six or more stakeholders, all formatting and delivering data differently.
That complexity is why your team loses hours reconciling payouts, troubleshooting declines, and piecing together fee structures across markets.
When you look at your stack through this lens, the bottlenecks make sense. You’re not struggling because you’re disorganized. You’re struggling because the data arrives fragmented, delayed, and inconsistent. AI’s value starts right here, by pulling these threads into something coherent enough for you to act on.
AI’s Role: Moving From “Data Everywhere” to “Insights on Demand”
Payments teams are already drowning in files, formats, and delays. As such, you don’t need more data. You need automation, pattern detection, and guidance. AI can help turn that chaos into something usable, fast.
1. Standardizing and unifying multi-provider data
AI first restructures what you receive from gateways, acquirers, fraud tools, and settlement files. Providers deliver multiple files per day with inconsistent schemas, and manually aligning them is impossible at scale. Automated pipelines normalize these inputs so you finally get a single, trustworthy view of each transaction. This alone cuts hours of reconciliation and removes the need to stitch spreadsheets together.
2. Identifying anomalies quickly
After data is unified, AI models continuously scan for unusual patterns. Issues you’d never notice on your own can surface immediately. For example, a small regional payment method might drop for days without anyone realizing it.
Instead of discovering misses in a month-end review, you receive near-real-time alerts on routing issues, issuer anomalies, performance drops, or rising fees. It’s proactive protection, rather than reactive cleanup.
3. Reducing manual investigation by providing natural-language answers
AI also accelerates daily decision-making. Rather than digging for data, you ask questions in plain language and get instant answers.
This shrinks multi-hour investigations into seconds. Removing much of the friction that slows down payments, risk, and finance teams.
What This Means for You
AI shifts your workload from chasing data to acting on insights.
CFO questions answered in minutes, not hours. You can surface fees, trends, declines, or anomalies instantly.
Real-time anomaly detection across dozens of providers. Issues surface early, long before they impact KPIs.
A future where humans focus on strategy, not stitching CSV files together. Your time goes to optimization, not manual reporting.
AI transforms fragmented data into “insights on demand,” giving you clarity and control in an environment where complexity only grows with time.
The Practical Roadmap: How Merchants Actually Build Payments Intelligence
Marco describes payments intelligence as a staircase: each step building on the last, no matter where you begin. His model gives merchants a clear maturity path, whether you’re just centralizing data or already experimenting with AI-driven optimization.
Here’s the journey, distilled:
Centralize & warehouse your data
You start by bringing everything into one place: your data lake or warehouse (ClickHouse, Snowflake, etc.). Without this layer, nothing else works.Basic reconciliation & matching
You confirm you’re actually getting paid. This covers transaction matching, payout checks, and resolving mismatches – your operational foundation.Foundational payments analytics
Once reconciliation is stable, you track core KPIs: authorization rates, drop-offs, refunds, disputes, and basic fee metrics.Customer-level KPIs
You mature beyond transactions to understand customer behavior: lifecycle patterns, retention, repeat declines, and multi-attempt success rates.Fee intelligence
This is where complexity spikes. You break down interchange, scheme fees, FX, and penalties. You then build allocation models that show why costs occur, not just the totals.Anomaly detection
Machine learning and statistical models (not just LLMs) watch your data for unexpected patterns. These might include issuer anomalies, routing failures, geographic shifts, and more.Agentic automation & AI copilots
With clean data and reliable models, you begin automating. AI agents handle repetitive analysis, surface insights, and answer questions in natural language.
Together, these steps form a practical, achievable roadmap. One that turns fragmented payments data into a continuously improving intelligence layer for your business.
What Industry Leaders Are Seeing: ROI, Fees, and the Reality of Building In-House
If you’re struggling to justify investment, start by quantifying today’s operational hours, inefficiencies, and penalties. Industry leaders are doing exactly that. And the patterns that emerged in the webinar Q&A echoed what many merchants discover, once they look closely at their own data reality.
Here are the key takeaways:
ROI shows up in three places: lower fees, fewer manual hours, and better performance.
Leaders consistently link ROI to savings on scheme fees and penalties, reduced reconciliation hours, and revenue lift from fixing avoidable decline patterns. Once your data is centralized and monitored, these savings surface quickly.Building payments intelligence in-house is far more expensive than most expect.
Merchants often assume they can “just hire a data team.” In reality, you’re looking at hundreds of thousands of dollars, multi-year build cycles, and the ongoing work of data engineering, data science, payments expertise, and product management. Even large enterprises take 5-7 years to reach a stable version. And that’s before layering in AI capabilities.Data latency is a universal challenge. You must monitor ingestion, not just outputs.
PSPs deliver files on different schedules, sometimes with delays of hours or even days. Payment leaders stressed the need for threshold-based monitoring, so you know immediately when data is late or incomplete, rather than discovering gaps during month-end reporting.LLMs won’t replace machine learning. They interpret results; they don’t detect anomalies.
Statistical models and ML handle pattern recognition. LLMs sit on top, helping you explain anomalies, summarize trends, and answer questions in plain language. If you’re expecting an LLM to spot routing failures or issuer shifts, you’ll miss critical events.
If you’re making the case internally, start with a simple exercise:
How many hours does your team spend today on reconciliation, reporting, investigation, and fee analysis?
Translate those hours (and the penalties or missed optimizations they hide) into dollars. That baseline becomes your clearest ROI story.
The Future of Payments Intelligence Is Agentic, Automated, and Merchant-Friendly
By 2026, payments teams will expect AI agents that detect issues, summarize insights, and surface recommendations before anyone asks. Industry research shows that AI in payment processing, especially in automation and fraud detection, is expected to cut costs by up to 30%.
For merchants, this shift means spending less time stitching spreadsheets together and more time improving performance and reducing costs. As data pipelines stabilize and AI capabilities mature, payments data finally becomes clear, predictable, and actionable.
To see the full conversation and live examples, don’t forget to watch the webinar.