The payments industry is at a turning point. As the volume of digital transactions grows, companies are facing new challenges in fraud detection, regulatory compliance and customer expectations. But one trend is becoming clear: managing data well is no longer a back‑office task. It is now central to business performance.
Many organizations are still working with old systems that were not built for today’s demand. These systems can slow down operations, make it harder to see risks in real time and limit a company’s ability to innovate.
Industry watchers say that the companies that succeed in the next decade will be those that rethink how they collect, govern and use data.
Recent research supports this view. A McKinsey report released in 2025 shows that payment firms that improve their data capabilities are twice as likely to increase revenue growth as those that do not. The same report notes that industry leaders are investing heavily in cloud‑based systems and real‑time data tools to stay competitive.
In this transition, experts who understand both the business and technical sides of data are in high demand.
Data Is Moving to the Center of Business Strategy
Himanshu Shah, who leads data engineering strategy at a major payments organization, says that data has become the backbone of how payments firms operate.
“Data used to be something that supported reporting or analysis after the fact,” he says. “Now it has to support decisions in real time. That changes how companies must think about their systems.”
Shah explains that payments companies today need to focus on two areas. First, they must build systems where data is clean, reliable and easy to use. Second, they must connect that data across teams so that risk, finance and operations groups can make faster, coordinated decisions.
He explains this with a simple example. In the past, a fraud team might look at transaction data overnight. Today, fraud decisions must happen in milliseconds. That means the data pipelines must be fast and the data itself must be trusted. If the data is incomplete or out of sync, the fraud team may make the wrong call.
Real‑time decisioning is becoming a requirement. According to industry surveys, more than 60 percent of payments firms see real‑time fraud detection as a top priority for the next three years.
The Shift From Pipelines to Products
One of the most talked about changes in data strategy is the idea of treating data as a product. This means thinking of datasets not just as something that moves from one system to another, but as something designed to be reliable, well documented and reusable by many teams.
Shah says this approach makes data easier to use across the company. Finance, sales, risk and compliance teams can all work from the same trusted source of information. This reduces confusion and speeds up decision‑making.
When teams use different versions of the same data, they can reach different conclusions. That can slow down approvals, increase disputes and even raise regulatory concerns.
Treating data as a product also means setting clear standards for access and quality. Shah explains that this includes tracking where data comes from and how it changes over time.
This helps teams understand why a number looks the way it does. It also helps auditors and regulators see that the company is treating data seriously.
Challenges With Regulation and Compliance
The regulatory environment for payments is changing rapidly. In the United States, Europe and Asia, new rules require greater transparency, better data protection and stronger reporting controls.
These regulations are well intentioned, but they also make it harder for companies with outdated systems to keep up.
Shah notes that many companies struggle with compliance because they do not have a consistent view of their own data. If different teams interpret data differently, it becomes hard to respond quickly when regulators request reports or evidence.
Cloud‑based data platforms can help. They allow companies to consolidate data into a single source of truth, apply consistent rules and show a clear audit trail, a key requirement in many regulatory frameworks.
AI: A Source of Opportunity and Risk
Artificial intelligence is another major driver of change in payments. Firms are exploring AI tools to improve fraud detection, personalize customer experiences and predict customer behavior.
But Shah says success with AI depends on strong data foundations.
“AI models are only as good as the data they use,” he says. “If your data is messy or incomplete, the model will be too. That can lead to bad decisions or even increased risk.”
For example, if an AI fraud model is trained on incomplete data, it may miss new fraud patterns or wrongly flag legitimate customers. That can harm the customer experience and expose the firm to financial loss.
To prevent these problems, payments firms are focusing first on building reliable, governable data systems. Only then are they layering AI tools on top.
The Road Ahead for Payments Data
Experts say that the payments industry is still early in this transition. Many companies have started moving to cloud‑based data platforms, but few have reached the point where data is fully governed and trusted across all teams.
Those that do succeed will have several advantages. They will be able to detect fraud faster, comply with regulations more easily, and make better business decisions based on real‑time information.
Shah believes that this evolution will define the next era of payments.
“We are moving from technology that supports operations to technology that drives decisions,” he says. “That requires a new way of thinking about data, not as something you store, but as something that powers every part of the business.”
Whether the industry reaches that point quickly or slowly, one thing is clear: data strategy is now central to the future of payments.