Why the Most Valuable Part of a Payment Isn't the Money 

A payment may take seconds to complete. But its information can keep working long after the money has arrived. 

Think about a business making a payment to a supplier. The money moves from one account to another and the transaction is complete. 

But that payment also tells a larger story. It reveals who paid whom, when, how much, how often, and sometimes what the payment was for. Combined with thousands of other payments, those small pieces of information can reveal whether a business is growing, whether a supplier is under pressure, whether inventory may soon run short—or whether something looks like fraud. 

The payment has taken two journeys: the money has reached its destination; the information has started a second journey.

Sofia's Café Knows More Than She Does 

Every morning, before Barcelona's narrow streets begin to fill with tourists, Sofia unlocks the door to her little café. 

The day unfolds in a familiar rhythm. A commuter taps a phone to buy coffee. A student pays for breakfast. A supplier delivers fresh bread and receives payment. Later, Sofia pays her staff, settles an electricity bill, and orders more coffee beans. 

By closing time, hundreds of payments have flowed through her business. Sofia sees individual sales: €3.50 for a coffee, €12 for breakfast, €18 for lunch. To Sofia, they are simply transactions. Some are large, most are small. She remembers the customers, not the payments. 

Her payment system sees something much bigger. Over several months, it notices that weekday morning payments are becoming slightly larger. More customers are buying breakfast rather than just coffee. Friday evening sales are rising. Weekend payments from a particular neighbourhood are increasing. 

None of these changes is dramatic enough for Sofia to notice. Together, however, they tell her something useful: her customers' habits are changing. 

She could respond by changing staffing, adjusting her breakfast menu, ordering more stock for busy periods or offering promotions when the café is quieter. 

The payment hasn't just recorded a sale. It has created a small piece of intelligence about the business. 

From One Café to an Entire Economy 

Sofia's café is a small example of a much bigger idea. When millions of payments are viewed together, they can reveal changes that may not yet be visible in conventional economic statistics. 

A sudden rise in restaurant spending might point to a tourism boom. If people start spending less on restaurants, travel or other non-essential items, it could be an early sign that households are tightening their budgets. Changes in where and what visitors spend can also show which areas of a city are attracting people and where tourism money is actually flowing. 

This is no longer just a theoretical possibility. Visa now offers governments a dedicated Government Insights Hub that uses anonymised and aggregated payments data to provide snapshots of economic and consumer behaviour. Its current capabilities include tourism intelligence, small-business insights and economic analysis, with spending trends that can be examined by geography, sector, consumer group and transaction type. Visa says its network processes more than 276 billion transactions annually. 

There are real examples behind the idea. In New Zealand, an experimental dataset based partly on electronic card transactions was incorporated into the government's Tourism Recovery Dashboard, helping track visitor-spending recovery and identify which visitor groups were driving it. In Australia's Gold Coast, VisaNet transaction data was used in a tourism insights dashboard to assess the economic impact of events and understand how spending flowed into surrounding areas—information that could also support infrastructure and service decisions. 

And Visa's work has moved beyond tourism. Its current economic-insights offering is designed to help governments understand consumer-spending momentum and regional and sector-level changes—essentially giving policymakers a faster view of what people are actually doing with their money.  

The payment data doesn't predict the future. It can show the future beginning to take shape. 

Carlos Never Looked at Inventory 

As a Procurement Manager for a food distribution company in São Paulo, Brazil, Carlos is responsible for keeping the company's shelves stocked. 

One morning, Carlos's procurement dashboard flags a pattern. Sales of cooking oil and packaged food are rising faster than usual, while supplier invoices show that replenishment is taking longer. The payment data adds another clue: Carlos's payments to those suppliers are also increasing as he places more frequent orders. 

Individually, none of these signals is unusual. Together, they suggest that demand is rising faster than the supply chain can comfortably respond. Carlos decides to bring forward his next few orders.  

Brazil's instant payment system, Pix, has transformed how quickly businesses can send and receive money. But the bigger opportunity lies in what businesses can learn when payment information is combined with sales, invoicing and inventory data. These combined signals can help them spot changes in demand, manage working capital and respond earlier to potential supply problems. 

A few weeks later, several suppliers struggle to replenish their stocks. Some of Carlos's competitors run short of popular products. His company doesn't. 

The system didn't predict the shortage. It connected signals that Carlos would have struggled to see by looking at individual transactions. 

Emily Is Watching the Cash, Not the Payments 

The same idea works for something less visible than inventory: cash. 

In London, Emily is a treasury analyst at a multinational company. Every day, money comes in from customers and goes out to suppliers, employees and other businesses across several countries.  

A traditional cash forecast might depend heavily on spreadsheets and information that arrives after transactions have already happened. A modern treasury system can do much more. It can bring together incoming payments, supplier payments, invoices, foreign-exchange activity and other financial information. AI and analytics can then look for patterns and estimate where the company's cash position is heading. 

One Friday, Emily's dashboard shows that the company's expected cash position several weeks ahead has weakened. She investigates. Several overseas subsidiaries have accelerated payments to suppliers because they are trying to secure materials for new contracts. 

The payments have already happened. But their second journey has just begun. Emily adjusts the company's funding plans and foreign-exchange positions before the cash shortage becomes a problem. 

Payment data is therefore moving beyond reconciliation. It is becoming part of liquidity intelligence. 

Financial technology companies and banks already offer cash-flow forecasting and money-movement analytics that use transaction and invoice information to help corporate treasurers anticipate liquidity needs. Companies such as J.P. Morgan and Kyriba operate in this broader space. 

Rebecca Is Looking for Something That Doesn't Look Right 

Now consider the same information from the other side. 

In Singapore, a fraud investigator doesn't want to know what looks normal. She wants to know what suddenly doesn't. 

Imagine money moving rapidly through several accounts. Or a customer who normally makes small household payments suddenly sending money to a new beneficiary. Or many apparently unrelated accounts making small payments to the same merchant. 

A single transaction may not trigger an alarm. A pattern might. This is where richer payment data becomes particularly valuable. 

The Monetary Authority of Singapore has been exploring the use of transaction data from multiple banks to identify suspicious patterns and higher-risk transactions earlier. The broader idea is simple: fraud can be difficult to see when each bank sees only its own piece of the puzzle. Bringing appropriately protected information together can make the pattern easier to spot. 

The same principle is being applied by fraud-fighting systems around the world. Payment history can help identify mule-account networks, unusual beneficiary relationships, sudden changes in spending behaviour and other signals that traditional rules may miss. And AI makes it possible to examine these patterns at a scale no human investigation team could manage manually. 

The payment is no longer just evidence of what happened. It can become an early warning of what might happen next. 

Priya Didn't Need to Email Her Bank Statements 

In Pune, Priya runs a small engineering company supplying precision components to larger manufacturers. Business is growing, and she needs additional working capital to fulfil a new contract. 

A few years ago, applying for a loan might have meant downloading bank statements, collecting invoices, scanning tax returns and emailing documents while the bank tried to piece together her financial picture. 

This time, the process is different. Her bank asks a simple question: "Would you like to securely share your financial information?" 

Priya agrees. 

Her information can now be shared with the bank through India's Account Aggregator framework, with her consent. An Account Aggregator does not become a giant warehouse of everyone's financial information. Its role is to enable customers to securely share information held by regulated financial institutions with another regulated institution when they permit it. 

The important word is consent. Priya remains in control of her information throughout the process.  

For the bank, the benefit goes beyond convenience. Instead of looking at isolated documents, it can understand how Priya's business actually operates—its cash flows, seasonal patterns, repayment behaviour and financial health.  

The loan officer still makes the decision. But now, the decision is based on a much richer picture. The payment has completed its first journey. Its information is helping with the second. 

Priya's experience is no longer just a glimpse of where lending could be heading. India is already doing this at remarkable scale. In FY 2025–26, the State Bank of India used AI to underwrite ~₹1trillion in Micro, Small, and Medium Enterprises (MSME) loans, The bank's digital underwriting draws on financial and business information to assess borrowers and automate parts of the credit decision process. 

The significance is not simply that AI can process more loans. It is that information generated through a business's financial activity can be turned into a usable picture of its creditworthiness—at a scale that would be difficult to achieve through traditional paperwork and manual assessment alone. 

This is one reason alternative data is becoming increasingly important in lending. Payment behaviour, utility payments and other financial information can help lenders assess customers who may not have a long traditional credit history. Digital lenders in markets including India and Africa have been using consent-driven financial information to speed up lending decisions. 

Why the World Is Making Payments Richer 

This is also why the payments industry is changing in a way that is easy to miss. 

For years, the goal was straightforward: Move money faster, more cheaply and more safely. That goal hasn't disappeared. But a second goal has emerged: Make the information travelling with the payment more useful. 

This is one reason ISO 20022 matters. It allows payment messages to carry richer, more structured information than many older formats. The benefit isn't simply better-looking messages. Better-structured information can make reconciliation, fraud analysis, compliance, liquidity management and automation more effective. 

Different countries are approaching the opportunity differently. 

India combines UPI with Digital Public Infrastructure and the Account Aggregator framework, creating ways for identity, payments and consented financial information to work together. 

Brazil is building on Pix alongside Open Finance, allowing customers to share financial information with authorised providers. 

Singapore combines PayNow and FAST with initiatives such as SGFinDex (Singapore Financial Data Exchange), which allows individuals to aggregate and share their financial information through a consent-based framework. 

Europe has PSD2 today and is moving towards PSD3 and the Payment Services Regulation, while GDPR and other data rules place strong boundaries around how personal information can be used. 

The United States is modernising payment infrastructure through FedNow and ISO 20022 migration, while its approach to open banking and data sharing remains more fragmented than in Europe or India. 

Nigeria has also been developing an open-banking framework and consent infrastructure around its large instant-payment ecosystem. 

The systems are different. The regulatory approaches are different. But the direction is remarkably similar. Payments are becoming information-rich events rather than simple instructions to move money. 

But Who Gets to Use the Information? 

This is where the story becomes more complicated. If payment information can improve fraud detection, lending, inventory management and economic forecasting, it is obviously valuable. 

But valuable information can also be sensitive information. 

A person's payment history can reveal where they shop, what they buy, how often they travel and, potentially, aspects of their lifestyle. A company's payment history can reveal its suppliers, customers, cash position and commercial relationships. 

So the second journey cannot simply be: Payment → Data → AI → Decision. There has to be another question in the middle: Who gave permission? 

This is why consent, purpose limitation, data minimisation, security and transparency are becoming as important as the technology itself. 

India's Account Aggregator model puts consent at the centre. Brazil's Open Finance framework operates under its LGPD (General Personal Data Protection Law) privacy regime. Europe's GDPR establishes strong rights around personal data. Singapore's PDPA (Personal Data Protection Act) and SGFinDex similarly emphasise controlled data sharing. Nigeria's open-banking framework includes consent requirements. 

The challenge will become even greater as AI systems start making decisions automatically. If an AI system uses payment information to decline a loan, flag a customer or predict a company's financial difficulty, the question will not simply be whether the prediction was accurate. 

It will also be: Was the data used fairly, lawfully and for a purpose the customer understood? 

The Payment Is Only the Beginning

  • Go back to Sofia's café. She thinks about today's sales. 
  • Carlos thinks about tomorrow's inventory. 
  • Emily thinks about next month's cash. 
  • Rebecca thinks about the next fraudulent transaction. 
  • Priya thinks about the next loan. 

They appear to be solving completely different problems. But all five are benefiting from the same underlying shift. 

Payments are becoming a source of trusted, contextual information that can be connected with other data to make decisions faster and smarter. That is why the quality of payment data matters. 

A payment that simply says "€100 transferred" tells us very little. 

A payment that carries reliable information about the parties, purpose, timing, references and relationships—and can be safely connected to other authorised information—can tell a much richer story.

And that is where AI becomes useful. AI doesn't create the intelligence from nothing. The payment ecosystem gives it something worth understanding. The better the data, the better the questions that can be answered. The better the questions, the better the decisions. And the better the decisions, the more valuable the payment becomes—not just as a way to move money, but as a source of insight. 

The first journey is still essential. The money has to get there. 

But the second journey is becoming impossible to ignore. The money moves once. The information keeps working.