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Applying Payment Analytics to Business

By Elena Malykhina

Payment transactions are a vital part of doing business, but the importance of payments extends beyond a simple transaction. As the usefulness of payment analytics becomes clearer to businesses, more companies are beginning to collect payment data—but many don’t yet know how to leverage it, experts say.1

Payment analytics typically involve consolidating payment data from various sources—such as online, mobile, card, cash, or check—to one centralized platform.2 When used effectively, payment analytics can help businesses gain insights into revenue, costs, and payment trends; make more informed business decisions; and even prevent fraudulent activity.


What are Payment Analytics?


Whether credit, debit, or wire transfer, all digital payments transfer vital information from one party to another. This data can be analyzed and used to garner insights and measure business performance.3


But as payment platforms continue to evolve and grow in complexity, businesses are faced with the challenge of managing (and understanding) these troves of data. Put simply, there are too many parameters for a human mind or a simple dashboard to efficiently make sense of them all.4


That’s where data analytics comes in. Payment analytics tools are designed to provide a single, consolidated view of a business’s payment processes. For example, they might let businesses access their data through reporting dashboards, mobile apps, or application programming interfaces.5


While some advanced payments analytics tools may require hardware, extensive training, or changes to a company’s existing infrastructure, simpler web-enabled tools are on the rise. These easier-to-use platforms can provide even the most basic businesses with transaction data online anywhere, anytime. Such tools may also come with an audit functionality to give businesses better visibility into spending. Others offer location-based analytics to detect fraudulent transactions.


How Businesses Can Use Payment Analytics


Before turning to payment analytics tools, businesses may wish to identify what they hope to accomplish. Common reasons to use payment analytics include:


  • Gaining insight into costs,
  • Improving cash management,
  • Enabling more efficient payments,
  • Monitoring compliance with contract terms,
  • Comparing spending patterns across different company units, and
  • Reducing overall spending.6

Businesses can also use payment analytics to proactively identify and prevent risks related to fraud and overall data security. For example, analytics tools can discover patterns to detect anomalies that might indicate fraudulent activity.7


Types of Payment Analytics Tools and Strategies for Businesses


In addition to the various web-based payment analytics tools mentioned above, businesses can build more specialized and customized analytics. However, more complex systems may require building an environment that sits in the cloud, since cloud computing can support large volumes of data and provide access to information from any location or device.8


For cost-conscious small and midsize enterprises (SMEs), there are other alternatives on the market—such as solutions that combine cloud services with data science, behavioral analytics, and finance.9 For example, one service taps into a company’s financial health, customer demographics, and other sources of data to help smaller businesses adjust their cash flow by advancing payments for unpaid invoices.


What’s more, understanding payment activity may be a critical part of customer relationship management (CRM), according to one business technology industry expert.10 By intersecting invoicing, payments, and CRM, businesses can use transaction data to analyze customer relationships. The analysis can be as simple as an overview of how much a customer has paid over time, or as complex as triggering custom messages to customers who have missed a payment.


Analytics Trends in the Payments Industry


Thanks to the growth of artificial intelligence and machine learning, current trends suggest that businesses will have more opportunities to use payment analytics to extend targeted offers to customers.


Another area where artificial intelligence and machine learning have become critical is fraud detection, as companies struggle to keep up with the rate at which fraudulent activity grows—and gets more sophisticated.11 Using machine learning models, payment analytics systems can make informed decisions about whether to approve, block, or mark a transaction for manual review.12


And it doesn’t seem like the payments analytics industry will slow any time soon. According to Capgemini’s 2019 Top-10 Trends in Payments report, countries around the world are modernizing their payments to handle transactions that are seamless and rich in data. As a result, efforts to capitalize on transaction data are on the rise globally.13


Payment analytics can be useful to businesses of all sizes, but the sheer volume of data in today’s digital environment can make it hard to efficiently garner insights by hand. Payment analytics platforms and strategies can help companies consolidate payment data into a centralized platform, process data accurately, and therefore be better equipped to make informed business decisions and detect fraud.

Elena Malykhina - The Author

The Author

Elena Malykhina

Elena Malykhina is professional writer who has covered science, technology and business for more than 10 years. Her work has appeared in InformationWeek, Scientific American, Newsday, The Wall Street Journal and Adweek, as well as through the Associated Press.


1. “Growing Your Business Through Payments Analytics,” Payments Journal;
2. Ibid.
3. Ibid.
4. “18 Companies Pushing The Envelope in Payments Analytics,” MEDICI;
5. “Insightful Sales Intelligence for a Bird’s Eye View of Your Business,” BlueSnap;
6. “Payment Analytics Turn Invoices Into Insights,” WEX;
7. “Growing Your Business Through Payments Analytics,” Payments Journal;
8. “Payment Analytics Turn Invoices Into Insights,” WEX;
9. “18 Companies Pushing The Envelope in Payments Analytics,” MEDICI;
10. “Getting CRM Technology Up To Speed With SMB Payments,” PYMNTS;
11. “A Beginner’s Guide to Machine Learning in Payment Fraud Detection & Prevention,” Nethone;
12. Top-10 Trends in Payments: 2019, Capgemini;
13 Ibid.

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