How VISA used in-memory analytics for better customer experience

When card issuers first started using automated business rules software to counter debit and credit card fraud, the limits on that technology were quickly evident: Customers reported frustrating payment rejections on dream vacations or critical business trips.

Visa works with its clients to improve customer experience by providing cutting-edge fraud risk tools and consulting services that make its strategies more effective. Through this approach, Visa enhances customer experience and minimizes invalid transaction declines.

The company’s global network connects thousands of financial institutions with millions of merchants and cardholders every day. It has been a pioneer in cashless payments for more than 50 years.

By using SAS® Analytics, Visa is supporting financial intuitions to reduce fraud without upsetting customers with unnecessary payment rejections. Whenever it processes a transaction, Visa analyzes up to 500 unique variables in real time to assess the risk of that transaction. Using vast data sets, including global fraud hotspots and transactional patterns, the company can more accurately assess whether you’re buying escargot in Paris, or someone who stole your credit card.

“What that means is that if you are likely to travel we know it, and we tell your financial institution so you’re not declined at the point of sale,” says Nathan Falkenborg, Head of Visa Performance Solutions for North Asia. “We also will assist your bank in developing the right strategies for using the Visa tools and scoring systems,” he adds.

Visa estimates that big data analytics works; state-of-the-art models and scoring systems have the potential to prevent an incremental $2 billion of fraudulent payment volume annually.

A globally recognized name, Visa facilitates electronic funds transfer through branded products that are issued by its thousands of financial institution partners. The company processed 64.9 billion transactions in 2014, and $4.7 trillion in purchases were made with a Visa card in that same year.

It has the computing capability to process 56,000 transaction messages per second, which is greater than four times the actual peak transaction rate to date. Visa doesn’t just process and compute – it is continually using analytics to share strategic and operational insights with its partner financial institutions, and assist them in improving performance.

As an example of marketing support, Visa has assisted clients globally in identifying segments of customers that should be offered a different Visa product. “Understanding the customer lifecycle is incredibly important, and Visa provides information to clients that help them take action and offer the right product to the right customer before a value proposition becomes stale,” says Falkenborg.

How can using in-memory analytics make a difference?

In a recent proof-of-concept, Visa used a high-performance solution from SAS that relies on in-memory computing to power statistical and machine-learning algorithms and then present the information visually. In-memory analytics reduces the need to move data and perform more model iterations, making it much faster and accurate.

Falkenborg describes the solution as like having the information memorized, versus having to get up and go to a filing cabinet to retrieve it. “In-memory analytics is just taking your brain and making it bigger. Everything is instantly accessible.”

Ultimately, solid analytics helps the company do more than just process payments. “We can deepen the client conversation and serve our clients even better with our incredible big data set and expertise in mining transaction data,” says Falkenborg. “We use our consulting and analytics capabilities to assist our clients in tackling business challenges and protect the payment ecosystem. And that’s what we do with high-performance analytics.”

“The challenge that we have, as with any company managing and using massive data sets, is how we use all necessary information to solve a business challenge – whether that is improving our fraud models, or assisting a client to more effectively communicate with its customers,” elaborates Falkenborg. “In-memory analytics enables us to be more nimble; with a 100X analytical system processing speed improvement, our data and decision scientists can iterate much faster.”

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