Achieving 100% Uptime with DataStax
With DataStax Enterprise (DSE), ACI’s fraud prevention platform can process transactions quickly and securely. ACI uses the transaction data to add to its growing machine learning algorithm and intelligence, with no worry of downtime and with the assurance of easy scalability during high-traffic seasons.
Products & Services
Financial Services & Insurance
ACI Worldwide, Inc.
A global leader in payment systems software, Florida-based ACI Worldwide, Inc. powers electronic payments and banking for over 5,100 organizations worldwide, including 18 of the world’s 20 largest banks and more than 300 of the world’s largest retailers.
ACI’s solutions are used globally by banks, merchants, intermediaries, and corporations. ACI offers a wide range of transaction-generating endpoints, including ATMs, merchant point of sale (POS) terminals, bank branches, mobile phones, tablets, and eCommerce sites. The “Universal Payments” company, ACI, uniquely offers software for every aspect of a payment flow. From online payments to wholesale banking to fraud detection to queries and settlements, ACI offers integrated solutions using the wide variety of data services it has created over its 40-year history.
ACI handles fraud prevention for payment organizations worldwide via its payments risk management platform. ACI knows that whenever a transaction occurs—whether it’s a consumer transaction, a business transaction, or an internal bank transaction—it’s susceptible to fraud. Hence, any time finances are moved from point A to point B, ACI recommends the transaction go through a fraud detection layer. ACI’s powerful fraud detection solution acts as this layer and can be deployed on-premises or in the cloud.
With the number of eCommerce transactions rapidly increasing even more so during the holiday season. In recent years, while eCommerce transactions have grown by 19%, the fraud attempts on those transactions increased by 22%. A few years ago, one of every 109 transactions was a fraud attempt, compared to one out of every 85 today. Due to this alarming increase in transactions and fraud attempts, ACI sought a powerful, flexible database that could manage massive amounts of transactional data in real-time and mine it for mission-critical payments insights. The desired database needed to be horizontally scalable to handle the transaction volatility from seasonal surges like tax season and holiday shopping.
“We can see the trend of fraud is increasing, and when we combine that with the increasing rate of transactions, and the variability in the data, we very quickly get to a situation where we have to process larger amounts of data, larger numbers of transactions and utilize more complicated algorithms to determine whether or not a transaction is legitimate,” said Ken Chenis, Chief Architect at ACI Worldwide.
ACI’s software is so ubiquitous it’s safe to say that nearly every electronic payment, credit card, or debit card swipe involves its systems in some way. Although this persists as a huge responsibility, it also serves as an opportunity to use this incredible wealth of data to make every transaction, no matter where or how completed, a safe and fast transaction that adds to ACI’s powerful payments intelligence capabilities.
For ACI, finding a well-suited database required a lot of research and analysis.
ACI desperately needed a solution that allowed multi-cloud flexibility while simultaneously being easily scalable and capable of managing modern applications that were contextual, distributed, always-on, and “right now.” Any database utilized by ACI needed to be future proofed by how it allows ACI to quickly apply what it’s learning from its massive data intake to create new and innovative products and proofs of concept around transaction processing.
ACI reviewed several vendors, and after several proofs of concepts, the company selected DataStax Enterprise (DSE), the only active-everywhere database built on Apache Cassandra TM and designed for hybrid cloud. Through the acquisition of DSE, ACI has a highly performant complex event processing (CEP) engine, which deconstructs every element of a transaction—from the ATM used to the demographics of the person making the charge to the type of charge—to determine if it’s legitimate or potentially fraudulent.
DSE works in conjunction with ACI’s batch analytics engine and expands the use of its data management system for other valuable use cases, such as loyalty and rewards. The utilization of DSE allows for rapid innovation and shorter feature time to market. “Implementing the solution on DSE has been one of the easier technology adoptions for us; things worked the way they should,” Chenis said.
The use of DSE supports the goal of ACI’s payments intelligence platform- allowing vendors to determine a fraudulent transaction- without actually bothering the customer. Using DSE, ACI’s fraud prevention platform can process transactions quickly and securely while using the transaction data to add to its growing machine learning algorithm and intelligence, without downtime, and with the assurance of easy scalability during high traffic seasons.
Combining big data with real-time decision-making, ACI has used DSE to empower its fraud analysts with the potent combo of granular data and “the big picture” of what’s going on. In turn, this allows ACI to innovate at a much faster pace than before and to turn small innovations into proofs of concept that sometimes become major product breakthroughs.
The DataStax-backed solution saves customers millions while improving the fraud detection rate and the false-positive rate. And that’s just the beginning. “The fraud risk use case is just a starting point for these innovations,” Chenis said. “But, ACI is already looking at other opportunities to use this platform because the data is extremely rich. It has information across a wide variety of services, a wide variety of demographics, a wide variety of customers using all kinds of different onramps and off-ramps. It gets collected together in one common repository and that information is very powerful for meta use cases.”