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Build modern fraud detection platforms that protect your business.
With exponential growth in online transactions, detecting and mitigating fraud is now more complex than ever before. You need to handle AI and machine learning workloads, perform real-time statistical analysis, and provide consistently high write throughput at low latency. Redis Enterprise is the answer to fast, accurate detection of fraudulent transactions.
Today’s digital and mobile payments platforms are much more complex and distributed,which present many more software vulnerabilities. And they operate with a level of interconnectedness that traditional fraud detection techniques weren’t designed to address.
Using current software platforms, transactions are executed nearly instantly. That processing speed creates a great customer experience. But it also leaves banks and payment processors with less time to identify and prevent fraud.
Historically, personal identity information was verified with physical documents. That information is stored online, which adds speed and convenience. But the same data is easily accessible for a single big data breach to put millions at risk due to identity theft, account takeovers, and the creation of fake identities.
Financial services companies lose tens of billions of dollars to fraud attacks each year. Beyond direct losses, they experience financial pain in the form of fines, settlements, and erosion of trust and customer loyalty. The increased complexity, volume, and speed of today’s online transactions means your organization needs advanced fraud detection systems to keep up with malicious fraudsters.
Combating the use of stolen information requires maintaining up to date, real-time digital identities. Redis Enterprise can handle millions of daily updates to dynamic digital profiles. The data is returned with low latency, so that an application user is validated in real-time. Redis Enterprise also supports multiple data models to natively store the different types of identity elements. The result? Reduced complexity and lower costs.
Fraud detection systems use real-time transaction risk scoring algorithms to identify questionable purchases or payments. These systems consider transaction details, user profiles, behavioral biometrics, geolocation, IP/device metadata, account information, and more. Redis Enterprise serves real-time features for risk scoring model inferencing with sub-millisecond response latency. That means it keeps up with instant transactions and real-time applications, so you can ensure a great customer experience.
“Using Redis Enterprise in our fraud-detection service was an excellent decision for our organization. It is enabling us to easily manage billions of transactions per day, keep pace with our exponential growth rate, and speed fraud detection for all of our clients.”
Redis Enterprise has been benchmarked to handle more than 22 million read/write operations per second, at sub-millisecond latencies with only a 40-node cluster on Amazon Web Services. This admirable speed enables concurrent fraud detection inline with the transaction.
Redis Enterprise is available on all of the major cloud providers as a managed service or as software. It provides automation and support for common operational tasks. It also integrates with popular machine learning feature stores as well as with the platforms underpinning modern software architectures, such as containers and Kubernetes.
Redis Enterprise offers a cost-effective solution for hosting large datasets by combining DRAM, SSD (Flash), and persistent memory (such as Intel® Optane™ DC). It uses an innovative tiered approach that places frequently accessed hot data in memory and colder values in Flash or persistent memory. As a result, Redis on Flash delivers high performance similar to Redis on DRAM, while saving organizations up to 80% on infrastructure costs.
Bloom filters are probabilistic data structures used to determine if an item is part of a set. Probabilistic provides a fast, efficient implementation of Bloom filters that you can query to see whether a particular transaction is in a list of known fraudulent patterns. If it is, there’s no need to use an expensive machine learning predictive model; you already know to reject the transaction.
Redis Enterprise scales linearly and with zero downtime to provide resource-efficient databases that reliably deliver high throughput and sub-millisecond latency.
Redis Enterprise uses a shared-nothing cluster architecture and is fault tolerant at all levels. It has automated failover at the process level, for individual nodes, and even across infrastructure availability zones, as well as tunable persistence and disaster recovery.
Redis Enterprise provides Active-Active database replication with conflict-free replicated data types (CRDTs) to gracefully handle simultaneous updates from multiple geographic locations. You get global scaling without compromising latency or availability.
Fraud monitoring is the process of detecting and preventing fraudulent activity by continuously monitoring transactions and activity within an organization. This can be done through a variety of methods such as analyzing data patterns, setting up alerts for unusual activity, and verifying the identify of individuals attempting to access certain account or information. The goal of fraud monitoring is to protect an organization and its customers from financial loss and damage to reputation.
Fraud detection software is a type of computer program that is designed to automatically detect fraudulent activity by analyzing data patterns and identifying anomalies. It can be used in a variety of industries, including financial services, e-commerce, and healthcare, to identify fraudulent transactions, false claims, and other types of fraudulent activity. Fraud detection software typically uses artificial intelligence and machine learning techniques to analyze large amounts of data in real-time.
Fraud detection is the process of identifying and preventing fraudulent activities. Fraud can take many forms, such as identity theft, credit card fraud, and money laundering, among others.
