Fraud detection and prevention systems today typically combine machine learning and artificial intelligence logic with a set of rules to predict whether a given activity/transaction is legitimate or fraudulent. The systems not only have to stop fraud, but also have to minimize false positives so that legitimate transactions are not inadvertently flagged as fraudulent.
One way to create more accurate fraud prevention systems is to simultaneously run multiple detection models that lead to a composite score that can predict fraud at a higher success rate. This adds a competitive advantage by reducing losses from fraud and increasing revenue with fewer false positives. But this approach requires significant speed that can be achieved efficiently only with in-memory processing.
Many customers today run fraud checks at high speed on Hazelcast. As they and other companies look to the cloud, they can continue to use Hazelcast to run high throughput, low latency, and reliable fraud detection.
Payment processing requires reading an incoming stream of transaction data, validating the transactions, then directing the data to the most efficient payment gateway. As more and more consumers turn to new ways to make payments, the transaction load will continue to grow quickly.
This growth in data load requires a high-performance foundation that can handle load spikes and high ongoing volume, with a high level of reliability. Hazelcast provides the functionality to process payments in its in-memory store and handles an incoming, real-time stream of transaction data with its stream processing capabilities.
Today’s microservices need to access an external data store for saving state and looking up relevant data. Since the simple, streamlined model of microservices can often add higher performance to a system, an extremely fast data store is necessary to fully take advantage of microservice performance.
Unlike disk-based data stores that can become the bottleneck for microservices, Hazelcast uses RAM for data storage and enables high-speed data access. Hazelcast can also be used as an in-memory publish/subscribe platform for exchanging messages between microservices in a fast and reliable way.
Risk management comes in many forms, and some of the more complex risk analysis models can entail billions of calculations that assess the many possibilities of how a large transaction can be impacted by future uncertainty.
Hazelcast can be used as a big pool of memory to do extensive parallel processing to do number-crunching for risk models in a Monte Carlo simulation framework. As more data is needed, more cloud instances can be added to grow the memory pool.
Customers stream into your business millions of times per day and expect a personalized, thoughtful, and fast experience. When a few seconds means the difference between a sale and abandonment, speed matters more than ever.
Hazelcast can act as a fast, central repository for customer data coming in from many different sources to help businesses get a more complete picture of customers. Using in-memory speed, this data can be shared in real-time with customer service agents who directly interact with customers to provide the level of service they expect.
Two key characteristics of Internet of Things data are the continuous flow and the many distinct data sources. This generally results in a large volume of fast-moving data. And since by definition these “things” are operating in a real-world environment, real-time responsiveness is a critical requirement.
Hazelcast provides the speed and scale to handle the large volumes of data that Internet of Things analytics requires. With its real-time processing capabilities, it can analyze the data individually and collectively to uncover insights that traditional analytical processes would likely miss.
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