Real Time Fraud Detection | Redis Enterprise

Leading companies power real-time fraud detection with Redis Enterprise

With exponential growth in online transactions, detecting and mitigating fraud is now more complex than ever before. Built to handle AI and machine learning workloads, tools to power real-time statistical analysis, and provide consistently high write throughput at low latency, Redis Enterprise is the answer to faster and more accurate fraud detection.

Modern digital platforms add unprecedented complexity to fraud detection

Today’s digital and mobile payments platforms are much more complex and distributed. They present many more software vulnerabilities and operate with a level of interconnectedness that traditional fraud detection techniques weren’t originally designed to address.

Keeping up with instant transactions is increasingly challenging

Thanks to modern software platforms, transactions are executed nearly instantly, but the same processing speed that creates a great experience for customers leaves banks and payment processors with less time to identify and react to fraud.

Digital identity brings far greater fraud risk

Personal information traditionally verified with physical documents is now stored online—easily accessible enough for a single data breach to put millions at risk of identity theft, account takeovers, and the creation of fake identities.

How Redis Enterprise meets the challenges of real-time fraud detection

Companies lose tens of billions of dollars to fraud each year in the form of fines, settlements, and erosion of the trust and customer loyalty that underpins the financial services industry. The increased complexity, volume, and speed of today’s online transactions means your organization will need more advanced methods of fraud detection to keep up with malicious actors.

Score transactions faster with colocated AI inferencing

Serve deep learning models directly where your data lives in Redis Enterprise for dramatically increased performance, enabling faster and more accurate fraud analysis.

Keep digital customer identity updated in real time

Redis Enterprise provides the capabilities required for creating and storing digital identity: high write throughput with minimal latency, geolocation and identity searches, and multiple data models.

Reduce cost with high-speed statistical analysis

Bloom filters, time series, and other data structures in Redis Enterprise let you efficiently check transactions against known patterns before deciding if extensive forensic analysis is needed.

Product features

Applying AI and machine learning

Colocated AI model serving with RedisAI

AI and machine learning models are increasingly being used to improve the speed and accuracy of fraud-detection platforms, but the need to query reference data stored in a separate database creates network overhead that slows processing times. Redis Enterprise lets you serve deep-learning models directly where your data lives for dramatically increased performance, enabling faster and more accurate fraud analysis.

Programmable data processing with RedisGears

RedisGears is a serverless engine for transaction, batch, and event-driven data processing in Redis that enables you to execute functions in Redis with infinite programmability. RedisGears enables use cases like write-behind caching, event processing, and the use of multiple models together in Redis to power more sophisticated fraud analysis.

Creating and updating digital identities

Redis Enterprise as a cache for user profiles

Being able to store and quickly access user profiles is essential to verifying customer identity and preventing fraud. Redis Enterprise can act as a highly available cache for user profile and session data so that companies can prevent fraud from occurring as transactions are processed.

Redis Enterprise as an in-memory database for digital identity

Digital identities that examine transaction history alongside user information must be updated constantly to work properly. Redis Enterprise provides the high write throughput and low latency needed to act as a primary database for storing and updating digital identities in real time.

RedisGraph to track relationships and fight synthetic fraud

Graph databases that can track relationships at the attribute level are being increasingly used to detect synthetic fraud, where multiple fake identities are created from a combination of real and falsified personal information. RedisGraph enables graph processing for these use cases to be executed up to 600x faster than any other graph database.

Powering high-speed statistical analysis

Anomaly detection with RedisTimeSeries

RedisTimeSeries enables you to ingest and query millions of metrics and events per second for historical analysis and anomaly detection, with support for incredibly fast ingest operations and integrations with existing metrics-visualization platforms.

Set detection with RedisBloom

Bloom filters are probabilistic data structures used to determine whether or not an item is part of a set. RedisBloom provides a fast and efficient implementation of Bloom filters that can be queried to see whether a particular transaction is present in a list of known fraudulent patterns to help decide whether deeper forensic analysis is needed.

Ensuring reliability and operational simplicity

Fault tolerance, resilience, and high availability

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.

Streaming and event sourcing with Redis Streams

Redis Enterprise can act as an event store with Redis Streams supporting fraud-detection platforms designed to ingest and analyze large amounts of transactions in real time.

Multiple models and data structures for real-time fraud detection

By combining multiple Redis modules and data structures, Redis Enterprise can power multiple components of your fraud-detection platform. The result is a simpler architecture that can process data across multiple models without needing to run multiple database clients and connectors.