Delivering the real-time future of financial services
Financial services firms are undergoing massive digital disruption, and are modernizing their applications to provide superior customer experience, better decision-making, and improved resilience. Redis Enterprise provides the modern data models required to successfully deliver real-time financial services and comply with Open Banking requirements, while enabling organizations to remain secure and compliant.
Every facet of the finance industry is being digitized—internet giants and fintech startups have disrupted traditional financial institutions with technology platforms that deliver more responsive and customer-focused financial services.
COVID-19 accelerated changes in customer behaviors, consumption habits and expectations that are here to stay. In order to stay relevant and competitive, your organization needs a modern technology platform of its own. According to a recent survey by BDO 43% of C-suite executives are accelerating some or all existing digital transformation plans and improving the customer experience (CX) is the number one digital priority.
Your systems need a new set of data management capabilities to meet the demands of today’s financial services customers: true real-time performance at scale, modern data models, and enterprise-grade security and compliance in any environment.
Get a complimentary, personalized report of the value Redis Enterprise could deliver to your business
Redis Enterprise provides the high write throughput and multiple data models needed to keep customer profiles and account information updated in real time for mobile banking, personalized offers, log-in authentication, targeted advertising, and more.
Detecting fraud is becoming more difficult as applications and customer data become increasingly distributed. Redis Enterprise enables financial services firms to examine patterns across transaction histories, perform geospatial analysis, and check transactions against known fraudulent patterns with probabilistic data structures.
Collecting, storing, and processing large volumes of high-variety, high-velocity data presents complex challenges—especially given that responsive, timely, and accurate data-driven decision making is core to financial services. Redis Enterprise provides real-time data collection and enables analysis of any data, including sentiment, price fluctuations, geospatial, SEC 10-K forms, sales, weather and satellite data to instantly inform your trading algorithms, risk calculation engines, investment recommendations, personalized offers, and more.
Digitization, automation, remote work, and fintech partnerships have created new cyber risks and single points of failure. Risk management is critical in addressing cyber threats, identity theft, fraud, and automated financing – not to mention the inherent financial risk in asset management and investing. Redis Enterprise enables financial services firms to perform more frequent financial risk analysis, close fraud case investigations with fast search, comply with KYC regulations, and enable granular access management.
Open banking is both a regulatory attempt to address data hoarding in financial services and a response to customer demand to share their data with other providers. The foundation of the open banking revolution is the data, databases, standards, and open APIs that make the free flow of data between banks, third party service providers, and consumers possible. From PSD2 to BIAN, serverless to cloud-native MACH architectures, Redis Enterprise provides the underlying modern data stack that can enable banks to deliver the required data with sub millisecond latency, leverage Open Banking standards to develop new services and business models, and future-proof themselves against new standards.
Technical use cases
Caching decreases application response times by serving frequently needed data from an in-memory cache instead of making calls to a database with network-attached persistent storage. Redis Enterprise provides enterprise-grade caching with expiration and eviction policies to efficiently manage cache objects, global distribution with Active-Active replication, and virtually unlimited scale.
Storing user session data enables applications to remember user identity, login credentials, and personalized information, while making sure that application response times are as fast as possible for users. Redis Enterprise speeds session management with support for extremely large datasets using Redis on Flash and data-persistence options that don’t impact performance.
Redis Enterprise provides fast data ingestion and can act as an in-memory query accelerator in front of operational and analytics databases to provide real-time decision making. With support for most data structures providing the needed pre-sorting in-memory, Redis delivers dynamic querying over millions of records at sub-millisecond latencies.
RedisJSON is a high-performance NoSQL document store that allows developers to build modern financial services applications. It provides native APIs to ingest, index, query, and run full-text and fuzzy search on JSON documents at millions of operations per second with sub-millisecond response times.
Explore customer use case examples
Learn about the typical use cases in the financial services industry.
Enable instant customer experience
Enrich reference data for securities trading
Create efficient financial risk analysis
Reduced case management and reporting costs
Enable zero trust with granular access management
Detect online transaction fraud
Provide AI/ML online feature store
Deliver real-time analytics
We rely on Redis Enterprise Cloud on AWS for performance and scale. As a result, we’re able to provide an exceptional customer experience, regardless of data types, without any downtime or limitations.
Senior Vice President of Engineering, Ekata
E-commerce merchants and their card-issuing banks love that we help them recapture more good business by delighting their customers. Redis brings a major part of that value proposition and allows us to rapidly scale without jeopardizing our superior customer experience.
Co-founder & CTO, Kipp Ltd.
Redis modules, such as RediSearch, RedisGraph, RedisBloom, and others can be readily applied to use cases like fraud detection, personalization, transaction scoring, and more.
Leveraging Redis Enterprise’s Active-Active database replication with conflict-free replicated data types (CRDTs) enables financial services applications to gracefully handle simultaneous updates from multiple geographic locations, powering use cases like fraud detection, rate limiting, and personalization on a global scale without compromising latency or availability.
Redis Enterprise ensures production data is isolated from administrative access and offers multi-layer security for access-control, authentication, authorization, and encryption (including data in transit and data at rest).
Redis Enterprise uses a shared-nothing cluster architecture and is fault tolerant at all levels—with 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 is available on all of the major cloud providers as a managed service or as software, provides automation and support for common operational tasks, and integrates with the platforms underpinning modern software architectures, such as containers and Kubernetes. Redis Enterprise provides the modern data platform for building mission critical financial services apps in AWS, GCP, and Azure.
Efficiently scaling database performance is critical for real-time financial services applications. Redis Enterprise scales linearly and with zero downtime to provide more resource-efficient databases that reliably deliver high throughput and sub-millisecond latency.
Data modeling is a process through which data is stored structurally in a format in a database. Data modeling enables financial services organizations to make data-driven decisions and meet varied business goals. Examples of data models include relational, network, hierarchical, object-oriented, etc.
NoSQL databases (aka “not only SQL”) are non-tabular databases and store data differently than relational tables. NoSQL databases come in a variety of types based on their data model. The main types are document, key-value, wide-column, and graph. They provide flexible schemas and scale easily with large amounts of data and high user loads common to many industries including financial services.
Databases are used in banking applications to store and process financial transactions; from keeping track of customer accounts, balances and deposits, to asset management, loans, and credit cards. Banking websites and mobile apps use databases to store content, customer login information and preferences and may also store saved user input.. Databases allow data to be stored quickly and easily and are used by banks in their front, middle, and back office operations. As banks continue their digital transformation efforts, migrate to the cloud, and adopt new technologies, the choice of database type and vendors is becoming increasingly critical.
Data management is the process of ingesting, storing, organizing and maintaining the data created and collected by an organization. Effective data management is critical to deploying and running business applications and analytics programs to help drive operational decision-making and strategic planning by executives, business managers and other end users.
Open banking is a banking practice that provides third-party financial service providers open access to consumer banking, transaction, and other financial data from banks and non-bank financial institutions through the use of application programming interfaces (APIs). Open banking will enable the connection of accounts and data across institutions for use by consumers, financial institutions, and third-party service providers.
Real time analytics lets users see, analyze and understand data as soon as it arrives in a system. Logic and mathematics are applied to the data so it can give users insights for making real-time decisions. Latency needs to be extremely low (sub-millisecond) and availability requirements are high (e.g., 99.999%). compared to batch analytics.
By continuing to use this site, you consent to our updated privacy agreement. You can change your cookie settings at any time but parts of our site will not function correctly without them.