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.
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.
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