Use Cases›Retail Banking

Retail Banking

Banks strive for innovative IT capabilities while simplifying their architecture, balancing new opportunities with technology change risks.

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Modernize applications with the Hazelcast Unified Real-Time Data Platform.

Introduction

Consumers increasingly opt for touchless and online interactions for financial matters. Growing web and mobile app usage reduces costs by promoting self-service. Widespread non-cash payments drive banks toward modern scalable systems due to increased transaction demands. With rising online transactions, vigilance against fraud is vital for banks, particularly with the surge in e-commerce. Card-issuing banks must balance minimizing fraudulent risks with avoiding overly strict fraud detection algorithms that could reject legitimate transaction fees.

Business Requirements

Retail banks grapple with persistent challenges in customer experience, responsiveness, reliability, and security due to evolving customer patterns, leading to the business requirements that include:

  • Improving customer experiences via fast data access at scale with reads and writes in the millisecond range, to provide the performance to handle growth and spikes of online requests at the level of tens of thousands per second, without a costly overprovisioning of hardware
  • Taking action in real-time, especially to drive responsiveness that leads to increased revenue, such as timely promotions and offers
  • Adding new capabilities that create more customer-facing product offerings, with faster time-to-market, and without a costly system overhaul
  • Controlling existing expenses, especially mainframe MIPS costs, when expanding core banking functionality into new channels
  • Improving fraud detection techniques to reduce loss due to fraud while also retaining transaction fees that were previously lost due to a high rate of false positives
  • Boosting profitability by increasing IT agility and enabling faster development and deployment of internal business applications that focus on operational efficiency
  • Addressing all of the above while running a secure and highly available infrastructure

These are just some of the reasons why banking customers choose Hazelcast to run their critical systems.

Technical Challenges

IT teams in retail banking tackle technical challenges, primarily due to legacy systems. As banks innovate through data-driven initiatives, leveraging leading technologies becomes crucial for staying competitive.

Some of the challenges that retail banks face today include:

  • Overwhelmed systems that result in slow responsiveness
  • Scaling limitations that preclude adjustments to varying workloads
  • Unsustainable increases in costs for hardware to support future business growth
  • Difficulty and complexity of adding new capabilities and functionality, especially around real-time action, which directly and positively impact business metrics like growth, costs, and risk
  • Implementation effort of adding new, required technologies to an otherwise working system

Why Hazelcast

Hazelcast is a preferred partner for retail banks seeking speed at scale, security, and reliability. Our unified real-time data platform combines a distributed, fast data store with a high-speed stream processing engine, enabling the fastest applications in any data-intensive environment. With these technology advantages, Hazelcast empowers customers to run highly successful banking solutions.

Easy to Develop and Deploy

Hazelcast Platform was designed to simplify the application development process by providing a familiar API that abstracts away the complexity of running a distributed application across multiple nodes in a cluster. This allows developers to spend more time on business logic and not on writing custom integration and orchestration code. Our platform can seamlessly integrate with your IT architecture to add new capabilities without having to rip and replace your existing stack. The Hazelcast cloud-native architecture requires no special coding expertise to get the elasticity to scale up or down to meet highly fluctuating workload demands.

Performance at Scale

Whether you process a large volume of transactions, enhance online experiences with faster responsiveness, run large-scale transformations on data, or cut costs with a mainframe integration deployment, Hazelcast Platform is designed for the ultra-performance that today’s banking workloads require. The proven performance advantage is especially valuable for data-focused experimentation that enables ongoing business optimization, especially in data science initiatives including machine learning inference for fraud detection.

Mission-Critical Reliability

With built-in redundancy to protect against node failures, and efficient WAN Replication to support disaster recovery strategies that safeguard against total site failures, Hazelcast Platform was built to provide the resilience to run mission-critical systems. The extensive built-in security framework protects data from unauthorized viewers, and security APIs allow custom security controls for sensitive environments.

Customer Success Story

Swedbank faced excessive setup overhead for backend systems driving business initiatives. They used a traditional relational database management system (RDBMS) for data storage across all apps, creating schemas even for basic use cases, hindering agility.

Reliability and high uptime were crucial, leading them to seek a system with business continuity features. Security, given sensitive data, demanded comprehensive built-in capabilities.

Hazelcast's speed was evident from benchmarks, and combined with security and business continuity, the technology appeared to be a compelling option. They adopted a hot data layer architecture for continuous data access. Hazelcast Platform’s schema-free storage suited their needs, allowing faster movement.

Integration was swift; the commercial version with WAN Replication and 24/7 support was pivotal. They first stored single sign on (SSO) tokens, replacing diskless RDBMS. Open banking security token storage followed suit. More use cases, like data sharing, were added, utilizing Hazelcast's versatile capabilities, including the stream processing engine, for future growth.

Use Cases

Delivering the right offer at the right time boosts customer engagement. However, the challenge lies in identifying the ideal offer once the right time is recognized. Customer interactions provide signals for specific offers, but your systems might lack precise offer calculation. Hazelcast enables capturing real-time customer opportunities by collecting pertinent details during interactions. For instance, you can instantly determine a suitable loan offer based on immediate customer needs.

More about Real-Time Offers

Building web and mobile apps that connect directly to core banking backend systems is expensive and time-consuming, especially since many backends were not designed for the modern interfaces of today. By deploying a hot data layer based on Hazelcast Platform, banks can deliver a common API that delivers core banking data with in-memory speeds to new customer channels.

Payment processing often entails a series of computations to determine the validity of a payment request, and the optimal routing of a transaction across payment partners. With Hazelcast, banks can run complex operations including sophisticated, machine learning-based fraud algorithms to reduce fraud loss while also reducing other operational costs.

Many banks have data sets that need to be shared/curated from across different divisions, and direct connectivity to the systems of record often creates an excessive load that can cause disruptions. A distributed data-as-a-service on Hazelcast Platform allows data sharing across different divisions and business applications, in a high-speed, cost-effective manner. And as much more than just a cache, Hazelcast can support other compute-intensive operations on shared data to run transformations, data enrichment, aggregations, and any other operations that makes the data more consumable for the various end-users.

Mainframes still run many banking operations today, but extending these systems to do more often comes with much higher MIPS cost. By leveraging Hazelcast Platform in a mainframe optimization initiative, much of the workloads that do not have to run on the mainframe can be handled in a more cost-effective way while retaining similar performance and availability characteristics on commodity hardware or in the cloud.

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