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Modernizing a Financial Transaction Infrastructure for Cross-Border Expansion
Technology Category
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
- Platform as a Service (PaaS) - Connectivity Platforms
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
- Quality Assurance
Use Cases
- Fraud Detection
Services
- Software Design & Engineering Services
- System Integration
The Challenge
The bank was looking to expand their online payments business, but was hampered by the inflexibility of their existing solution. The existing application architecture could not scale up to meet the demand of larger customers that could unlock the highest potential return on their technology investments. In addition, the legacy code was difficult to extend and evolve, so that adding new features or adapting to facilitate entry into new markets could not be done in a timely fashion - release cycles of the monolithic application were generally six months in duration. One of the client banks in Hong Kong had a requirement for a 50x increase in transaction speed, so this became one of the criteria for evaluating the performance of the solution. Even if large investments were made in upgrading the hardware environment for the application, the bank was doubtful that such an improvement would be possible with the existing software architecture.
About The Customer
A large multinational bank headquartered in the Asia Pacific region was looking to build out its payment processing infrastructure. With over 30 different payment offerings in their portfolio, and clients across 40 countries, the bank is an established global leader in Asia, the Middle East, and Africa. With a wide range of payment solutions in their portfolio and many underserved markets in the geographic regions where they operate, the bank saw fertile ground for expansion. They knew their current application architecture was not flexible enough to handle the new demands. Their markets required operating in multiple countries, with multiple currencies, languages, and regulatory requirements. The effort required to adapt their software to move into new markets made it cost prohibitive to take advantage of the opportunities that were available. To unlock the capabilities needed to tackle new markets, they undertook a digital transformation project to build the foundation upon which new applications could be built.
The Solution
To enable the bank to move aggressively into new markets to capitalize on the opportunities they saw, they re-architected their monolithic application into a set of microservices. The benefits of a microservices architecture aligned well with the bank’s need to be more agile. They could have smaller, more nimble teams developing each service, and the loosely coupled services could evolve independently to support new business opportunities or adapt to changing conditions. Hazelcast’s lightweight footprint and deploy-anywhere flexibility make it a great choice for moving to cloud-native microservices-based architectures. Speed: To iterate quickly on new applications and features, the manual deployment process was replaced with an automated CI/CD process. Hazelcast’s familiar API, based on Java collections, means that developers didn’t spend time learning new ways of doing things, and were able to begin development right away using Map and other familiar classes. Scale: Hazelcast’s elastic scalability means that capacity increases can be done in a zero-downtime fashion, simply by bringing new nodes online. Data structures will be rebalanced to the new node configuration without interrupting ongoing processing. Stability: To achieve the required up-time, the new architecture featured automatic failure detection, auto-healing systems, and blue-green deployments. Security: Industry standard security protocols, such as TLS/SSL, encrypt data at every step of the way to meet strict compliance requirements. The built-in security features of Hazelcast protect sensitive customer data, making it easy for the application developers to provide the required security features without developing a lot of customized security code.
Operational Impact
Quantitative Benefit
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