How Cloud-Based Architectures are Transforming the Banking Industry


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To compete with today’s tech-savvy customers, many banks will team up with agile Fintech in 2023 and use the cloud to create new products and services

The financial services sector employs decades-old legacy systems and applications.  These programs cannot use modern IT infrastructure to benefit banks or adapt to rapidly changing business needs. Because of the poor performance, poor customer experience, and extended time-to-market for new products, banks’ ability to compete is harmed. Additionally, legacy application landscapes hinder banks’ digital transformation efforts and delay cloud migration.

Although banks know the urgent need for legacy modernization, a practical and affordable solution has not yet been found.

With over a decade of cloud-based services, it is unsurprising that the technology has evolved from a lift and shift of applications to the cloud to cloud-native deployments.

even with mentioning a model-based approach to application development, also known as low-code, modern cloud technologies like containers, microservice architectures, and orchestration have all impacted storage, computation, and cost.

Here are some things to consider when modernizing cloud-based banking architectures to achieve effective digital transformation.

Powerful Orchestration

Architecture configuration adds complexity that is difficult for a container to handle alone. Because of this, orchestration skills are essential for delivering high availability. A platform can run containers across several machines, maintain consistency across instances, distribute load among containers, and offer redundancy with orchestration. It is now possible for IT managers in the financial services industry to create the Application as a Service (AaaS) components and manage these applications independently with the Kubernetes standard, which is the industry’s top orchestration system when combined with Helm charts, a group of files that describe a related set of Kubernetes resources. Unlike service-oriented architectures, an AaaS architecture allows each service to use its application services and API gateway. As a result, there is independence and less reliance on other computing resources.

Micro service architectures

In the 2010s, the combination of containers and serverless computing drove the next generation of cloud software to support cross-channel interaction without interruption, infinite scale, and continuous upgrades. Containers summarise an application as a single bundle with any associated binaries, libraries, configuration files, and dependencies needed for its execution. Today, containers allow developers to package applications with their key components while utilizing the host for essential functions like the operating system, creating what has come to be known as “microservices.” These packages, also referred to as images, enable the delivery of applications in a resource-efficient and flexible manner, reducing the size and speed of applications and minimizing the waste frequently present in legacy Virtual Machine (VM) architectures. By integrating containers and microservices into a single architecture, financial services organizations could structure applications as a selection of self-contained services distributed across a network environment. Established businesses, however, must carefully plan how to integrate a microservices approach with their current legacy systems and established business processes.

A single model-based strategy

The technology stack enabling financial services organizations to progress toward a unified “model-based” approach to application development and deployment should include all the above mentioned components. A solid low-code development platform focused on case and process-centric applications can provide an environment that guarantees all models that make up an application are consistently built, managed, deployed, and monitored to ensure cohesion and compatibility. The “model-based” or “low-code” application development offers exceptional productivity and teamwork between the business and technical teams. Models can be combined and tailored to the needs of the enterprise whether they are built on top of an on-premises or cloud-based service platform and are considered to be a part of Intelligent Automation (also known as Digital Process Automation or DPA), business process management (BPM), Data Capture and Management, or other functions. Then, regardless of the underlying architecture, this ensures that applications behave consistently.

Low-code platforms now offer a visual definition of application components at a higher level, which is less technical and more in line with business semantics. As a result, it is simpler for the more significant business to participate in the application’s development, and IT departments can better communicate with the company and product to get an application ready for production quickly. It entails emphasizing crucial actions and the final results required to generate value. The visual models, in contrast to code, are understandable by both technology and business staff, offering a common forum for communication. Models can be used in agile development and shared and versioned like code.

Also Read: How Banks Can Drive Fintech Growth by Embracing API and Fostering Open Banking

Data, AI, and Privacy

The banking sector is developing exponentially, from introducing chatbots to using decision models and ML-powered risk. As financial institutions expand their operations and embrace novel technologies, their data stockpile grows simultaneously. Financial data, which frequently includes sensitive and personally identifiable information, is subject to the strictest governance because of its importance and the responsibility that comes with it. This is discouraging because banks need to work together and share data to address the most pressing issues in global finance, such as anti-money laundering and discrimination in credit decisions.

Banks can now work together on a massive scale to address these critical financial issues without physical dataset sharing, data movement, or manual partner agreements, all thanks to advances in private AI. With PETs, they can conduct data science and analytics in a distributed manner without ever having to access the underlying data. Banks can retain full control over their data because it never leaves their servers, and third-party model developers and software engineers can work with the data privately to create models and applications without having direct access to the underlying datasets. Data now has an invisibility cloak with private AI and the suite of PETs powering innovation and future collaboration in financial services.

The fintech sector has expanded globally as banks attempt to modernize in the face of changing laws and regulations. To compete with today’s tech-savvy customers, many banks will collaborate with agile Fintech in 2023 and use the cloud to create new products and services. There will be obstacles before these collaborations can reach their full potential. As regulatory oversight increases, it will be crucial for established banks that their fintech partners refrain from adding systemic risk to their supply chains. Adopting a hybrid cloud strategy, which uses industry-specific clouds with built-in security and compliance controls, can support the secure modernization the banking industry needs to navigate the shifting landscape. In the coming year, Fintech should collaborate with reputable technology providers to investigate the adoption of sector cloud platforms that aid in removing obstacles within collaborations that stifle agile innovation. When combined, modernized cloud-based banking architecture and low-code application development can help deliver the digital transformation banks need to stay competitive.

Swapnil Mishra
Swapnil Mishra
Swapnil Mishra is a global news correspondent at TalkCMO, with over six years of experience in the field. Specializing in marketing technologies, Swapnil has established herself as a trusted voice in the industry. Having collaborated with various media outlets, she has honed her skills in content strategy, executive leadership, business strategy, industry insights, best practices, and thought leadership. As a journalism graduate, Swapnil possesses a keen eye for editorial detail and a mastery of language, enabling her to deliver compelling and informative news stories. She has a keen eye for detail and a knack for breaking down complex technical concepts into easy-to-understand language.


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