Banks are embracing the cloud because it promises scalability, agility, and cost savings. Financial companies may deploy new services more quickly, cut infrastructure costs, and enhance consumer experiences. However, given the power of cloud computing, why do many institutions still struggle to scale effectively?
Moving to the cloud does not guarantee scalability. Many banks lift and relocate legacy systems, without redesigning them for cloud-native efficiency. This leads to performance bottlenecks, regulatory issues, and antiquated infrastructure that restricts expansion.
Scaling is more than just implementing cloud solutions; it also entails transforming financial infrastructure to align with cloud-native principles. This article delves into hidden scalability problems, shortcomings in typical cloud adoption, and how institutions might achieve real cloud-native scaling.
The Cloud Adoption Myth in Banking
Many banks believe that the best way to achieve scalability is to move to the cloud. Moving core banking to AWS, Azure, or Google Cloud for immediate, limitless scalability seems like a straightforward concept.
However, cloud acceptance and cloud scalability are not synonymous. The issue is that many institutions do not update their architecture before making the move. Instead of rebuilding systems to be cloud-native, they simply lift and relocate their existing infrastructure.
A bank that migrates its solid banking core system to the cloud may nevertheless have delayed deployments, stiff scaling, and expensive expenses. Without a cloud-native solution, old processes on new technology lead to inefficiencies, canceling the benefits of cloud computing.
To truly scale on the cloud, banks must reconsider how their systems function, not just where they run. This necessitates upgrading their entire approach to financial infrastructure.
How Traditional Architectures Work
Traditional designs focus on networks, and their underlying tools, such as firewalls and VPNs, strive to:
- Establish a security perimeter. The network implies internal trust and external distrust, with firewalls as gatekeepers between them.
- Backhaul traffic. Remote users access apps via VPNs that connect to centralized data centers, which increase latency and complexity.
- Scan traffic. Firewalls scan network traffic, but they have trouble protecting encrypted data and frequently overlook threats.
The Hidden Scaling Challenges Banks Face
Here are some of the key challenges that banks face.
Legacy Monolithic Architectures Still Exist
One of the most significant impediments to cloud scalability is the continuous reliance on monolithic systems. Even on the cloud, many banks still use huge, single-structure apps that cannot scale properly.
Here’s why this is an issue. In a monolithic system, all functions—payments, user authentication, transaction processing—are combined. When one component wants more resources, the entire program scales, even if just one feature needs them.
For example, during busy hours, a bank’s transaction processing may need to ramp up. In a monolithic structure, growing transactions grow the entire program, resulting in wasted resources and higher cloud costs.
Compliance & Data Residency Complexities
One of the sectors with the highest levels of regulation is banking, which has stringent regionally specific compliance requirements. Data residency regulations limit the location and method of data storage for banks expanding internationally.
Regulations such as GDPR, PSD2, and India’s data regulations require the storage and processing of financial data in specified areas. This complicates multi-cloud scaling because banks must carefully consider which data can be moved and where.
Without a well-structured cloud compliance strategy, banks struggle to expand globally, delaying growth and raising operational complexity.
Security and Latency Trade-Offs
Security is always a top priority for banks, and for good reason. However, when financial institutions scale their cloud operations, they often face a trade-off between security and performance.
Banks that use multi-cloud for redundancy may experience higher delays while syncing data across several providers. This can impact real-time transaction processing, causing delays in payments and fund transfers.
Additionally, many cloud providers have varying security frameworks, so banks must coordinate security policies across several settings. The more sophisticated the security configuration, the more difficult it is to scale without adding vulnerabilities.
Underutilization of Cloud-Native Services
Despite their cloud migration, several banks still employ traditional infrastructure strategies rather than utilizing cloud-native services. They continue to rely on virtual machines (VMs) rather than containerization, microservices, or serverless computing.
Underutilization causes inefficient scaling, making banks pay for unused resources while failing to optimize infrastructure for growth.
How Banks Can Truly Achieve Scalability in the Cloud
The following are ways that will ensure banks scale in the cloud:
Embrace a Microservices Approach
To achieve true scalability, banks must decompose monolithic programs into microservices. This lets separate financial services such as payments, fraud detection, and user management to scale independently without harming the overall system.
Prioritize Cloud-Native Compliance Strategies
Regulatory issues should not impede scalability. Banks should use automated compliance solutions, such as policy-as-code frameworks, to protect data while allowing for seamless growth across many locations.
Invest in Serverless and Event-Driven Architectures
Banks can scale automatically in response to real-time demand thanks to serverless computing, which does away with infrastructure administration. Event-driven architectures improve real-time financial services by increasing responsiveness and minimizing cloud expenses.
Implement Multi-Cloud Resilience Without Complexity
Multi-cloud strategies require centralized security and orchestration tools like Kubernetes to scale efficiently without added security or performance risks.
Generative AI Governance Framework
As generative AI becomes more integrated into financial operations, banks must create a governance structure to ensure responsible AI usage. This encompasses:
- Transparency and Explainability. Detailed records of AI models, training data, and decision-making procedures.
- Bias Mitigation. Routine checks to find and eliminate biases in results produced by AI.
- Regulatory Compliance. Ensuring AI complies with changing international financial standards.
- Security and Risk Management. Protects against artificial intelligence hallucinations, fraud, and adversarial assaults.
- Human-in-the-Loop Oversight. An organized strategy in which AI outputs are vetted and approved by domain experts before being deployed.
Conclusion
Scaling on the cloud takes more than just moving to AWS or Azure. This means that banks must upgrade their infrastructure and embrace cloud-native solutions. True scalability requires rethinking infrastructure, implementing microservices, automating processes, and assuring compliance. Banks that fully adopt cloud-native architectures will lead financial services by providing faster, more reliable, and cost-effective solutions.
