Background
 
A global bank with operations spanning retail, corporate, and wealth management faced significant challenges with fragmented legacy infrastructure across regions. Outdated mainframes, siloed applications, and high maintenance costs slowed innovation, increased risk, and limited the bank’s ability to scale services securely. By executing a structured cloud migration program, leveraging leading hyperscale platforms and cloud-native services, the bank modernized its core systems, unified operations, and created a flexible digital foundation.
 
Customer Outcome
 
Banking customers now benefit from faster, always-available digital services, seamless onboarding, personalized financial insights, and secure mobile-first interactions—building trust, convenience, and loyalty.
 
Business Outcome
 
The migration reduced infrastructure costs by up to 40%, accelerated product innovation cycles by 50%, and improved resilience and regulatory compliance. The bank now operates with simplified governance, real-time risk monitoring, and elastic scalability—enabling sustained growth, improved customer satisfaction, and long-term competitiveness in global financial markets.
 
Technology Platform Options
 

1. Cloud Service Providers (CSPs)

   • Examples: AWS, Microsoft Azure, Google Cloud Platform, Oracle Cloud Infrastructure.

   • Why: Provide secure, scalable, and compliant environments for financial services with global coverage.

2. Cloud Migration & Modernization Tools

   • Examples: AWS Migration Hub, Azure Migrate, Google Migrate for Compute Engine.

   • Why: Simplify workload migration, track progress, and optimize performance during transition.

3. Core Banking & SaaS Platforms

   • Examples: Temenos, Thought Machine, Finastra, nCino.

   • Why: Replace or augment legacy systems with cloud-native banking solutions to accelerate digital transformation.

4. Cloud-Native Data Lakes & Warehouses

   • Examples: Snowflake, Databricks, BigQuery, Azure Synapse, Redshift.

   • Why: Centralize structured/unstructured financial data for risk modeling, regulatory reporting, and AI-driven insights.

5. AI, Risk & Analytics Layers

   • Examples: SAS Risk & Fraud, Azure AI, AWS SageMaker, Google Vertex AI.

   • Why: Enhance fraud detection, compliance, credit scoring, and customer personalization at scale.
     
     Connect below with an aiT Specialist today about how to implement 'Cloud Migration' across your organisation.