Cloud, Data, and Automation: The Foundations of AI in Banking

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Artificial intelligence has become the centerpiece of digital strategy conversations in financial services. Across the industry, leaders see AI as a way to improve decision-making, personalize services, and increase operational efficiency. But while expectations are high, the road to AI transformation is far from simple.

Many financial organizations are still working with fragmented systems, outdated platforms, and data that is difficult to analyze or even access. Before advanced AI solutions can deliver real value, institutions must modernize their technology foundations and rethink how information flows across the business.

Modernization Through Cloud Migration

The core of digital transformation today is the move to the cloud. Many institutions upgrade their core systems only once every decade or less, which causes operational risk and slows innovation. Cloud adoption provides a practical way to modernize critical platforms without rewriting business logic from scratch.

IBA Group supports three key migration approaches that align with the organization’s technical readiness and strategic goals.

1. Application Migration

There is no universal approach here. Migration strategies can be combined to achieve the right balance of speed, risk mitigation, and long-term value.

Examples of successful transformation:

• A European retail chain unified distributed apps across different locations, reducing TCO by 35%, accelerating feature delivery 5-fold, cutting data sync time by 90%, and achieving 99.9% availability.
• A major European automaker centralized infrastructure across factories using AWS, improving scalability, standardization, and maintenance efficiency.

2. Data Migration and Modern Data Architectures

Moving data to the cloud is not a purely technical exercise. It addresses critical challenges such as fragmented data sources, high manual workload, poor data quality, and slow reporting.

Transitioning from on-prem databases (including IBM DB2 and Oracle) to cloud platforms like Azure, AWS, Snowflake, or Databricks:

• Improves performance and real-time analytics
• Enables automatic scaling
• Reduces infrastructure costs by 30–60%
• Uses pay-as-you-go models ideal for variable workloads

Our approach includes gradual migration using tools like AWS DMS and Azure Data Factory. This ensures continuity of business services.

Case Highlights

• A European agribusiness gained a single source of truth for decision-making, real-time analytics, and eliminated local infrastructure spending.
• An IT infrastructure provider reduced migration timelines by 150% through a custom conversion tool for IBM DataStage workloads.

We also support modernization initiatives that involve legacy mainframes, including hybrid architectures, modern microservices, API-first integration, and Kafka-based data streaming. These upgrades improve system performance by up to 60 percent.

3. Data Readiness for AI

Even the most advanced AI cannot compensate for poor-quality data. In fact, data scientists often spend up to 80% of their time cleaning and reconciling data instead of building models.

IBA Group applies a structured, five-stage framework that includes the following phases.

1. Data consolidation into a single repository
2. Creation of a common semantic layer to unify business meaning across systems
3. Metadata management (catalogs, lineage, quality rules)
4. Data cleansing and harmonization (profiling, deduplication, standardization)
5. Data orchestration with automated pipelines and monitoring for AI workloads

Hybrid Cloud and Data Sovereignty with ICDC

For organizations facing compliance constraints, IBA Group offers ICDC, a full-featured private cloud that can be deployed on-premises within 2–3 weeks. The platform provides VM management, networking, storage, Kubernetes orchestration, DevOps as a Service, and built-in security monitoring.

ICDC is already operating in Kazakhstan with telecom providers and major banks, helping them maintain sensitive data locally and integrate cloud-scale services.

Building the Foundation for AI Success

Application modernization, data migration, and AI data readiness are not isolated initiatives. They form a single modernization pathway.

• Legacy applications must be modernized before data can be efficiently migrated
• Clean, unified data must exist before AI can deliver business value

Most failed AI projects struggle not with algorithms, but with data quality. To achieve meaningful results, financial institutions must strengthen their IT systems, optimize business processes, and build data governance.

For examples of IBA Group’s success stories, please click here. For more information on technology strategy and how tech connects to real business solutions, please click here.