Customer story · Banking
We had to align models, migrate platforms, and build trust, all at once.
The challenge: merging complexity while preparing for the cloud
While the merger was legally complete, the underlying data reality was far more complex. Crelan and AXA Bank had evolved under different regulatory requirements and architectural assumptions. Crelan’s data infrastructure had historically focused on national-level compliance, while AXA Bank operated under broader European standards that introduced additional risk and finance requirements.
This disparity created gaps in operational coverage, duplicated logic across systems, and inconsistencies in data models used for reporting and regulatory processes. Many of the existing pipelines were built for on-prem systems and simply did not scale well in a cloud environment. Manual processes increased risk and slowed delivery at a time when regulatory expectations were rising.
The combined bank needed a unified data platform that could support European Central Bank approval, reduce manual work, and serve both risk and finance teams under strict regulatory requirements. It also needed a foundation that could support future AI-driven use cases.
As the team described it,
“We had to align models, migrate platforms, and build trust, all at once.”
The approach: model-driven automation for a unified bank
VaultSpeed provided the automation layer Crelan needed to unify and modernize its data environment at enterprise scale. Using VaultSpeed’s metadata-driven and model-driven approach, the data team merged and refactored existing risk and finance pipelines into a single logical architecture.
Data models from both banks were aligned and standardized, replacing brittle, manually maintained pipelines with cloud-ready transformations generated directly from the model. Instead of relying on custom ETL logic, teams defined business concepts once and used automated patterns to produce governed, auditable data products.
This approach made it possible to meet Basel requirements and support European Central Bank validation workflows without slowing delivery. Governance and lineage were embedded by design, allowing the platform to evolve as regulatory and business requirements changed.
The result was a standardized, high-performing data product layer that supports regulatory submissions, internal reporting, and future innovation on a single, trusted foundation.
VaultSpeed helped deliver what both regulators and internal teams needed, while providing a platform designed to scale.
CrelanFlagship use case: IRB risk models for regulatory capital
One of the most critical data products delivered after the merger was the Internal Ratings-Based risk model, which determines how much regulatory capital the bank must reserve against risk exposures.
Using VaultSpeed, the team merged the IRB models from both banks into a unified pipeline and established transparent, auditable data flows suitable for European Central Bank validation. Delivery was accelerated to meet a June 2025 approval target, and repeatable workflows were put in place to operationalize the models after approval.
The European Central Bank validation process spans 12 weeks and involves detailed scrutiny of model inputs, assumptions, and outcomes. VaultSpeed made it possible to prepare and submit the IRB models with the required level of accuracy, traceability, and confidence.
For Crelan, this was the first major data product delivered as one bank. It demonstrated that the new organization could meet regulatory expectations while operating at enterprise scale. Delivering IRB at this pace and level of auditability would not have been feasible with manual pipelines or fragmented legacy tooling.
The operating model: from compliance to scalable innovation
VaultSpeed’s role extends well beyond regulatory delivery. It has become a core component of Crelan’s multi-year data strategy, which focuses on improving operational efficiency, enhancing customer and agent experiences, and maintaining trust and compliance.
Operational initiatives include automating onboarding processes, strengthening fraud detection, and streamlining internal workflows. Customer-facing use cases focus on equipping agents with better insight into customer preferences and product interests. At the same time, the platform supports ongoing compliance with GDPR, the AI Act, and financial frameworks such as Basel.
Crelan is also laying the foundation for an AI center of excellence, starting at headquarters. Early initiatives include phishing detection and virtual assistants, all of which depend on having a stable, trusted Data Vault layer as a foundation.
As the team noted,
“VaultSpeed didn’t just help us merge our banks, it helped us design for the future.”
The impact: a trusted foundation for speed and scale
The result of Crelan’s data transformation is not just a unified platform, but a fundamentally different operating reality. Regulatory delivery is faster and more predictable, integration work no longer requires rebuilding logic from scratch, and new initiatives can be delivered without reintroducing compliance risk. Risk and finance models are aligned across the organization, manual processes have been reduced, and data products can be delivered with greater consistency and transparency.
By replacing manual pipelines with model-driven automation, the bank has reduced complexity and created a platform that can evolve alongside regulatory demands and business priorities. VaultSpeed plays a central role in enabling this transformation by making enterprise-scale data products repeatable, auditable, and cloud-ready.
The takeaway: designing a unified bank for the cloud era
For Crelan, merging two banks was not simply an integration exercise. It was an opportunity to build a new institution designed for the cloud, for regulatory rigor, and for long-term innovation.
With VaultSpeed at the core of its data strategy, Crelan has established a foundation that supports compliance today while enabling AI-driven use cases tomorrow. The result is a unified bank that can move faster, remain trusted, and scale with confidence because its data products are automated, governed, and built to last.
For banks facing mergers, regulatory pressure, or cloud transformation at the same time, Crelan’s experience shows that model-driven automation is no longer optional. It is what makes scale, compliance, and speed coexist.