Accelerating Digital Transformation for a Mid-Sized Bank
INDUSTRY:
Banking
ABOUT THE CUSTOMER
A mid-sized commercial bank based in the Middle East sought to modernise its legacy banking infrastructure. The institution served a growing retail and SME customer base but faced operational inefficiencies due to fragmented legacy systems. Manual reconciliation, data silos, and regulatory compliance challenges were limiting its ability to launch new digital banking services efficiently.
BUSINESS CHALLENGE
The bank planned to migrate its core banking, loan origination, and customer data to a modern digital-native platform for lending and deposits. However, several challenges were identified:
- Legacy data scattered across multiple core and CRM systems.
- Lack of data standardisation and inconsistent data quality.
- Concerns about migration risk, data loss, and business downtime.
- Limited in-house expertise in large-scale data migration and AI-driven validation.
To address these challenges, the bank partnered with Ennovision Technology Solutions, leveraging its CoreAI Integrate offering for a risk-free, AI-powered migration journey.
Ennovision’s Solution: CoreAI Integrate
CoreAI Integrate is Ennovision’s AI-powered, migration-focused workbench designed for financial institutions transitioning from legacy systems to digital-native core platforms.
The engagement followed a two-phase approach:
Phase 1: Discovery + Pilot (1 Month)
The Discovery phase ensured a structured, low-risk start to the migration project.
Key activities:
- AI-driven data profiling across core banking, CRM, and transaction systems to assess readiness.
- Integration feasibility study to map existing data to the new platform’s data model.
- Pilot migration workflow with maker-checker validation for governance.
- Comprehensive Discovery Report with readiness scores, mitigation plans, and migration roadmap.
Outcome:
The pilot validated the technical feasibility of migrating to the new digital platform with upto 95% data readiness and identified optimisation opportunities for transformation rules and metadata management.
Phase 2: Production Migration and Go-Live (3–4 Months)
Following successful validation, Ennovision executed a full-scale migration using AI-orchestrated ETL pipelines and automated validation workflows.
Key steps:
1. Migration Scope Finalisation: Defined data entities, business rules, and control checkpoints.
2. ETL Pipeline Implementation: Automated data transformation using AI profiling logic.
3. Maker-Checker Validation: Enabled dual-approval checkpoints, ensuring accuracy and compliance.
4. Cutover and Go-Live: Seamless one-time data migration to the target environment.
5. Post-Migration Support: Parallel run validation, issue resolution, and staff training.
Result:
- Achieved 100% migration traceability and zero data loss incidents.
- Reduced manual effort by 60% through AI-led automation.
- Enabled the client to go live on its new digital banking platform within 4-5 months.
- Improved data governance and compliance reporting, aligning with regional regulatory frameworks.
Key Success Factors
- AI-driven profiling and validation: Automated detection of anomalies and inconsistencies.
- Onsite-offshore delivery model: Combined Middle East–based governance with offshore technical acceleration.
- Full transparency and traceability: Maker-checker workflows ensured auditability at every stage.
- Scalable framework:
Designed for one-time migration with the flexibility to adapt to future upgrades.
| Metric | Before Ennovision | After CoreAI Integrate |
|---|---|---|
| Data Migration Accuracy | Upto 70% | Upto 95% |
| Manual Effort in ETL & Validation | High | Reduced by 60% |
| Time-to-Go-Live | 9–12 months | 4-5 months |
| Regulatory Readiness | Reactive | Proactive with full traceability |
| Customer Onboarding & Processing | Slow & manual | Digital, near-real-time |
