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Banking & Financial Services

Financial Services Organization

Platform consolidation and AI-driven customer experience transformation

The Business Challenge

A mid-sized financial services firm operated 23 disparate platforms accumulated through acquisitions and organic growth. This fragmentation created data silos, prevented unified customer views, and resulted in escalating operational costs. Customer experience suffered from inconsistent interactions across channels.

Key Issues

  • 1
    23 platforms with 70% functional overlap
  • 2
    No single source of truth for customer data
  • 3
    $45M annual infrastructure and licensing costs
  • 4
    18-month average integration timeline for new acquisitions
  • 5
    Inability to launch digital-first products
  • 6
    Regulatory reporting complexity across fragmented systems

Strategic Architecture Approach

Executed a Capital Lightning program focused on aggressive platform consolidation, establishing a unified data foundation, and implementing AI-driven customer intelligence. Applied value stream mapping to eliminate redundancy and optimize operating model.

1

Phase 1: Discovery & Rationalization

Platform assessment and consolidation strategy

  • Application portfolio analysis (capability mapping)
  • Technical debt quantification ($78M identified)
  • Rationalization roadmap (23 → 7 platforms)
  • Business capability model and value stream mapping
2

Phase 2: Foundation & Data

Unified data platform and master data management

  • Modern data platform (cloud data lake + warehouse)
  • Customer 360 master data management
  • Real-time data integration architecture
  • AI/ML platform for customer intelligence
3

Phase 3: Platform Consolidation

Core platform migration and decommissioning

  • Consolidated core banking platform
  • Unified customer engagement layer
  • API-first integration architecture
  • Legacy system retirement (16 platforms)

Transformation Roadmap

Q1

Q1: Assessment

  • Platform portfolio assessment completed
  • Value case development ($45M → $28M target)
  • Stakeholder alignment and governance
  • Quick win identification
Q2

Q2-Q3: Data Foundation

  • Data lake and warehouse deployment
  • MDM platform implementation
  • Data quality and governance framework
  • AI/ML platform setup
Q3

Q4-Q5: Platform Migration

  • Core banking consolidation (Phase 1)
  • Customer data migration and validation
  • API layer implementation
  • First legacy system decommissioning (3 platforms)
Q4

Q6-Q8: Scale & Optimize

  • Remaining platform migrations
  • AI-driven customer intelligence launch
  • Legacy decommissioning completion
  • Cost optimization realization

Technology Landscape

Platforms

  • • Azure (primary cloud) - AKS, Azure SQL, Synapse, Data Lake
  • • Databricks for data engineering and ML
  • • Informatica MDM for master data management
  • • MuleSoft for API management and integration
  • • Temenos core banking platform

Frameworks

  • • Event-driven architecture (Azure Event Hub, Kafka)
  • • Microservices with .NET and Java Spring Boot
  • • React for web, Flutter for mobile
  • • Azure DevOps for CI/CD automation
  • • Power BI for analytics and reporting

Practices

  • • TOGAF and business capability modeling
  • • Value stream mapping for capability optimization
  • • Cloud-native application design
  • • DataOps for data pipeline automation
  • • FinOps with Azure Cost Management

Business Outcomes

29% ($13M annually)
Cost Reduction

Infrastructure, licensing, and operational cost savings

23 → 7 platforms
Platform Consolidation

70% reduction in platform complexity

$78M → $23M
Technical Debt

71% reduction in technical debt burden

Unified 360°
Customer View

Single source of truth across all channels

18 → 4 months
Integration Speed

Acquisition integration timeline reduced 78%

15 use cases
AI Adoption

Customer intelligence, fraud detection, personalization

Key Lessons

**Value-driven prioritization**: Business capability mapping identified high-impact consolidation opportunities

**Data before platforms**: Unified data foundation enabled parallel platform migrations

**Quick wins build momentum**: Early decommissioning victories secured leadership buy-in

**Governance is critical**: Strong architecture decision records (ADRs) prevented scope creep

**Change management investment**: User training and adoption programs were key to success

**Measure everything**: Detailed cost tracking proved ROI and justified continued investment

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