Financial Data Platform

A normalized, governed financial data foundation engineered for scale, compliance, and advanced analytics.

Platform Architecture

AI-integrated lakehouse architecture for transaction processing and regulatory normalization.

Financial Data Platform Architecture

Architecture Walkthrough

Ingestion: Multi-source streaming and batch ingestion from ERP, CRM, core banking.

Processing: ETL/ELT validation, reconciliation, transformation.

Normalized Model: Canonical schema for transactions, balances, exposures.

Lakehouse Storage: Optimized structured storage for analytics & ML.

AI Layer: Governed LLM access for explainable insights.

Data Pipeline Architecture

  • Event-driven ingestion
  • Batch historical processing
  • Data validation checkpoints
  • Metadata & lineage catalog
  • Partitioned performance optimization

Use Case – Multi-Entity Consolidation

Regional subsidiaries feed standardized transaction data. Currency normalization and entity mapping consolidate balance sheets centrally with AI anomaly detection.

Outcome: Improved reconciliation accuracy and compliance readiness.

Enterprise Outcomes

  • Reduced reporting cycle time
  • Improved financial transparency
  • AI-ready data foundation
  • Scalable compliance architecture