Big-Data Architectural Renovation: 100x Speed & 10x Cost Reduction

The Client

The fintech SaaS platform operates in the mission-critical infrastructure layer of enterprise finance, providing comprehensive financial reference data that Fortune 100 institutions depend on for risk management, compliance reporting, portfolio analysis, and trading decisions. Their platform aggregates and normalizes financial information from thousands of sources—company financials, securities data, ownership structures, corporate actions, regulatory filings.

In the financial services industry, reference data accuracy and timeliness are non-negotiable. Success brought explosive demand growth: new regulations increased the scope of reference data needed, driving a 10x surge in processing requirements. The infrastructure couldn’t handle it.

The Challenge

The technical crisis was rooted in architectural debt. As data volumes grew exponentially, system performance degraded non-linearly. Processing that took 30 minutes with 10 clients was now taking 6+ hours with 50 clients, with projections showing 12-24 hour processing times at 100+ clients.

Clients accessing the platform expected current data but received stale information. Monthly infrastructure bills were growing faster than revenue. The existing system had grown organically, resulting in tightly coupled code difficult to optimize or parallelize. The solution needed to maintain absolute data accuracy—in fintech, a single data error could result in compliance violations or legal liability.

Our Solution

The transformation was a ground-up rebuild migrating to AWS, implementing cloud-native patterns optimized for massive-scale financial data processing. We designed distributed processing architecture leveraging S3 for storage, elastic EC2 compute, and AWS batch services—distributing work across dozens of compute nodes working in parallel.

The breakthrough innovation was integrating AI automation for company record alignment—previously a slow, manual process. The AI-powered matching system learned from historical decisions to automatically identify high-confidence matches, flag ambiguous cases for human review, and continuously improve accuracy. We implemented streaming processing, incremental updates, and intelligent caching, all while preserving every validation rule and quality check from the legacy system.

The Impact

Processing speeds improved up to 100x—jobs that took 6-8 hours completed in minutes. Infrastructure costs decreased 10x through elastic scaling. The architecture handled 10x demand surge without degradation, eliminating the business growth constraint.

The AI-powered alignment system delivered unexpected strategic value—improved matching accuracy enhanced data quality, the core value proposition. The infrastructure became a competitive moat, enabling the fintech platform to maintain market leadership as reference data requirements continued expanding.