We started Plexcore Hub because traditional financial analysis couldn't keep up with market complexity. Today, we're helping institutions make sense of massive datasets through cloud-based machine learning.
From a small research project to a comprehensive machine learning platform that processes billions of financial data points daily.
Our algorithms continuously learn from market behavior, adjusting risk calculations based on emerging patterns. Last month, our system identified a correlation between commodity futures and emerging market currencies that traditional models missed entirely.
Processing 847,000 data points per second across global markets. When Silicon Valley Bank faced liquidity issues in early 2023, our system flagged unusual deposit withdrawal patterns 72 hours before public news broke.
Built on cloud infrastructure that scales from startup portfolios to institutional-grade analysis. Regional credit unions use the same core technology as multinational investment firms, just configured for their specific needs and compliance requirements.
Beyond historical analysis, our models project potential market scenarios based on economic indicators, regulatory changes, and behavioral patterns. Think of it as financial weather forecasting, but with mathematical precision rather than educated guessing.
We analyze portfolio exposure across thousands of variables simultaneously. Instead of traditional correlation matrices, our system builds dynamic risk maps that account for market stress, liquidity constraints, and regulatory capital requirements in real-time.
Our machine learning models identify execution opportunities by analyzing order book depth, market microstructure, and historical price impact. Traders get precise timing recommendations that reduce slippage and improve fill rates across different market conditions.
Automated monitoring for Basel III, Dodd-Frank, and MiFID II requirements. Our system tracks position limits, calculates capital ratios, and generates compliance reports that adapt to changing regulatory frameworks without manual reconfiguration.
We process satellite imagery for agricultural commodity prices, social media sentiment for consumer discretionary stocks, and patent filings for technology sector analysis. This gives our clients information advantages that traditional financial data sources simply can't provide.
