About Us

At Synthera, we've built state-of-the-art generative AI models that capture what traditional financial approaches miss: the deeper structure, non-linear relationships, tail risks, and complex patterns embedded in historical financial market data. These insights enable our models to generate forward-looking synthetic data—highly realistic simulations of yield curves, FX, commodities, and other financial instruments that reflect real-world market behaviors.

The fundamental problem with traditional financial models—whether for optimization, risk assessment, or simulation—is their parametric (non-AI) nature. Conventional models are a formulas with a limited set of parameters (usually tens), capturing only a narrow subset of patterns in historical market data. They rely on reductive top-down statistical assumptions, such as returns being normally distributed, that fail to reflect market reality.

Our machine learning approach represents a paradigm shift, scaling from dozens of parameters to millions. Built by a team of quantitative finance and machine learning experts, our technology learns the full distribution and structure of market behavior directly from the data itself. This exponential increase in model capacity allows us to capture significantly more structure, non-linear correlations, cross-curve dynamics, regime changes, and rare but high-impact events that traditional models systematically overlook.

This breakthrough provides financial institutions with both superior simulated scenarios for stress testing and risk management, and powerful analytical capabilities that deliver far deeper insights into the complex web of correlations underpinning financial markets—directly enhancing investment decision-making in ways conventional models simply cannot.