Skanalytix Pty Ltd

High-Fidelity Synthetic Financial Data Skanalytix generates realistic synthetic time series for financial assets, including equities, yield curves, and macro-conditioned scenarios, powered by our proprietary UNCRi framework. Our models go beyond point forecasts, producing scenario-aware conditional distributions that reveal the full range of plausible market outcomes. Our platform helps financial institutions, fintechs, and researchers: • Backtest and compare strategies across varied market conditions • Generate forward-looking VaR and CVaR estimates under stress • Fill gaps in incomplete or sparse datasets • Train ML models on realistic, history-conditioned, high-variance time series Unlike GANs, VAEs, and other deep learning approaches, our framework is nonparametric, data-efficient, and interpretable, capturing volatility clustering, tail risk, regime shifts, and cross-asset dependencies even with limited historical input. The result is synthetic financial data that behaves like real markets, revealing risk and uncertainty in ways that single-point forecasts cannot. After several years of independent development, we are now exploring strategic partnerships as we bring our technology to market. Visit skanalytix.com to learn more or get in touch.