Unlock What Real Data Can’t

Synthetic financial data that looks real and performs like the real thing. At Skanalytix, we generate high-fidelity time series for equities, yields, and beyond—empowering data-driven finance without compromise.

Bridging the Gap Between Data Limitations and Financial Innovation

At Skanalytix, we specialize in generating high-fidelity synthetic data to power the next generation of financial analytics. As the demand for robust, privacy-preserving, and representative data continues to grow—especially in heavily regulated and data-sensitive industries—our solutions enable financial institutions, fintech companies, and researchers to push the boundaries of what’s possible with artificial data.

Focused on Synthetic Time Series for Finance

Our current focus is on generating realistic synthetic time series data, particularly for applications in equity price simulation and yield curve modeling. These synthetic datasets retain the essential statistical properties, dependencies, and temporal structures of real-world financial data, making them ideal for:

  • Backtesting trading strategies
  • Stress-testing risk models
  • Augmenting scarce or private datasets
  • Scenario generation and forecasting
  • Model validation under privacy constraints

Whether you’re modeling asset prices, simulating interest rate movements, or constructing robust training data for machine learning models, Skanalytix provides powerful tools to generate realistic, high-utility synthetic alternatives.

Powered by the UNCRi Framework

Underpinning our synthetic data technology is the Unified Numerical-Categorical Representation and Inference (UNCRi) framework. Originally designed as a general-purpose tool for complex tabular data, UNCRi offers a graph-based, mixed-type data modeling approach that is especially well-suited for financial data, which often includes numeric time series interspersed with categorical factors such as regime labels, market segments, or macroeconomic indicators.

UNCRi allows us to flexibly estimate joint and conditional probability distributions across variables, enabling robust simulation, imputation, and probabilistic reasoning—all essential components for generating plausible and analytically useful synthetic financial data.

Ready to Learn More?

Whether you’re a hedge fund exploring new quantitative strategies, a fintech innovating under data constraints, or a research team overcoming limited data access, Skanalytix can help. Our platform and expertise provide a powerful, flexible foundation for financial innovation driven by synthetic data.