Frequently Asked Questions

Skanalytix builds generative models of financial time series to support synthetic data generation, forecasting, and risk analysis. Powered by the UNCRi framework, the platform captures realistic asset-level and macroeconomic dynamics without relying on rigid parametric assumptions or black-box machine learning. Skanalytix provides scenario-aware simulations that are scalable, interpretable, and ready for portfolio-level analysis — enabling more robust decision-making in quantitative finance.

The Skanalytix modeling framework is designed for quantitative finance professionals and institutions that require realistic, high-quality simulations of financial time series. These include:

• asset managers seeking robust scenario analysis and portfolio stress testing;
• quantitative researchers and modelers needing interpretable, data-driven simulations without rigid assumptions;
• risk management teams looking for forward-looking risk measures that capture path-dependent and systemic market dynamics; and
• fintechs that use synthetic financial data for testing, backtesting, or regulatory purposes.

Whether for research, decision support, or scalable risk modeling, Skanalytix provides a flexible and modern foundation for working with complex financial data.

Classical financial models rely on fixed functional forms — specific equations for jumps, volatility, tail behaviour, and cross-asset dependence. They work well for certain tasks, but they can become rigid, and capturing real market behaviour often requires stacking multiple components on top of one another.
Skanalytix takes a different approach. The UNCRi framework is non-parametric and data-referential: it learns behaviour directly from historical market states rather than assuming a pre-determined probability law. This makes it naturally capable of reproducing complex dynamics such as jumps, volatility clustering, regime shifts, macro sensitivity, and cross-asset interactions within one unified model.

GANs, VAEs, and diffusion models are built on fixed neural architectures with large sets of learned parameters. They require heavy training, are often opaque, and can be fragile in financial settings where data is limited and structural behaviour shifts over time.
UNCRi avoids these issues by using a non-parametric, interpretable design. It stores historical data in memory and generates new paths by referencing the scenarios most similar to the current one. This removes the need for backpropagation, keeps the model transparent, and allows domain knowledge and long-term dependencies to be incorporated without adding architectural complexity.

Many synthetic data methods rely either on simplified statistical assumptions or on opaque machine-learning generators, both of which struggle to capture the path-dependent and system-level structure of real markets.
Skanalytix uses the UNCRi framework to model behaviour directly from historical data, allowing simulated time series to reflect realistic asset dynamics, macro-financial context, and long-horizon state changes. The result is synthetic data that behaves like the real financial system, making it suitable for backtesting, risk analysis, model development, and regulatory use.

Thanks for your interest. Skanalytix is currently in an early deployment phase, and we’re keen to explore opportunities to collaborate with organizations that could benefit from our modeling capabilities.

If you’re interested in synthetic data generation, scenario analysis, or advanced time series modeling, we’d be glad to learn more about your needs. Please get in touch, and we can discuss how best to move forward — whether through custom simulations, exploratory use cases, or a more formal engagement.

If you’re exploring a specific application, we’re happy to work under a non-disclosure agreement (NDA) and can offer short proof-of-concept (PoC) collaborations to help assess how our model fits your needs.