Monte Carlo engine
Beyond spreadsheet pricing. A production-grade Monte Carlo core built to price optionality, path-dependent payoffs and structured products consistently.
Product / SciQuant
Pricingand validationwith regulatoryrigor.
SciQuant helps trading desks and risk teams price complex instruments and defend valuation methodology without spreadsheet fragility — a Monte Carlo engine designed for pricing, validation and model risk workflows.
Quant FinanceModel Risk ManagementMarket RiskPricing · Validation
Product thesis
Pricing and validation are scientific problems.
Complex instruments and model risk decisions cannot be resolved by more spreadsheets. They require an explicit Monte Carlo engine, defendable methodology and workflows that survive independent review by risk and regulators.
Defendable prices
Prices, benchmarks and reports. Generate valuations, benchmarks and audit-ready reports for instruments with optionality — one methodology across the desk.
Methodology that survives review
Independent valuation-style workflows. Designed for model validation and risk teams to challenge, reproduce and document pricing methodology.
No more spreadsheet fragility
Version, test, deploy. Replace opaque workbooks with a reproducible engine — versioned assumptions, automated tests and traceable outputs.
Applications
One quant engine, multiple financial workflows.
SciQuant is a reusable product family spanning pricing, validation and model risk across banks, asset managers and desks.
Pricing complex instruments
Price derivatives and structured products with optionality using a Monte Carlo core — consistent, reproducible and defendable across the book.
Independent model validation
Independent valuation-style workflows for MRM teams: challenger models, benchmarks and documentation that survives regulatory review.
Market risk & scenario analysis
Run VaR, stress and scenario analysis on the same engine used for pricing — a single source of truth across front office and risk.
Regulatory & audit reporting
Audit-ready reports for regulators and internal audit: reproducible pricing, documented assumptions and traceable methodology.
Implementation path
From proof of concept to OEM integration.
SciQuant is delivered as a journey: replicate current pricing, validate methodology with risk and MRM, then integrate as a component or OEM inside the client stack.
PoC: replicate & benchmark
Run SciQuant against the existing pricing stack to reproduce results, quantify differences and identify where methodology can be strengthened.
Validate with risk & MRM
Move to independent validation with model risk and market risk teams — challenger runs, sign-off and documentation ready for regulators.
OEM integration
Embed SciQuant as a pricing / validation component inside the client's platform, with defined interfaces, tests and release management.
Next step
Bring SciQuant to the book that can't be defended by a spreadsheet.
Start with a 2–4 week diagnostic: pick a portfolio, reproduce prices, compare methodology and propose the first validation loop.