Products
ALM Studio explains balance-sheet risk. Derivatives Studio delivers sensitivities, P&L explain, and pre-trade analysis. Both use the same valuation, attribution, and audit chain.
Built for
Treasury and ALM teams at banks and financial institutions
Built for
Trading desks, front-office risk, and derivatives operations
Compare where each product starts, what it is built for, and the kinds of questions it answers.
ALM Studio
Treasury, ALM, and balance-sheet risk
Derivatives Studio
Trading desks and derivatives risk
ALM Studio
Loans, deposits, securities, and banking-book derivatives
Derivatives Studio
Trades, portfolios, and desks
ALM Studio
Why did EVE or NII move? What if rates rise? Where did this IRRBB figure come from?
Derivatives Studio
What is the sensitivity profile? What drove the P&L move? What is the risk impact before execution?
ALM Studio
Balance-sheet scenarios, committee review, reporting, and audit trail
Derivatives Studio
Pre-trade analysis, hedging, desk decisions, and trade-to-portfolio risk
ALM Studio
Balance-sheet risk analysis, not treasury operations or payments
Derivatives Studio
Derivatives risk analysis, not trade booking or position management
Shared platform
Valuation, sensitivities, attribution, and audit stay on the same calculation chain, while each product supports a distinct workflow.
Adjoint Algorithmic Differentiation (AAD) calculates sensitivities alongside the valuation.
Decompose changes into rates, volumes, model effects, and assumptions.
Trace results back through the calculation chain, from output to inputs.
Access the valuation engine through Python, C#, Excel, or APIs, and send results to reporting and downstream services.
FAQ
Yes. They support different workflows but use the same valuation engine. Teams can deploy one product first and add the other later.
Valuation, sensitivities, and attribution use the same calculation, while treasury, ALM, and derivatives workflows remain separate.
No. MASTIX connects to existing infrastructure and can consume positions, contracts, curves, market data, and assumptions from current source systems.
Results can be returned through Python, C#, Excel, REST, or reporting integrations. Existing operational systems can remain in place.
Most evaluations start with a benchmark on a representative portfolio. We agree on the questions the team needs to answer first, such as a specific ALM scenario, an attribution view, a margin breakdown, or a sensitivity calculation.
Use real, curated, or synthetic data to compare the results that matter to the team before defining production scope.
For ALM Studio, the useful starting point is a representative balance-sheet slice: contract-level cash flows or position data for the books in scope, valuation curves, and assumptions for non-maturity deposits or behavioral models where relevant.
For Derivatives Studio, the useful starting point is trade economics, market data used for pricing, and curve definitions if you want to bring your own.
Evaluations can begin with a curated subset. Synthetic portfolios can be used when production data is unavailable.
