Balance-sheet analytics with full traceability
For treasury and ALM teams at banks and financial institutions.
How it's different
Explains exactly why EVE and NII moved, down to rate changes, new contracts, and model effects.
Explore ALM Studio
Two products. One engine.
ALM Studio models the balance sheet from individual contracts. Derivatives Studio covers trading and front-office risk. Both share the same analytical core, with Excel, Python, C#, and API workflows.
Built for interactive analysis
Benchmark figures from the reference workstation in the footnote.
0/s
Swap valuations with full sensitivity profile
Price and sensitivities come out of the same calculation, in one pass.
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Loans projected per IRRBB run, on standard servers
Bank-scale loan books run on standard server hardware. Larger portfolios just take proportionally longer.
0x
Up to 100x lower hardware cost than legacy ALM platforms
Runs full bank ALM on thousands of dollars in hardware. Legacy grid systems require hundreds of thousands for the same outputs.
*Benchmarks run on Intel i9, 16-core CPU, 64GB RAM, SSD (Lenovo ThinkStation P360)
Where it holds up in practice
These are the kinds of teams and workflows where speed, explanation, and review all matter at once.
Munksjö corporate treasury
Analytical layer alongside the Treasury Management System
Selected ALM Studio after evaluating multiple alternatives for broad instrument coverage, workflow flexibility, and direct access from Excel and Python.
In use for
- Broader balance-sheet risk coverage than a TMS-only setup
- Direct hands-on analysis from Excel and Python
- Credit risk modeling on accounts receivable
European bank: treasury, ALM, and risk control
Analytical engine for risk management, capital planning, and reporting
A European bank is implementing ALM Studio across treasury, ALM, and risk reporting, with the platform already in use for day-to-day risk management.
In use for
- Full balance-sheet modeling on one engine
- IRRBB and scenario analysis in day-to-day workflows
- Risk management, capital planning, and reporting on the same engine
Why the engine matters
Exact sensitivities from the valuation itself
The challenge is not just calculating the result. It is calculating the result and its drivers fast enough to use interactively.
MASTIX uses Adjoint Algorithmic Differentiation (AAD) to compute sensitivities as part of the valuation itself. The same calculation produces prices, sensitivities, and attribution, so scenarios can be analyzed without separate runs for each output.
AAD-based exact sensitivitiesValuation and sensitivities in one passEVE, NII, and attribution from one engineCash-flow-level traceability
When valuation, sensitivities, and attribution come from the same cash-flow engine, analysis becomes faster, more consistent, and easier to explain.