Balance-sheet analytics with traceable results
For treasury and ALM teams at banks and financial institutions.
How it's different
Links changes in EVE and NII to rate moves, new contracts, and model effects.
Explore ALM Studio
One engine for balance-sheet and derivatives risk.
ALM Studio models the balance sheet from individual contracts. Derivatives Studio covers trading and front-office risk. Both use the same valuation engine, available through Excel, Python, C#, and APIs.
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.
0+
Loans projected per IRRBB run, on standard servers
Bank-scale loan books run on standard server hardware. Larger portfolios just take proportionally longer.
0%
Lower hardware cost—while running faster
In a defined batch ALM comparison, MASTIX completed the same workload faster than the legacy software while running on a workstation costing approximately $3,000.
*Benchmarks run on Intel i9, 16-core CPU, 64GB RAM, SSD (Lenovo ThinkStation P360)
Used in production workflows
Reference results and implementation examples from treasury, ALM, and risk workflows.
Munksjö 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 reporting
Analytical engine for treasury, ALM, and risk 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 across treasury, ALM, and risk reporting
- IRRBB in day-to-day risk management
- Treasury, ALM, and risk reporting on the same engine
Benchmarks based on internal test environments and client implementations.
Why the engine matters
Exact sensitivities computed alongside the valuation
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 each valuation. 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 computed togetherEVE, 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.