Quantitative finance & risk engines
Banks and asset managers price instruments and measure risk with models, and regulators expect those models to be understood, documented and validated. QuantLib is a free, open-source quantitative finance library in C++ with bindings for Python, C#, Java and R, released under a modified BSD licence. The Open Source Risk Engine (ORE), sponsored by Post Trade Solutions (formerly Acadia), builds on QuantLib for risk analytics and valuation adjustments (XVA), with XML and API interfaces and Python and Jupyter launchers.
What we do
- QuantLib and ORE environments with reproducible runs
- Interest-rate and liquidity risk models
- Expected-credit-loss and CECL data pipelines
- Python and Jupyter analytics for risk teams
- Model documentation, versioning and validation support
- Reconciliation to ledgers and committee-ready reports
We use these libraries to bring analysis in-house: interest-rate risk such as economic value of equity and net interest income under rate shocks, deposit-behavior models and expected-credit-loss work, fed from public data and the institution's own systems. Every model gets versioned code, reproducible runs, documented assumptions and independent testing, which is what model-risk guidance asks for.
The engineering matters as much as the maths: data pipelines with lineage, scheduled runs, results reconciled to the general ledger, and reports for a risk committee that a non-programmer can read. We deliver and explain; the institution's model owners and validators stay responsible for their models. See tailored software and cloud management.
Leading platforms we work with
- QuantLib
- Open Source Risk Engine (ORE)
- Bloomberg
- Moody's
- FIS
- Numerix
Platforms and practices
- Yield-curve construction
- Interest-rate risk (EVE and NII)
- Deposit and prepayment modeling
- CECL and expected-loss analytics
- XVA and exposure simulation
- Python and Jupyter workflows
- Public data pipelines
- Model documentation and validation support
- Backtesting and reconciliation
References
Sources last checked .
Product names are trademarks of their owners and are listed to describe our experience; they do not imply endorsement or partnership.
From public data to the ALCO package
Public market and regulatory data is combined with the bank's own instrument-level extracts in a private data store, reconciled to the general ledger, run through a QuantLib and ORE cash-flow engine, and delivered to the Asset Liability Committee as EVE, NII and CECL results with every assumption visible.
The same rate shock, two answers
Sensitivity of a bank's economic value of equity (EVE) and net interest income (NII) to a 200 basis-point rate move, from the vendor model and from an in-house model with deposit betas fitted to the bank's own account history. EVE is the present value of assets minus liabilities; NII is the interest earned minus interest paid over the next 12 months. The in-house model shows more value at risk when rates rise, because the bank's deposits reprice faster than the vendor default assumed. Illustrative parallel-run figures.
How we use Quantitative finance & risk engines for clients
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Direct answers to the questions we hear most. Something else on your mind?
Send us a messageWhat are QuantLib and ORE?
QuantLib is a free, open-source quantitative finance library in C++ with bindings for Python, C#, Java and R, released under a modified BSD licence. The Open Source Risk Engine (ORE), sponsored by Post Trade Solutions (formerly Acadia), builds on it for risk analytics and valuation adjustments (XVA).
Why use open-source libraries instead of a vendor system?
They let an institution own and inspect its models, with versioned code and reproducible runs. They are not free of cost, since you still need engineering and validation. We help judge when in-house analysis makes sense and when a vendor system is the better choice.
Does this meet model-risk guidance?
It supports it. Every model gets versioned code, reproducible runs, documented assumptions and independent testing, which is what model-risk guidance asks for. The institution's model owners and validators stay responsible for their models, and we do not certify compliance.
What risk analysis can you build?
Interest-rate risk such as economic value of equity and net interest income under rate shocks, deposit-behavior models, expected-credit-loss work, and exposure simulation. They run from public data and your own systems. Each project starts with the question the risk committee needs answered.
What data does it run on?
Public data pipelines plus the institution's own systems, with lineage tracked so you can show where each number came from. Results are reconciled to the general ledger and delivered as reports that a non-programmer on a risk committee can read.
Do you replace our model validators?
No. We deliver and explain the work, including documentation and validation support, but independent validation and ownership of each model remain with your institution. We also do not claim any regulator's approval of the output.