GPU risk engine · Privacy-safe synthetic data

Your overnight VaR run, in minutes.

Proven on your data, inside your perimeter, in six weeks.

QSyn is a GPU-accelerated risk engine for Monte Carlo VaR and ES, credit, liquidity and portfolio optimization. It runs on your infrastructure and plugs into Python. When a team needs data it isn't allowed to touch, QSyn-Data generates privacy-safe synthetic sets for the same pipeline.

Book a benchmark pilot How we prove speed

The simulation on this page is running in your browser right now. Download the notebook and check our math. No email required.

Engine targets, measured in every pilot
≥50M
MC paths / sec on one GPU
<30 s
Market VaR job, full book
pip install
Python SDK, Jupyter-first
On-prem
or your private cloud
10-day 99% VaR · $10M book ready

Monte Carlo simulation of a ten-million-dollar portfolio over ten trading days. Results appear when the simulation finishes.

Paths
1,000,000
× 10 daily steps
99% VaR
—
analytic —
99% ES
—
analytic —
Time
—
1 CPU thread

Reference model: GBM, σ 20%/yr, seeded and reproducible. This is the CPU baseline, not the GPU engine. Hover the chart to read the percentiles.

Notebook
The problem

Three things stall every risk and AI program in finance.

QSyn is built to remove all three, starting with the one your quants feel every night.

01 · SPEED

Overnight is too slow

Monte Carlo, CVaR and XVA stacks built on 1990s architecture turn intraday questions into next-day answers.

02 · PRIVACY

The data can't move

GDPR, DORA and banking-secrecy rules keep real customer and transaction data away from the teams that need it.

03 · GOVERNANCE

Every model needs proof

Reproducibility, lineage and model-risk sign-off (SR 11-7) are mandatory, and usually bolted on after the fact.

The platform

Start with speed. Expand into the loop.

Most teams begin with the risk engine their quants can benchmark this quarter. The synthetic-data engine is the natural next step, and the two feed each other.

QSyn-Risk Start here

GPU-accelerated risk engine

A C++/CUDA simulation core that runs alongside, or replaces, your legacy risk stack, driven from Python.

Best for: trading, risk and quant research desks

  • Market risk: Monte Carlo, historical and delta-gamma VaR, CVaR and Expected Shortfall at GPU scale.
  • Credit & liquidity: Merton and reduced-form models, Gaussian-copula correlation, PD/LGD/EAD, time-to-liquidate curves.
  • Optimization: mean-variance, Black-Litterman and CVaR-constrained portfolios.
  • Quantum-ready: optional QUBO/QAOA backends, always benchmarked against a classical solve.
QSyn-Data Expansion

Synthetic data platform

High-fidelity, privacy-safe datasets for research, model training and testing, without exposing a single real record.

Best for: fintech, regtech and bank data teams

  • Generators: CTGAN, diffusion, TVAE, transformers, and GNNs for transaction networks.
  • Finance-native types: tabular banking, event streams, time series, order books, fraud graphs, exposures.
  • Differential privacy: (ε, δ) calibration, DP-SGD, membership- and attribute-inference tests, re-ID scoring.
  • Evidence: a versioned utility and privacy report for every dataset.

Privacy, enforced

Per-dataset (ε, δ) budgets and re-identification scoring; exports are blocked automatically when risk reads HIGH.

Reproducible by default

Deterministic seeds; every job logs scenario, dataset lineage and model config to an immutable store.

Deploys where data lives

On-prem, private cloud or hybrid; Kubernetes-native, TLS 1.3, RBAC and SSO. Security overview

Why both engines

A closed loop that sharpens with every run.

Each lap, the risk engine finds where losses concentrate and sends those tail scenarios back to the synthetic-data engine, so the next training set covers stress that history barely recorded. Point tools cover one arc of this loop. Press play, or pick a stage.

Step 1 of 3 · QSyn-Data

Synthetic data

In
your real data, which stays inside your perimeter
Out
a privacy-safe synthetic training set

QSyn-Data learns the shape of your data under a differential-privacy budget and generates a synthetic set that matches it.

Real data (never leaves) Synthetic set Stress scenarios from the risk engine Forecast 99% loss threshold
Lap
1 of 4
Tail scenarios in training set
—
Real records shared
0

Illustrative model: 1,000 days of simulated returns, with a simple kernel generator standing in for QSyn-Data's privacy-safe models. It shows the mechanism, not a benchmark.

Performance

Engineering targets, and how we'll prove them.

These are the speedups the engine is designed to hit against a CPU baseline. They stay labeled as targets until they're measured, and in a pilot they're measured on your workload.

Target speedup vs. CPU baseline

one linear axis · bar = target range
Monte Carlo path generationmarket VaR / ES
20–50×
CVaR-constrained optimizationportfolio construction
10–20×
Scenario generationregulatory & custom shocks
5–10×
Synthetic model trainingGPU vs. CPU
3–5×
Target range 1× = CPU baseline

Every published number states

  • Hardware: exact GPU and CPU models
  • Baseline implementation and library versions
  • Problem size: paths, steps, factors, instruments
  • Precision (fp32 / fp64) and error vs. baseline
  • Wall-clock including data transfer
  • A notebook you can re-run yourself

Check the math now

Download the reference notebook: the same 10-day VaR model that's running in the hero, in plain NumPy with an analytic cross-check. It's the CPU baseline our GPU numbers will be measured against.

Download notebook (.ipynb)
No email · NumPy only · runs in about a second
Built for

Where teams start with QSyn.

Every engagement starts with one concrete project and a success metric you choose.

Quant funds & prop desks

Intraday risk, not overnight.

Re-run VaR, ES and the optimizer as positions move, and research on thousands of fresh scenarios from the same Python notebook.

A typical first projectA GPU port of your Monte Carlo VaR, benchmarked against today's run.

Crypto & digital-asset desks

Stress for the tail you haven't seen.

Test 24/7 books against synthetic crash regimes that history hasn't produced yet, at a speed that keeps up with the market.

A typical first projectSynthetic stress scenarios plus intraday VaR on your live book.

Fintech & RegTech

Build on data you're allowed to use.

Models, demos and QA on synthetic customers and transactions, with no production data in dev environments or client sandboxes.

A typical first projectA synthetic twin of one core dataset that passes your privacy review.

Banks

One dataset, three teams.

Fraud, risk and compliance share one privacy-safe dataset instead of queuing three separate access requests.

A typical first projectA synthetic transaction set with a re-identification report for model risk.

Competitive position

The only column with the full row.

Each category of tool leads its own race in isolation. QSyn's ground is the combination: one auditable, finance-native stack. Columns are category archetypes, not specific vendors.

Capabilities of QSyn compared with three categories of tools
Capability QSynintegrated Synthetic-data
platformsSynth. data		 Risk & quant
suitesRisk suites		 Quantum
specialistsQuantum
GPU Monte Carlo VaR / CVaR / ES
Credit & liquidity risk
Synthetic time series & order books
Transaction-graph / fraud data
Differential privacy + audit reports
Quantum / QAOA optimization
Synthetic → risk → synthetic loop
Installed base & track record
Strong / native Partial / adjacent Not offered

Where we're behind: installed base and track record. That's what the design-partner program below is for: every pilot ends with a result you can re-run and, if you choose, a reference.

Free · no sales call

Get the technical whitepaper.

The architecture, the differential-privacy method and the benchmark methodology: what your quants and model-risk team need before a first meeting.

  • Architecture and closed-loop design
  • (ε, δ)-DP method and privacy tests
  • Benchmark methodology
Enter a valid work email.

We use your email only to send the whitepaper and one follow-up. Privacy

3 design-partner slots · Q4 2026

Prove it on your data in six weeks.

A structured, paid pilot with one success metric you set: a speedup on your workload, or a synthetic dataset that clears your privacy bar. You keep the result.

Week 1

Scope & success metric

We agree one measurable goal and the data you'll run it on.

Weeks 2–5

Run in your environment

Deploy in your sandbox, on-prem or cloud. Your quants drive it from Python; we co-develop and tune.

Week 6

Re-runnable readout

A benchmark you can re-run yourself, plus a governance pack for model-risk sign-off.

Format
Paid design partner
Duration
6 weeks
Fee
$10–25K
credited toward your first-year license
You get
Measured result
notebook + governance pack

Book a benchmark pilot

Tell us about your workload. We reply within one business day with a scoped pilot plan.

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Or write to founders@qsyn.finance · Privacy