Northhaven Analytics is developing a specialised synthetic data engine designed exclusively for the financial sector.
We create high-fidelity, correlation-aware datasets for banks, hedge funds, fintechs and quantitative research teams—enabling AI development, model training and risk analysis without relying on real customer data.
Our mission is to provide financial institutions with the safest, most realistic alternative to sensitive datasets, built on a foundation of deep domain logic, transparency and reproducibility.
Investment Thesis
The financial world is moving toward privacy-first AI.
Regulations (GDPR, banking secrecy, the upcoming EU AI Act), internal data-access barriers and strict model validation standards make it increasingly difficult for institutions to use real data in research.
Synthetic financial data solves this problem—but general-purpose generators fail to capture the complexity, behaviour and regulatory constraints unique to finance.
Northhaven is building a specialised engine that reflects the true structure of financial systems: correlations, lifecycle behaviour, risk dynamics, anomalies and domain logic.
Financial teams want realistic, compliant data.
We provide exactly that.
Market Opportunity
The global synthetic data market is expanding rapidly, driven by:
- increased regulatory pressure on real-data usage
- rising demand for AI-ready datasets
- the need for faster model development and validation
- adoption of synthetic data in banking, insurance and trading
Finance is the single largest segment of the synthetic data industry—and the one with the highest technical barriers.
This is where Northhaven operates.
Problem We Solve
Financial institutions face critical challenges:
- limited access to real customer data for training models
- long internal approval cycles for data access
- high risk of compliance violations
- legacy systems that prevent scalable experimentation
- general synthetic platforms that ignore financial logic
These barriers slow down innovation, increase cost and create operational bottlenecks.
Northhaven provides domain-accurate datasets that model true financial behaviour, enabling safe development of AI and quantitative systems.
Our Solution
Northhaven Analytics delivers:
- synthetic datasets that mirror real-world financial patterns
- multi-entity structures (clients, accounts, transactions)
- behavioural modelling based on income, spending patterns, credit risk and region
- scenario and lifecycle simulation
- reproducibility architecture for model validation
- full compliance alignment (privacy-by-design, NDA workflows, secure delivery)
We recreate financial logic—not raw data.
Technology Overview
Our engine combines:
- advanced dependency modelling
- probabilistic and rule-based generation
- correlation-preserving algorithms
- domain-specific constraints
- validation frameworks ensuring statistical integrity
Designed by a dual-founder team blending quantitative finance and data engineering, the system is built to scale into an enterprise-grade platform.
Traction
Despite being early-stage, Northhaven has:
- a working MVP generator
- complete dataset structure & metadata architecture
- internal validation and consistency checks
- early interest from financial professionals and quant teams
- a clear roadmap to enterprise deployment
A free demo dataset is provided to qualified institutions after a technical consultation.
Business Model
Northhaven operates on:
- one-off project fees for custom datasets
- enterprise simulation packages
- annual or quarterly subscriptions
- advisory and validation services
This structure aligns directly with the purchasing habits of financial institutions and research teams.
Roadmap
Phase 1 — Product Foundation (Current)
Core generator engine, metadata + validation suite, financial logic modules.
Phase 2 — Enterprise Architecture (Next 6–12 months)
Security layer, versioning, multi-domain modelling, institution-specific constraints.
Phase 3 — Market Expansion (12–24 months)
Strategic partnerships, SaaS portal for dataset requests, automated pipeline deployment.
Founders
Oleg Fyłypczuk — Financial Modeling & Product Vision
Oleg brings deep experience in quantitative finance, financial modelling, risk analysis and product-led growth. He defines the behavioural architecture behind Northhaven’s datasets and ensures alignment with real institutional needs.
Gabriel Wiśniewski — Data Engineering & Algorithmic Architecture
Gabriel designs the technical foundation of Northhaven: generation engines, backend architecture, constraint logic, validation frameworks and scalable pipelines. His work ensures that our datasets remain structurally consistent, reproducible and enterprise-ready.
Investment Opportunity
Northhaven Analytics is preparing a pre-seed round to accelerate development, expand core modules and secure early institutional partnerships.
Funds will support:
- advanced modelling R&D
- expanded security and compliance infrastructure
- enterprise-grade delivery systems
- business development and strategic outreach
We welcome conversations with investors specialising in:
- fintech
- AI/ML
- data infrastructure
- quantitative finance
- deep tech