At Northhaven Analytics, we don’t generate random numbers — we reconstruct financial logic. Our synthetic datasets emulate real-world behavior through probabilistic modeling, constraint-based generation, and domain-specific validation. Each dataset follows measurable, explainable, and reproducible patterns that mirror how actual markets and clients behave.
Data architecture based on financial logic
Every dataset starts with a schema built around realistic economic variables. For example:
- Income → Credit Score → Balance dynamics: higher income statistically correlates with improved credit score and higher average balances, with controlled exceptions to simulate credit mismanagement.
- Region ↔ Country coherence: regional codes and currencies match ISO mappings (e.g., client in “Mazowieckie” must belong to “PL”).
- Behavioral clustering: clients are distributed by activity class (active/passive/inactive) based on Poisson-like patterns of transactions per month.
This ensures every dataset can be plugged directly into financial models — without needing cleansing or remapping.
Controlled randomness and noise modeling
To maintain diversity and prevent overfitting, we inject controlled Gaussian noise into continuous variables such as income, balance, and transaction volume. This allows machine learning models to learn general relationships instead of memorizing samples.
Example:balance = income * savings_rate * months_active + ε, where ε ∼ N(0, σ²).
For categorical variables, random dropout and synthetic missingness are introduced to mimic real data imperfections — such as incomplete employment info or outdated client records.
Correlation-preserving generation
Each dataset is built from dependency matrices that define realistic relationships between key metrics:
| Variable 1 | Variable 2 | Correlation | Example Logic |
|---|---|---|---|
| Income | Credit Score | +0.72 | higher earnings, higher creditworthiness |
| Income | Avg. Transaction Value | +0.41 | spending habits scale with income |
| Account Age | Balance | +0.56 | long-term clients tend to save more |
| Credit Score | Overdraft Usage | −0.39 | riskier profiles use overdrafts more often |
These constraints make synthetic data structurally similar to real datasets used in banking or asset management.
Temporal and behavioral simulation
Financial data is not static — it evolves.
We simulate seasonality, volatility clusters, and spending cycles by integrating temporal features into synthetic generation.
Examples:
- Increased retail transactions in December (seasonality factor +0.25).
- Reduced activity during weekends and holidays.
- Short-term balance fluctuations reflecting salary inflows and recurring bills.
This temporal logic enables stress testing of ML models under realistic market conditions — something no static dataset can achieve.
Validation and compliance
Every dataset passes through a three-stage validation pipeline:
- Statistical validation: distributional tests (KS, Chi-square) to confirm similarity to expected profiles.
- Logical consistency: checks for impossible states (e.g., underage clients, negative balances > overdraft).
- Privacy verification: irreversible data generation — zero overlap with any real client record.
All datasets are GDPR-compliant by design, generated exclusively from probabilistic models rather than anonymized data, ensuring complete legal safety for enterprise users.
Our mission is simple:
to deliver synthetic financial data that behaves like the real market — but carries none of its risks.
That’s what makes Northhaven Analytics the data backbone for the next generation of quantitative finance.
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