Your financial AI teams are ready. Their data isn't.
Take your financial data further — generate, anonymize, and simulate diverse datasets for fraud detection, risk modeling, compliance testing, and model fine-tuning. Starting from your own samples. Your data never has to leave.
What's blocking your financial AI team?
Your transaction and document data is off-limits.
Anonymize or transform it — structure intact, sensitive content and PII removed. GDPR and SEC compliant by design.
A — Anonymize, AugmentFraud patterns, market crises, and rare events are too sparse to train on.
Simulate the edge cases and scenarios your real data never captured — labeled and ready.
S — SimulateYour models need more diverse financial data than you have.
Generate diverse, scaled datasets from your own samples — loan applications, P&L statements, transaction streams, KYC documents.
G — GenerateWhy financial AI teams are blocked
| Challenge | Description |
|---|---|
| Data Privacy & Security | Financial data is highly sensitive and regulated, requiring robust privacy protection and compliance with data governance standards across borders and jurisdictions. |
| Rare Event Simulation | Fraudulent transactions, AML violations, and market crises are rare in real data, making it difficult to train robust AI models for detection and risk assessment. |
| Regulatory Compliance & Governance | Financial AI must comply with complex regulations like Basel III, GDPR, SEC requirements, and various financial authorities' standards while maintaining transparency and auditability. |
| Risk Management & Accuracy | AI systems for credit scoring, fraud detection, and trading must achieve high accuracy to avoid financial losses and regulatory penalties. |
| Bias & Fairness in Financial Decisions | AI systems must be tested across diverse demographics to ensure equitable treatment and avoid discriminatory practices in lending, credit scoring, and financial services. |
Works from your financial data — adding diversity while preserving structure and constraints.
Diverse, distribution-tuned datasets. DataFramer starts from your real samples — transaction logs, loan documents, trading data, insurance submissions — and extends them faithfully. Every output respects the schema, value ranges, regulatory constraints, and relationships your models depend on.
Any textual dataset. Nested JSON, XML, multi-file document packages, high-token PDFs like P&L statements, income statements, legal contracts, real-estate documents, transaction streams — any format, any complexity.
How DataFramer solves it
Each solution starts from your own data — no random generation, no fabricated inputs.
| Solution | Description |
|---|---|
| Fraud Detection & AML Enhancement | Generate synthetic datasets with rare fraud scenarios, AML violations, and synthetic identities to improve detection accuracy without exposing real customer data. |
| Bias Correction & Fairness | Synthetic data augments underrepresented groups across demographics and geographies, improving fairness in lending decisions and credit scoring models. |
| Market Simulation & Stress Testing | Create synthetic trading data to simulate rare crises, emerging asset classes, and regulatory scenarios for safe strategy testing and risk assessment. |
| Privacy-Preserving Development | Synthetic data allows development and testing without exposing real financial information, maintaining compliance throughout the AI development lifecycle. |
| Compliance-Driven Data Sharing | Built-in differential privacy and anonymization enable cross-team and cross-border data sharing without exposing PII, supporting GDPR and SEC requirements. |
Use Cases
| Use Case | Description |
|---|---|
| Fraud Detection & AML | Synthetic transaction data enables rare-event simulation for fraud patterns, money laundering detection, and KYC verification without exposing real customer information |
| Credit Scoring & Risk Modeling | Generate synthetic datasets to test models under market shocks, defaults, and regulatory scenarios while improving fairness across demographics |
| Capital Markets & Trading | Create synthetic market data to simulate rare crises, test trading algorithms, and validate investment strategies in safe environments |
| Payments & Fintech | Test real-time fraud controls, cross-border payments, and digital wallet features using synthetic transaction streams labeled 'Safe for testing' |
| Compliance & Governance | Built-in privacy transformations enable cross-team collaboration with audit-ready lineage reports supporting regulatory requirements |
| Financial Document AI | Generate and anonymize high-token financial documents — P&L statements, income statements, loan applications, KYC documents — with structure, schema, and regulatory constraints preserved. Train and test document processing models without exposing real customer records. |
Built for financial data regulation
DataFramer works within the regulatory constraints that govern financial AI — not around them. Every dataset generated or anonymized preserves the structural fidelity your compliance and audit teams require, while removing what cannot leave your governance boundary.
Secure experimentation without moving your data
Financial AI teams need to run experiments — model training, eval calibration, stress testing — but production data can't leave the approved boundary. DataFramer lets you work from your own samples inside your own environment. Cloud or on-prem. No data movement, no governance violations, no compliance risk.
Key Benefits
| Benefit | Description |
|---|---|
| Your data never has to leave | Run experiments on synthetic data inside your own environment — cloud or on-prem. No compliance violations, no governance risk. |
| Accurate outputs at scale | In-built revision loops enforce distribution accuracy and schema fidelity across high-volume outputs — not just sample checks. |
| Control your distributions | Analyze seed samples and define exactly what you need — fraud ratios, demographic splits, regional variance, time periods. Your output reflects your world, not a generic one. |
| Long-form document support | Generate and anonymize high-token financial documents — P&L statements, income statements, loan applications, legal contracts — with structure and constraints preserved. |
| Audit-ready from day one | Built-in lineage and privacy controls satisfy regulators without custom compliance work. HIPAA, GDPR, SOC 2 Type II. |
| Lower cost at every step | Choose your model at each generation step — OSS, small, or large LLMs. Revision loops reduce human labeling costs. Optimized generation lowers cost per sample. |
Common questions
Does DataFramer work with data that can't leave our secure perimeter?
Yes. DataFramer deploys on-prem inside your own environment — Databricks, AWS, or your own cloud. Your data never has to move.
Is the output statistically faithful to our real financial data?
DataFramer starts from your own seed samples — it doesn't generate from scratch. Outputs preserve your schema, value distributions, and domain constraints. Built-in distribution comparison lets you verify fidelity before anything touches your model.
What financial data formats and document types does DataFramer support?
Any textual financial dataset — transaction logs, nested JSON records, multi-file document packages, high-token PDFs like P&L statements and income statements, KYC documents, legal contracts. Any format, any complexity.
How does this compare to building a synthetic data pipeline in-house?
DataFramer provides distribution control, automatic revision loops, multi-format support, and distribution comparison validation out of the box. Building and maintaining these capabilities in-house typically takes months and requires ongoing engineering investment.
"Real production data can't leave the approved boundary. Synthetic and anonymized datasets enable safe experimentation without governance violations."
See what DataFramer does with your data.
Send us a sample dataset and we'll show you what's possible — diverse, faithful outputs in your format, your schema, your constraints.