Driving Compliance, Speed, and Smarter Models in Finance with Synthetic Data
Power fraud defense, risk modeling, and compliance with privacy-safe synthetic data — accelerating innovation while protecting trust.
"Real production data can’t leave the approved boundary. Synthetic and anonymized datasets enable safe experimentation without governance violations."
VP, Data Science, CapitalOne
Key Challenges
| Challenge | Description |
|---|---|
| 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. |
| Data Privacy & Security | Financial data is highly sensitive and regulated, requiring robust privacy protection and compliance with data governance standards across borders and jurisdictions. |
| 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. |
| 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. |
Our Solutions
| 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 |
Key Benefits
| Benefit | Description |
|---|---|
| Accelerated Innovation | Cut months from model development by removing data bottlenecks and enabling safe cloud experimentation |
| Regulatory Peace of Mind | Synthetic datasets are fully compliant with Basel III, GDPR, SEC, and financial authority requirements |
| Enhanced Risk Management | Comprehensive testing across rare events, edge cases, and demographic variations improves model accuracy |
| Fair & Inclusive Models | Correct imbalances in datasets for more equitable lending, credit scoring, and financial service outcomes |
| Trust & Compliance | Reduce reliance on risky anonymization scripts and ensure regulatory alignment from day one |
| Cost Efficiency | Eliminate expensive real data acquisition while maintaining data quality and representativeness |
"We strive to start each relationship with establishing trust and building a long-term partnership. That is why, we offer a complimentary dataset to all our customers to help them get started."
Ready to Get Started?
Contact our team to learn how we can help your financial services organization develop AI systems that meet the highest standards.