Generate safe synthetic financial data at scale
Synthetic financial data with limitless possibilities
Generate Safe Synthetic Data for Analytics, ML, and Software Development.
- Risk Modeling
Utilize synthetic data to simulate various economic conditions and market fluctuations to enhance risk models and decision-making.
- Fraud Detection
Improve fraud detection models by generating diverse synthetic data to simulate various types of financial fraud.
- Personalized Banking Services
Create personalized banking services using synthetic data, ensuring customer privacy through differential privacy, allowing for innovation without compromising sensitive information.
- Software Development
Unlock data access for non-production environments increasing development cycles and productivity while eliminating sensitive data exposure.
Synthesize Financial Data for Training LLMs and Conversational AI
- Customer Service
Improve customer service bots by training them with synthetic data, enabling better handling of client queries and issues related to banking and finance.
Develop sophisticated conversational AI for investment advice by using synthetic training data to simulate various customer needs and market conditions.
Boost Model Robustness with Synthetic Data
- Trading Algorithms
Enhance the robustness of trading algorithms by using synthetic data to simulate different market conditions and outliers.
- Credit Scoring
Improve credit scoring models by introducing synthetic data that replicates various customer profiles and credit histories, ensuring models are resilient and unbiased.
The data bottleneck
- Limited diverse datasets
Predictive financial data analytics and modeling require vast amounts of data to train models, and even more data is required for edge-cases and anomalies like fraud or black swan events. Access to safe, diverse datasets at meaningful scale remains a bottleneck across the banking and finance industries.
- Sensitive, protected financial data
With ever-evolving data privacy regulations across the world, such as FINRA and the SEC, data compliance remains a top priority for banks and financial institutions globally. Protecting customer data and eliminating sensitive data exposure is an ongoing challenge.
- Stalled Innovation
The emergence of new technologies like generative AI require businesses to innovate or fall behind. Without access to safe training data, financial institutions cannot experiment and drive new technologies forward.
High-quality, safe financial data unlocked
Synthetic financial data allows institutions to meet the nuanced needs of modern banking and finance by providing private, safe data at scale. With Gretel you don’t have to compromise between data utility and data privacy.
Our synthetic data platform provides all the features you need to unlock the value of your data while maintaining high privacy and quality standards. Whether it’s preventing data leaks, sharing data across a vast organization, or reducing data compliance overhead, Gretel brings best-in-class privacy to your business.
Why synthetic data generation?
- Quality financial data at scale
Multiple generative AI models built for the enterprise, so you can rest assured that your data mirrors the statistical properties of real-world data without compromising on privacy.
- Meet compliance standards
Align your data privacy strategies with the ever-evolving regulatory environment and ensure stringent compliance while mitigating legal and reputational risks.
- Enable AI/ML innovation
Harness the power of the latest technologies, like generative AI, to drive innovation in services, customer interactions, and operational efficiencies without sensitive data exposure.