Upgrade Your Fintech Game with Cutting-Edge Synthetic Data Technology

Innovate faster, more securely, and more cost-effectively than would be possible with real customer data alone.
sandbox for regulators

What is it and how I could benefit from it?

01

Cost savings

Generating synthetic data is often less expensive than acquiring and storing real data. By using synthetic data, you can save money on data acquisition and storage costs.

02

Protecting sensitive data

Synthetic data can be used as a substitute for real data. This can help you protect the privacy and security of individuals while still being able to develop and test software, applications, and machine learning models.

03

Rapid prototyping

Synthetic data can be generated quickly and easily, allowing you to rapidly prototype and test new software, applications, and machine learning models. This can help you accelerate your development cycle and get your products to market faster.

04

Improved accuracy

Synthetic data can be used to create more diverse and representative datasets. This can help you improve the accuracy and effectiveness of your machine learning models, resulting in better predictions and outcomes.

Use cases

Finance

The Digital Twin data model provides individual-level data, which contains information across the level, such as education, number of family members, industry, and household.
When building loan default scenarios your models can be built to forecast defaulters in advance, with teams reporting 70%+ accuracy.

ESG

Our Digital Twin shows the environmental impacts of individuals and companies alike. For ESG solutions such as sustainable investing, environmental insights, and ESG-driven credit scores, the Digital Twin is here to help.

Test Data

We understand that it is often difficult to source sufficient data to properly build and test your applications. For large quantities of test data, the Digital Twin is perfect as it provides realistic data to streamline your stress tests.
NayaOne Usecases

What are the main features?

Synthetic data is not derived from actual observations or measurements of individuals, but rather generated based on certain assumptions and rules. As a result, synthetic data is anonymous and does not contain any personal identifiable information (PII) about individuals.
Generating synthetic data is often less expensive than acquiring and storing real data. This can help organizations save money on data acquisition and storage costs.
Synthetic data can be used multiple times for different purposes, such as testing and training machine learning models, without compromising the privacy or security of individuals.

Our Synthetic data is always extending, adding new data points and correlations​

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