An optimized dataset with more correctly labeled fraud events leads to easier training of the fraud detection models, higher accuracy, and reduced costs. Credit card and Transaction datasets are often imbalanced with only a few events of the fraudulent category. Using YData, you can generate more data samples for the category of your choice to get improved model performance.
Financial institutions use predictive AI/ML algorithms for tasks such as assigning credit limits or granting loan approvals, but it is hard to have access to the right data and the volume of high-quality data required for these tasks. YData helps to improve existing data and synthesize new records for sharing, without compromising individuals’ privacy.
Managing enormous volumes of data makes data privacy and security two of the main challenges for financial organizations. YData allows you to generate any amount of new data while complying with privacy regulations - synthetic data is artificially created and preserves the same statistical attributes.
Get started with Synthetic Data Generation with these Open Source Libraries Following the extraordinary advances of Generative AI models, Synthetic Data is becoming the standard for Machine Learning development. Especially with the rise of...
Photo by Agê Barros on Unsplash Unveiling the inner workings of how sequential data works and how Fabric can to smooth your journey in a time-series Machine Learning project Time-series data refers to a type of data that is collected and...
Photo by Roman Synkevych on Unsplash A probabilistic approach to fast synthetic data generation with ydata-synthetic To find synthetic data generation within the same sentence as Gaussian Mixture Models (GMMs) sounds odd, but it makes a...
