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.
As generative AI reshapes industries, the quality, diversity, and safety of the data used to train and evaluate models have never been more critical. Today, we’re thrilled to announce two major additions to YData's product portfolio that...
We are thrilled to announce that YData has been recognized as the most statistically accurate synthetic data generator in AIMultiple's 2025 benchmark. This independent evaluation assessed seven publicly available synthetic data generators...
Cover photo by Andrea De Santis on Unsplash
