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.
In the last decade, we’ve increasingly focused on model-centric Artificial Intelligence, building ever more flexible machine learning models. However, a new paradigm shift – Data-Centric AI – is currently revolutionizing the industry, as...
Synthetic data is a cornerstone of Data Centric-AI, an approach that focuses primarily on data quality rather than models. For the past few years, synthetic data gained attention because of a wide range of applications such as data...
In the rapidly evolving Artificial Intelligence landscape, data quality is the lifeblood that fuels the development of accurate and efficient models. However, accessing and acquiring high-quality, diverse, and labeled data can be quite a...
