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.
Synthetic data, artificially generated data that mimics real-world data, is a technology that has undergone significant transformation in recent years. Since the dawn of data-driven synthetic data generation with generative models,...
Photo by Roman Kraft on Unsplash As machine learning continues to evolve, the use of Large Language Models (LLMs) has become increasingly prevalent, particularly in complex tasks requiring deep understanding and generation of human-like...
Photo by Isaac Smith on Unsplash Synthetic data bootstrap In the dynamic landscape of organizations high-quality data is a requirement for the development of many solutions - from software testing and validation all the way to Artificial...
