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.
YData Brings State-of-the-Art Data Quality Profiling and Synthetic Data Generation to Databricks, Enhancing Data Workflows and Ensuring Safe Data Sharing
At YData, open-source solutions have always been a fundamental part of our DNA. Through ydata-synthetic, we’ve shared knowledge and empowered users to explore the potential of different generative models like TimeGAN, CTGAN, and many other...
The Importance of Security and Privacy in a Data-Driven World
