Discover How IGLOO Transformed Cybersecurity with Synthetic Data
Cover photo by Andrea De Santis on Unsplash
Use cases for the Financial Services
Improved fraud detection
Save money by detecting more fraud events
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.
Improved underwriting
Optimize credit acceptance
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.
Data compliance
Data sharing and data privacy combined
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.
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ydata-synthetic: Models to revolutionise Synthetic Data Generation
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...
