YData for Financial services

Data-Centric AI for Financial Services

From fraud detection to credit underwriting, YData Fabric provides Financial services innovation leaders with a complete workbench for data-centric AI applications development accelerated by synthetic data and data profiling.

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.

ydata financial use case

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.

ydata financial use case
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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.

ydata financial use case

Join AI innovation with the right data

Become the best in class by delivering faster and better AI solutions with improved data.

How to pick the best fit data catalog for your data stack?

Dive into data management with our latest whitepaper, which presents an in-depth Gap analysis among YData Fabric, Alation, and Informatica—three solutions in the realm of data catalogs. These platforms are chaging how organizations govern,...

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How to evaluate the re-identification risk in Synthetic Data?

While allowing for meaningful data behavior, it is crucial that synthetic data safeguards individual privacy. Therefore, ensuring the efficacy of synthetic data applications also requires a strong assessment of re-identification risks.

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How is diversity preserved while ensuring privacy in synthetic data?

One of the most valuable and unique characteristics of synthetic data is that it keeps the properties and behavior of original data without a one-to-one link with the real events, thus fostering data privacy and enabling secure data...

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