Democratized access for large transactional datasets

Cover Photo by Avery Evans on Unsplash

The value of data and the adoption of data-driven strategies have proven valuable for many organizations, particularly the financial services sector, with cases ranging from fraud detection to improved credit scoring. Nevertheless, privacy regulations make accessing and sharing data within and across organizations harder.

Much of the information recorded by data-oriented processes in Financial institutions is transactional. This data type refers to the information recorded from customers' and institutions' interactions.

A privacy-compliant data-sharing of this type of record is vital for developing new data-driven strategies. Synthetic data, an emerging privacy-enhancing technology (PET), have the potential to unlock the total value and granularity of the data while remaining privacy compliant as no one-to-one match is kept between the original and generated data.

Synthetic transactions' data generated by YData Fabric keeps the general statistics of the dataset and the integrity between the entities involved in the transactions.

Download this case study to learn more about:

• Synthetic transactional data benefits
• How to generate synthetic transactional datasets
• The quality & privacy guarantees of the generated synthetic data