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The Value of Synthetic Data in Financial Services
Data anonymization is key to applying the best data-driven strategies in the banking industry. The correct implementation of this strategy based on synthetic data will allow extracting all the knowledge and insights from customer financial data.
Synthetic Data use cases in Financial Industry
The banking industry faces a wide variety of challenges when it comes to anonymization and data privacy. Thanks to synthetic data, banks can tackle data challenges more effectively and unlock the full potential of their data assets.
Why do traditional data anonymization methods no longer work?
The techniques that have always been used to anonymize data do not have the guarantee of ensuring the total anonymity of the data. These are techniques that have flaws when it comes to ensuring data privacy and utility.
Anonymization & Data Sharing
Throughout this article, we will work with a Kaggle dataset, making a brief demonstration of how synthetic data is generated and how it preserves the statistical properties of the original data.
The benefits of Synthetic Data
The applications and benefits that synthetic data can bring in the development of data-centric strategies for companies are huge and growing in relevance for data science teams.
What is Synthetic Data and Why is it so important?
Without any doubt, synthetic data is one of the key technologies in the present and future of data science and artificial intelligence, but what is synthetic data? How do they work? And why is it key for data-driven companies?