The use of Synthetic Data in Financial Services

“Ignoring technological change in a financial system based upon technology is like a mouse starving to death because someone moved their cheese”

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The Bottom Line:

For many companies in the finance sector, privacy fears and compliance headaches are turning valuable datasets into white elephants. They’re too risky to put to work, too precious to let go… and far too costly to leave sitting idle.

Which is a shame, because data-driven, machine learning models can be used to develop new products and revenue streams, reduce lending risk, streamline KYC processes, and predict fraud attempts with impressive accuracy. Insights and analytics from your data help streamline internal processes, predict customer churn and LTV, and design personalized marketing campaigns to boost the business.

When you consider the lucrative potential of all that data, it’s frustrating to think of it languishing in silos. But how do you share and collaborate on data projects, internally and externally, without risking data leaks and privacy breaches?