Using Federated Learning to Bridge Data Silos in Financial Services

Unlocking the full potential of artificial intelligence (AI) in financial services is often hindered by the inability to ensure data privacy during machine learning (ML). For instance, traditional ML methods assume all data can be moved to a central repository. This is an unrealistic assumption when dealing with data sovereignty and security considerations or sensitive data like personally identifiable information. More practically, it ignores data egress challenges and the considerable cost of creating large pooled datasets. Massive internal datasets that would be valuable for training ML models remain unused.