Federated learning and analytics for fraud prevention

Fraud is costly, but rare. First-party data held by banks, insurers, and consumer brands are skewed, with a very small proportion of fraudulent transactions, making it difficult to detect fraud patterns with machine learning. Data sharing across institutions and borders enables more accurate fraud risk modelling.
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But with data sovereignty and privacy regulations, and concerns about leaking sensitive IP, financial institutions are unable to collaborate with their first-party data to build better fraud risk models.