Federated learning and analytics for fraud prevention

Enable your banking and insurance customers to build better fraud risk models on your platform, with machine learning and analytics on distributed data.

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Financial Data

Single party financial data misses the full picture.

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.

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.
Integrate.ai

Make more financial data accessible for fraud risk modeling.

With integrate.ai's production-ready, API-based platform, stand up federated machine learning and analytics across data on your platform in less than a week.
Your customers' data never moves off their servers.
Integrate seamlessly and quickly using our SDK.
Deploy easily on integrate.ai secure architecture.
Protect sensitive financial information with differential privacy.
Save engineering time with managed infrastructure.
Empower data scientists and analysts with tools for their entire workflow.
Use Cases

Accelerate your product roadmap for fraud prevention

Banks: Catch fraudulent transactions early with more accurate fraud risk models.
Insurance companies: Manage total claims costs by identifying more fraudulent claims.
Enterprise fraud management software: Optimize fraud risk models with access to more customer data.
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