Bitfount for Financial Services

Smarter finance through federated AI

Harness the full power of siloed data without compromising on privacy or compliance. From fighting financial crime and fraud to assessing credit risk. Develop and send algorithms and AI models to the data with Federated AI and distributed data science.

Enhancing financial security and efficiency

The Future of Federated Finance

Detect and prevent financial crime

Traditional methods of detecting financial crime often rely on siloed data and manual analysis, which can be slow and error-prone.

Use Bitfount to deploy internally-developed or 3rd-party models across jurisdictional data silos to detect financial crime in real-time and respond quickly and effectively to protect both institutions and customers.

Leverage collective intelligence to reduce false-positive rates and case volumes while preserving data privacy and security.

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Fraud detection

Centralized data analysis methods can be slow and may not be able to keep pace with the constantly evolving tactics used by fraudsters.

Use federated data science and AI across jurisdictions and institutions to develop and deploy advanced anomaly-detection, clustering and other algorithms.

Federate analyses across siloed transaction data, customer behaviour data, and external data sources to spot patterns and perpetrators of fraudulent behaviour with higher accuracy, and block fraud before it happens.

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Credit risk

Fast-changing economic conditions and customer behaviours make it difficult for risk teams to keep their models up-to-date and accurate.

Use Bitfount to collaboratively analyse and train models on historical customer credit scores, payment histories, and financial behaviours.

Privacy-preserving partnerships with external data providers provide a more complete picture, drive more informed lending decisions and reduce the risk of loan defaults.

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Industry benchmarking

Industry benchmarking can improve an institution's performance, competitiveness, and reputation, but is currently hampered by issues of privacy and data availability.

Use federated data science to set up privacy-preserving benchmarking consortia. Analyze data on operational efficiency, cost structure, sales performance and more.

Built-in techniques like Secure Aggregation return aggregated benchmarks without sharing any individual participant's contribution to the total.

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Key features for financial services

Built-in privacy preservation

No need for a PhD in cryptography. Remove risk barriers to opening up sensitive data by combining any of a range of built-in privacy-enhancing technologies (PETs).

Usage-based access controls

Partners are only able to carry out the analyses you have allowed them to. Role-based access controls let you technically enforce governance and usage policies.

Audit and monitoring

Bitfount provides a real-time, granular audit trail of how partners or consortia members use your data.

Zero-trust security model

Bitfount follows the latest in network security best practices, including Zero-Trust principles. Never trust, always verify.

Simple IT integration

Bitfount's communication model only requires that data custodians and data scientists have outgoing internet connections. No need to open any ports in the firewall, dramatically simplifying IT integration.

End-to-end encryption

Bitfount itself never receives data or statistics. Any analysis results are transferred between data custodians and data scientists using end-to-end encryption.

Get started for free

Create your first federated project now or get in touch to book a demo