How Federated Analytics Can Help Banks to Share Data Securely

Intel is working with software firm Data Republic on a joint federated analytics project.

Key Takeaways

  • The challenge for FSI businesses is to securely gain insights from the ever-increasing amount of data that is stored in different locations.

  • Using federated analytics, businesses can carry out analytics on data without having to move it, reducing the risk of it being hacked or leaked, and the cost of moving huge quantities of data.

  • Intel is partnering with software firm Data Republic to introduce the technology at scale.



The amount of data generated by businesses across all sectors continues to grow, and one of the key concerns is keeping sensitive data safe. This is especially true in the Financial Services Industry (FSI), where organizations hold a huge amount of highly sensitive personal and transactional data. As the industry has evolved, growing amounts of data are stored in various places in different formats, rather than being curated in one central location. The arrival of PSD2 and Open Banking regulations and the growth of mobile banking has further increased this deluge of data.

The challenge is how FSI businesses can gain insights from data that is stored in different locations. After the discovery phase of a data collaboration project, it doesn’t always make sense to keep moving the data to the algorithm. This increases the risk of data leakage and involves costs to move large quantities of sensitive data. Federated analytics flips the process by taking the algorithm to the data. This process can operationalize multi-party analytics to run at scale without having to move the data.

Federated analytics enables businesses to gain insights from disparate data sources, without the data having to be moved to one central environment by bringing the algorithm to the data. The security this offers makes its particularly well suited to businesses in the financial sector who want to make sure that data stays at rest in local environments and only the insight moves across distributed architecture. As well as maintaining the security of data, there's a variety of possible use cases for federated analytics within the financial world. The pooling of data insights across banks is especially useful when trying to verify sensitive insights such as those related to income verification or fraud. For example, a matching query could be run across different banks in order to understand if an account holder was working fraudulently. All of the banks would benefit, without having to reveal their raw data to competitors.

That's why Intel is working with ecosystem partners to develop a federated analytics solution that helps FSI businesses to get the best from vast amounts of data available. "Data is defining the future — not just for financial services, but for the world," said Mike Blalock, General Manager, Financial Services Industry at Intel. "If we can unlock the value of the data and enable it to be shared more broadly while maintaining privacy and security, it increases its value and impact. Our goal is to develop a new approach to analyzing data that helps to ensure privacy and protect IP, while also supporting collaboration across distributed datasets that may contain proprietary or protected information."

Intel is partnering with software firm Data Republic to develop a combined solution that allows companies to enact a secure discovery phase between their datasets before applying federated analytics technology. Headquartered in Sydney, Australia, Data Republic provides software that helps data teams at major enterprises govern the way that they're sharing data, reducing the risk of data exchange projects. As well as securely maximizing the value of data, Data Republic's Senate data-sharing platform can also reduce the time taken to gain business insights.

"By bringing the algorithm to the data and governing the outputs, Data Republic, and Intel are changing the analytics status quo for FSI providers," said Frank McKenna, chief product officer at Data Republic. "Data Republic offers the governance and discovery layer for managing multi-party data collaboration projects, and Intel is providing the framework for the federated analytics solution".

“It's about working together to allow joint, multi-party computation of sensitive data stored in different secure locations, while ensuring data privacy and governance are upheld."

Data Republic’s Senate Platform is built around a data sharing governance framework, and enforces governance with features including audit logs and user permissions. And because Data Republic's governance and discovery framework is already in place — this acts an as an on-ramp to the Intel-powered future roadmap of federated analytics. The aim of the partnership between Intel and Data Republic is to work out how to bring this technology to market and deliver it at scale.

Data Republic already works with a number of major financial firms including some of Australia's largest banks — ANZ (Australia and New Zealand Banking Group) and Westpac, and is also in active discussions with other banks globally. Meanwhile, Intel recently carried out a successful trial with Singapore's United Overseas Bank (UOB), using its federated analytics platform to boost cross-border anti-money laundering (AML) efforts.

"We're excited to work with Data Republic and other end users to drive this initiative forward," said Blalock. "This is a new paradigm for FSI — it reduces barriers and increases data access, which allows banks and other financial institutions to get more value out of their data. It really sets the stage for this collaborative intelligence vision of the future that we have around federated analytics."

Federated analytics will become increasingly important to financial organizations in the new, data-centric world, and balancing privacy with innovation is the key. While the technology is still in its early days, FSI businesses that get involved now can reap the benefits in the near future.

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