Results from our customers
In the customer's words
A CRIF technology leader on how they scouted 20 vendors and chose Anonos for the control and parameterization it gave them over data security and privacy (under 1 minute).
Read the CRIF × Anonos announcement →
Show transcript
We scouted 20 different technology providers, developed internal frameworks to analyze them, to test for security of the data. And by applying these frameworks, we identified a partner, Anonos.
In our case, that was matching our use case, but that also provided us with the tools to fill in control of the process, so that we had the ability to parameterize, to adjust parameters to fine tune data security, data privacy, and data properties by ourselves.
Raiffeisen Bank International · with Hitachi
Raiffeisen Bank International, with Hitachi, on putting Anonos to work for regulated, cross-border data.
Speaking at Hitachi NEXT, Raiffeisen Landesbank shared why they partnered with Anonos and Hitachi Vantara on a project to transform data into insights — all while complying with privacy regulations.
For highly regulated industries, balancing data privacy with unlocking new insights that could boost competitive advantage is a major challenge. As more regulations to protect consumers come into play, compliance is a top priority for the bank.
With the rise of artificial intelligence and machine learning, anonymizing or masking data no longer provides sufficient protection. But the concept of the variant twin — or pseudonymizing personal data — could be a game changer.
Manuel Schwarzinger, Head of IT Digitalization and Information Management, Raiffeisen Landesbank: "When we started the project, we wanted to understand if it would be possible to pseudonymize parts of our transactional and customer data or to share it, for example, with a retailer."
The first phase of the project involved optimizing data storage, followed by a pseudonymizing program that would ensure customer details remained secure but usable. The bank, which encourages a culture of innovation, created skilled teams from several disciplines and stood up an innovation lab to support the project.
"We are reinventing our business model," reveals Schwarzinger. "We want to build stronger relationships with our customers so we can make their lives better — and that takes relevance, personalization and two-way communication."
Delivering more personalized financial advice with pseudonymization
"We're trying to take a very personalized and segmented approach, which could even include psychographic data, such as what kind of person are you? What kind of things do you buy?" explains Schwarzinger — with the goal of helping customers and providing better services.
Eventually the bank wants to take this even further, providing recommendations to customers that might help them achieve their goals. For example, it could use machine learning to identify how a customer could cut back on certain expenditures to enable them to save for a mortgage.
Relationships with retailers and other partners will be key to enabling this end-to-end approach to financial advice. For this strategy to be a success, the bank will need to ensure consumer trust is not compromised. In a digital age, customers expect banks to look after their money and their data.
Adapted from remarks at Hitachi NEXT.