Unlock your sensitive data

Learn how synthetic data lets you learn from, analyze and use sensitive data - without compromising on your responsibility to treat personal records appropriately.

Are You Missing Out On Your Data’s Potential?

The job of a CIO has never been as dynamic and varied as it is right now. It’s never been as daunting, either.

Erring on the side of caution is no longer possible. If you’re barricading your data, resisting requests to release it to developers and data scientists, you’re not realizing your data’s potential. 

Today’s CIOs need effective, reliable ways to deliver on their traditional roles while at the same time rising to new challenges and expectations.

Artificial Intelligence innovation is exploding, extracting meaningful patterns and business-critical predictions from avalanches of Big Data. Machine learning gives banks, lenders, and insurers the ability to perform more effective, accurate credit scoring, improve risk assessment, finesse marketing campaigns, ensure regulatory compliance, optimize their processes and cut costs. 

This is just the beginning. The global AI fintech market is expected to be worth $22.6bn by 2025. Globally, half of all data and analytics decision-makers already have AI investments underway. The question is: why only half? Given the benefits, why haven’t all financial institutions embraced AI?

Download our ebook, Unleashing Your Data Potential: AI & ML in the Financial Sector

“Privacy is not for sale, it’s a valuable asset to protect.”

Stephane Nappo, VP & Global CISO, Groupe SEB

Synthetic data is an effective, accessible, and affordable alternative that retains the properties of source data, without the personally identifiable information.

That means you need to think very carefully about whether to go down the road of encryption, differential privacy, or a distributed systems approach. Or whether to eliminate the privacy risk completely by using artificially generated, synthetic data.

“AI is going to be extremely beneficial and already is, to the field of cybersecurity. It’s also going to be beneficial to criminals.” 

Dmitri Alperovitch, Co-Founder of Crowdstrike

There’s a war going on in AI… and I don’t mean a Terminator-style rise of the machines. 

It’s the war between privacy and progress.

AI demands data. So much data. Machine learning models and neural networks need a ton of information to train, test, and deploy. And not just any data. They need nuanced, accurate, quality data. Data with detail and context.

“We are moving slowly into an era where big data is the starting point, not the end.”

As more businesses seek to overcome barriers to adopting AI-driven, intelligent data analytics, it’s little wonder privacy-preserving technologies (PPTs) have become vital elements in the AI innovation ecosystem.

But anonymizing or disguising data has limitations. Attacks and leaks are constant risks. Maintaining high levels of protection hinders what you can do with data. Operations are restricted and projects become hard to scale.

Find out how synthetic data removes a major barrier to adopting AI. Read the full, deep-dive article in the comments below.

“Ignoring technological change in a financial system based upon technology is like a mouse starving to death because someone moved their cheese” 

Chris Skinner, financial markets commentator and author

For many companies in the finance sector, privacy fears and compliance headaches are turning valuable datasets into white elephants. They’re too risky to put to work, too precious to let go… and far too costly to leave sitting idle.

Major financial corporations and organizations from American Express to the FCA are already using synthetic data to build sophisticated fraud-detection models, to assess a customer’s level of financial vulnerability, and to make nuanced, accurate, SME lending decisions.

“If you put a key under the mat for the cops, a burglar can find it, too. Criminals are using every technology tool at their disposal… If they know there’s a key hidden somewhere, they won’t stop until they find it.”

Tim Cook, CEO of Apple

Today’s tech giants, financial institutions, and other organizations that work with sensitive data understand deeply that basic anonymization isn’t enough to protect people’s privacy. They’re also bound by a swathe of regulations that limit what they can do with real people’s data.

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