Extract superior insights from your data

Learn how synthetic data enables you to develop and train high performing machine models that extract superior insights from your data.

In financial services, Artificial Intelligence (AI) and Machine Learning (ML) are the new normal.

That’s not an exaggeration:

“The majority of financial services (54%) companies have already adopted some form of AI/ML And it’s a trend that’s set to increase, with 86% of financial services executives saying that they plan to increase their AI-related investments by 2025.”


The revenue engine is a whole system. It encompasses a diverse set of integrated components, each doing its part to advance the system’s purpose. The engine is not just comprised of marketing and sales— it includes product, accounting, and the underlying technology and data infrastructure required to keep everything flowing.” 

Tom Mohr, Founder, CEOQuest 

In the oil and gas industry, there’s a wasteful practice called flaring. At extraction sites, the first thing to come out of the ground is associated gas. It’s fuel, but it’s volatile; if it escapes uncontrolled, it could do a lot of damage. Ideally, oil companies would capture it and sell it, but many lack the technology to do that safely. Instead… they burn it. 

Many an ambitious financial institution has, at some point, had to put a potentially game-changing AI project on ice. Perhaps you didn’t have the data you needed, or you couldn’t use the data you have. Perhaps your resources were spread too thin, or there was a mismatch of technical expertise between the business and data science departments. Perhaps the cycle of developing, testing, deploying, monitoring, reviewing, tweaking, retesting, and re-deploying your machine learning (ML) models into production was just too fraught with friction to make it work.

 Whatever the reason, a brilliant concept slipped just out of reach

“Predictions have an expiry date. Action is needed before predictions expire.”

Shitalkumar R Sukhdeve

Data drives AI – but only if you can actually use your data for AI! 

Many organizations that have painstakingly accumulated mountains of insight-rich customer data are grappling with the frustration of not being able to use that data for their machine learning projects for fear that they’ll fall on the wrong side of GDPR and other regulations.

“Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we’ll augment our intelligence.” 

—Ginni Rometty, former CEO of IBM

When you think of synthetic data, what benefits spring to mind? Do you see this as predominantly a fix for data privacy issues, allowing financial institutions to experiment with ambitious new ML models without falling foul of privacy regulations?

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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