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Data: Fake will be the new real in financial services in 2023


By Harry Keen, CEO & Co-Founder, Hazy


Data drives valuable innovation in an increasingly digital financial services sector. For many companies, 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.

However, as many of these institutions are still sweating old IT assets, they are laggards when it comes to technology, innovation and the speed at which they can digitally transform. And with increased regulatory pressure, cybersecurity threats and increasing rates of customer concern, they cannot risk not driving the valuable innovation needed to improve automation, decision-making and data security

It’s time to turn to synthetic data. This is ‘artificial’ data generated digitally using algorithms that maintains the same statistical properties of ‘real’ data. Whether the aim is to make data available across an organisation or accessible to third-party partners, it drives speed to innovation within financial services.         

Synthetic data already plays an important role in the future of banking, and I believe this will only accelerate in 2023. Access to meaningful customer and transaction data is getting more restricted. Growing cybersecurity concerns and increasing legislative pressure are only some of the reasons. Legacy systems represent a mounting challenge to data architectures and customers demand digital personalization and privacy simultaneously. Synthetic data can solve all these issues and more in 2023.  


Supercharging data privacy 


According to Gartner, synthetic data will enable organisations to avoid 70% of privacy violation sanctions. Financial data, such as consumer transaction records, account payments, or trading data, is sensitive personal data subject to data protection obligations, and is often commercially sensitive. 

Financial services data is (quite rightly) highly regulated, because of the sensitive nature and volume of personal information within data sets. But this regulation does make sharing of data difficult between financial institutions and those trying to innovate and solve the challenges of fraud and financial crime, for example.

Synthetic data is not tied to a real individual and does not contain any real personal information. As no real individuals can be identified from the synthetic data, data protection obligations, such as GDPR, do not apply. This will undoubtedly be top of mind in 2023 for business leaders, with the fifth anniversary of GDPR in May. This means data can be more easily shared for collaboration on some of the major industry challenges.

Since privacy compliance and information security regulations will not be an issue, the synthetic data generated can then be used to create new revenue streams, allowing financial institutions to take their Open Data and data monetization strategy even further in 2023.


Effortless cloud migration

Banks and financial institutions often miss out on innovative technologies, such as machine learning and cloud AI, because uploading sensitive data to cloud platforms is just out of the question for policy reasons. A synthetic version of a sensitive dataset can stand in here.

Pseudo-anonymized data created by traditional processes, such as anonymization, can still lead to re-identification or redacted data that loses most of its utility. Instead, with synthetic data generation, the dataset is new and holds no ties to the original. If used, in 2023, financial services can train on their real datasets on-premise – even behind the walls of separate departmental silos. The artificial data can then be released into the cloud. And as the synthetic data contained no real information, security teams will be able to sign off the cloud usage of this data immediately.

Any financial organisation that is looking to be more competitive and benefit from the flexibility of the cloud whilst avoiding lengthy projects and the risk of using real customer data style=”font-weight: 400;”> will need to use      synthetic data in cloud migration projects.  Something that financial organisations can use to great effect in 2023 to survive in today’s trying times.

The commercial impact of generative AI

Generative AI underwent a huge step change in the latter half of 2022. Teams from OpenAI through to StabilityAI have been creating models that can replicate hyper-realistic text and images from seemingly thin air with little to no verbal prompts. In fact, the realism of response you can get from these models is almost hard to believe, and like nothing we’ve seen prior to this year.

This development is undoubtedly going to impact business and society. But how? It’s actually not clear, but what we do know is that these teams are making these models available for anyone to play with for free right now. This will create the perfect test ecosystem for developers, hackers and anyone who wants to try their ideas.

I am certain that in 2023 we will start to see businesses forming around these tools. We have already seen examples of text or formula auto completion tools being embedded into Microsoft Office software that could revolutionize productivity and speed up learning curves of users. And those types of revolutionary tools can impact more businesses than just those in financial services

Although exciting, there are certainly concerns and legal challenges that still need to be overcome before this technology can be commercialized. Who owns the output of one of a code auto completion model if it was trained on data under different licences? Who owns the copyright to images generated from a model trained stock images under different licences?

We are still in the early adopter phase of this exciting technology. And despite the challenges discussed in this piece, its potential should not be underestimated, and I cannot wait to see and be a part of all the developments and uses of synthetic data in 2023.


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