Across financial services, data (opens in new tab) is recognized as an organization’s most valuable asset. From this data comes knowledge and new insights that can be used to improve every function of a business. However, privacy (opens in new tab) fears and compliance headaches turn valuable data into a liability for banks and financial institutions.
Traditional financial institutions playing by the book are being left behind in innovation, rapidly leaking market share to disruptive fintech players and more agile competitors. As the financial services industry becomes increasingly digital, large amounts of diverse datasets are needed to fulfil the demands of running transformation programs.
It’s true that in the financial sector you can’t afford to play fast and loose with sensitive data, but the good news is that there is a way to keep up with the pace of digital transformation (opens in new tab), at a time when market and economic trends are dynamic and unpredictable.
Enter synthetic data. This is not ‘real’ data created naturally through real-world events. It is ‘artificial’ data that maintains the same statistical properties as ‘real’ data, generated using algorithms. Whether the aim is to make data available across an organization or accessible to third-party partners it drives speed to innovation within financial services.
There’s been a huge spotlight on generative AI this year, and synthetic data sits under this innovative umbrella. Excitingly, the FCA is testing the potential role of synthetic data in the way financial services test, design, develop and regulate. But it’s time for the sector to open its eyes to the potential of synthetic data and how it can fuel growth, overcome long standing obstacles and red tape, and totally rejuvenate the way financial services institutions meet and exceed the ever-evolving requirements of customers (opens in new tab) and regulators.
Financial institutions can wave goodbye to GDPR
Gartner has revealed that synthetic data will enable organizations to avoid 70% of privacy sanctions. Under GDPR, using and sharing personal data – whether it is customer information, transaction records, account payments, or trading data – between internal departments or external partners, is totally limited. As a consequence, the possibilities of getting relevant insights and optimizing the potential of data decreases significantly.
Some legacy privacy enhancing technologies (PETs), such as masking and anonymization have been used in the past to try to skirt around this, but these processes reduce the utility of the data and there is a high risk of the data being reverse engineered back to a real person, resulting in significant risk of private information exposure.
Synthetic data combined with differential privacy, another privacy enhancing technology, is classed as sufficiently anonymous and 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 used and shared for collaboration (opens in new tab) on some of the major industry challenges.
With privacy compliance and information security regulations no longer an issue, and with intensified privacy and security legislation around the world, the banking sector can use the new artificially generated data to take their Open Data and data monetization strategies even further, creating new revenue streams and remaining ahead of competitors.
Harry Keen is the CEO and Co-founder at Hazy.
Supercharge anti-money laundering
Banks and financial institutions often struggle with accurately predicting risk, fraud and money laundering. As these events are small and often rare, it can be difficult to build and train accurate models based on real-world data. Synthetic data can aid the process of pattern recognition in money laundering activities by duplicating correctly identified fraud patterns and using it to train prediction models.
Firms can use synthetic data to model uncommon scenarios and potential accounts, transactions, payments, withdrawals or purchases can be better identified by the AI algorithm through a larger amount of synthetic training data. Due to the anonymity of the data, datasets from different banks can be transmitted to institutions and governments for them to examine as a merged dataset. Patterns of criminal activity can still be seen in the synthetic data without compromising data protection.
This enables inter-organisational processes in combating fraud activities while maintaining data protection. Especially in money laundering detection, very large anonymous data sets will be of enormous importance to avoid statistical false-positive reports.
Generating insights like never before
Banks and financial institutions depend on accurate data to make critical business decisions. Silos and compliance barriers often prevent them from accessing the data they need to gain valuable insights, resulting in missed opportunities and revenue loss.
Synthetic data can help financial institutions stay ahead of their competitors by being effective and securely leveraging their data assets, extracting additional value from them. Business leaders, engineers and data scientists, to mention a few, will be able to use synthetic data with complete confidence for a wide variety of purposes, from training machine learning algorithms to improving deep learning models, always knowing that they are working with high quality data very similar to the real data. But the most important takeaway is that they are never putting the customer’s privacy at risk and are complying with the many data protection regulations, protecting the business from fines or brand reputation damage.
The datasets can be tailored to include characteristics that are not present in traditional datasets, which allows financial services to gain significantly more insights in a way that could not be possible with traditional data. This provides an enhanced source of insight to better inform decisions around financial services business strategies, operations, and more.
Synthetic data generation allows financial services to think, for example, about the full lifecycle of a customer’s journey that opens an account and asks for a loan. It’s not simply examining the data to see what people do, but analyzing their interaction with the firm and essentially simulating the entire process.
Financial institutions worldwide are racing to keep up with the pace of digital transformation, at a time when market and economic trends are dynamic and unpredictable. But the nature of their data could leave a company caught mishandling data, resulting in extensive fines. In order to soar in today’s competitive landscape, financial services need their data to work harder and go further than ever before, and synthetic data will allow just that.
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