Synthetic financial data: banking on it
By banking on synthetic financial data, banks can tackle the challenge highlighted by Gartner – that by 2030, 80% of legacy financial services firms will go out of business, become commodities or exist only formally but unable to compete effectively. .
“A pretty dire prophecy, but none the less realistic, with small neobanks and big tech companies eyeing their market. Survivalist banks and financial institutions need a strategy where the creation, use and sharing of synthetic financial data is a key component,” says Tobias Hann, CEO of MOSTLY AI.
Banks and financial institutions are aware of their data and innovation gap, and AI-generated synthetic data is one area they are investing in to gain a competitive advantage. Synthetic financial data is generated by AI trained on real-world data. The resulting synthetic data looks, feels and means the same as the original. It’s a perfect proxy for the original, as it contains the same insights and correlations, plus it’s completely privacy-proof.
Easy to deploy data science use cases in banking demonstrate clear value from the use of synthetic financial data, including advanced analytics, AI and machine learning; data sharing; and software testing.
Synthetic financial data for advanced analytics, AI and machine learning
“AI and machine learning open up a number of business advantages for retail banks. These include advanced analytics that improve customer acquisition by optimizing the marketing engine with hyper-personalized messages and precise next-best actions. Intelligence from the very first point of contact increases the customer’s lifetime value. Since synthetic financial data is GDPR-compliant, yet contains the intelligence of the original data, customer consent is not required to harness its power, says Hann.
Synthetic financial data also makes it possible to reduce operating costs if decision-making in procurement and service is supported with well-trained machine learning algorithms. In addition, underserved customer segments can get the credit they need by fixing embedded biases via data synthesis; and it simplifies the mass-market AI explanation, which is increasingly demanded by tech-savvy customers.
Synthetic financial data for enterprise data sharing
Open financial data is the ultimate form of data sharing. According to McKinsey, economies that embrace financial data sharing could see GDP gains of 1-5% by 2030, with benefits to consumers and financial institutions. More data means better operational performance, better AI models, more powerful analytics and improved customer-centric digital banking products.
Synthetic test data for digital banking products
One of the most common use cases for data sharing is linked to the development and testing of digital banking apps and products. Banks accumulate tons of apps, continuously develop them, adopt new systems and add new components. Manually generated test data for such complex systems is a hopeless task, and many revert to the risky use of production data for testing.
Typically, manual test data generation tools miss most of the business rules and edge cases critical to robust testing practices.
To put it simply, it is impossible to develop intelligent banking products without intelligent test data. The same applies to testing AI and machine learning models. Testing these models with synthetically simulated edge houses is extremely important to do when developing from scratch and when recalibrating models to avoid drifting.
Start your journey with synthetic financial data
Not all synthetic data generators are created equal. It is important to choose the right provider of synthetic data that can match the financial institution’s needs. If a synthetic data generator is inaccurate, the resulting synthetic data sets can lead your data science team astray. If it is too accurate, the generator overfits or learns the training data too well and may accidentally reproduce some of the original information from the training data.
Open source options are also available. However, quality control is quite low. Until a global standard for synthetic financial data is in place, it is important to be careful when choosing suppliers. Choose synthetic data companies with extensive experience in handling sensitive financial data and knowledge in successfully integrating synthetic data into existing infrastructures.
“Our team at MOSTLY AI has seen large banks and financial organizations up close. We know that synthetic financial data will be the data transformation tool that will change the financial data landscape forever, enabling the fluidity and agility needed to create competitive digital services, concludes Hann.
