Artificial intelligence promises to revolutionize finance, but financial institutions face steep barriers around data privacy and risk management. This is where synthetic data comes in.
Synthetic data is artificially generated to mimic real data, without exposing sensitive personal information. It preserves key statistical properties and patterns found in the original data.
In this blog post, we’ll explore the top 4 applications where synthetic financial data can drive innovation and value in 2024 and beyond.
1. Enabling Data Sharing and Collaboration
Banks house extremely sensitive customer data like bank account transactions, credit card purchases, and more. Regulations like GDPR and CCPA place strict limits on how this data can be shared, even within companies.
For example, GDPR requires anonymizing any personal data shared with third parties. But traditional anonymization techniques like data masking can still be vulnerable to re-identification attacks. A 2000 study found 87% of Americans can be identified by linking gender, birth date and zip code.
This makes it challenging for banks to collaborate with fintech partners, third-party developers, or even internal teams. Valuable use cases in analytics, AI development and new product testing hit roadblocks.
Synthetic data provides a privacy-safe workaround. Banks can use AI to generate synthetic versions of their datasets, containing artificial data points with the same patterns and distributions as the real data.
According to a 2021 MIT study, 87% of financial institutions are testing or deploying synthetic data to enable controlled data sharing. It preserves analytics value while protecting sensitive personal information.
JP Morgan leveraged synthetic data at scale, generating a dataset of 100 million synthetic credit card transactions. They shared it broadly for an AI modeling competition on Kaggle. Participants built fraud detection models without accessing any real customer data.
2. Detecting Fraud and Rare Events
Detecting fraudulent transactions is a killer application for AI in finance. However, fraud is inherently a rare event, making up less than 0.1% of activities. Standard AI/ML models struggle to detect rare events based on imbalanced training data.
Banks can leverage synthetic data generation to create balanced datasets containing more examples of fraudulent behaviors. This expanded training data powers more accurate fraud detection with machine learning.
For example, a European bank created a synthetic dataset boosting fraud cases from 0.2% to 15.8%. They trained a gradient boosting model on this data, improving fraud detection by 60%.
McKinsey estimates that this use of synthetic data could reduce banks’ credit card fraud losses by up to 30%.
3. Enabling Simulations and Strategy Testing
Banks rely on data to backtest strategies and simulate scenarios. But relevant historical data is not available for events like market crashes, new product launches or operational failures.
Here again, synthetic data can fill in the gaps. Banks can leverage AI to generate synthetic datasets modeling extreme events, new markets and more. This powers dynamic simulations to stress-test systems and fine-tune business strategies.
One global bank recently simulated a two-year market shock using synthetic data. By modeling client behaviors and balance sheet changes, they assessed the impact on liquidity ratios and valuations. The project yielded a $2.5 billion capital release.
My team has partnered with numerous financial institutions to develop synthetic scenarios for simulation testing and contingency planning. Our in-house finance experts ensure the synthetic data accurately reflects real-world conditions.
4. Improving Deep Learning Model Accuracy
Deep learning thrives on massive datasets, with more data leading to higher accuracy. Synthetic data generation enables banks to exponentially increase training data volume and variety to boost model performance.
A large US bank recently saw a 2.5% accuracy improvement on loan default prediction by augmenting just 20% synthetic data to its training dataset. The synthetic data increased variability and expanded the feature space.
Advanced deep learning techniques like GANs and VAEs can generate highly realistic synthetic data to minimize statistical deviations from the real data. Our data scientists follow best practices to tune GAN architectures and loss functions.
Synthetic financial data opens the door to game-changing AI applications while protecting data privacy and enabling collaboration. Leading banks already use synthetic data for fraud detection, simulations, deep learning and more.
As a finance data expert, I expect synthetic data adoption to accelerate given the competitive advantage it creates. Reach out if you want to explore synthetic data opportunities tailored to your use case.