Data is the foundation of every fintech innovation. The better and more plentiful the data, the better the outcomes. But real data comes with challenges. It can be hard to gather, expensive, and often restricted by privacy rules. Imagine if you could create data that behaves just like the real thing but without those issues. That is what synthetic data offers. In fact, a 2023 Gartner survey found that 67% of organisations developing AI applications now use synthetic data for training their models, a number projected to reach 80% by 2025.
Synthetic data is quickly becoming a powerful solution for fintech firms wanting to push the limits of what’s possible with data. Let’s explore what synthetic data is, how it’s created, and why it’s transforming the fintech landscape.
What is a synthetic dataset, and how are they generated?
They are artificially created sets of data designed to resemble the patterns and characteristics of real data. Think of synthetic data as a digital replica that mirrors the important features of real information without containing any actual personal or sensitive details.
Creating these datasets is more than simply making random numbers. There are several advanced ways to generate synthetic data. Early methods involved statistical simulation. This means building mathematical models of data distributions and then producing data points based on those models. For example, you might simulate the behaviour of customers applying for loans using probabilities.
More recently, artificial intelligence has introduced powerful new techniques. One popular method uses generative adversarial networks, often called GANs. GANs involve two neural networks working against each other. One creates synthetic data while the other tries to tell if it is real or fake. Through this back-and-forth, the synthetic data gradually becomes more realistic. Another method uses variational autoencoders, which compress data into a simplified form and then generate new similar examples.
The main challenge is to keep the statistical properties and relationships from the original data intact. If the synthetic data does not closely match the real data, models built using it may not work well. That is why generating synthetic data usually involves careful checks to confirm the data is accurate and representative.
Why are synthetic datasets becoming essential in modern fintech workflows?
Synthetic data answers many problems that fintech developers and institutions regularly face. One of the biggest benefits is protecting privacy. Laws such as GDPR and CCPA have made it difficult to access or share personal financial data without strict controls. These datasets provide a way to use information safely by giving you data that looks like real data but does not reveal anything about actual individuals.
Another advantage is overcoming the lack of enough quality data. Sometimes collecting real data is expensive, slow, or simply not possible. Take fraud detection, for example. Real fraud cases are relatively rare, making it hard to train machine learning systems. A synthetic dataset can simulate thousands of plausible fraudulent transactions, enriching the training process without needing real fraud examples.
Synthetic data also helps fix problems caused by bias or imbalance in datasets. Real financial data can sometimes favour certain demographics or outcomes, which can cause unfair or inaccurate AI models. By designing a synthetic dataset carefully, it is possible to balance the data and train stronger, fairer models.
Furthermore, synthetic data supports reproducibility and testing. Fintech startups can test new algorithms under a variety of conditions without risking real customer data. These datasets also make sandbox environments safer and more versatile for testing compliance, stress scenarios, or regulatory responses.
How are synthetic datasets applied in fintech?
Synthetic data is already playing a critical role across financial services.
In fraud detection, synthetic datasets help banks and payment processors build better models by creating examples of fraudulent transactions across different regions, payment types, and customer profiles.
In credit scoring and risk assessment, fintech firms can model synthetic borrowers with different financial behaviours to evaluate how scoring systems perform under varied economic conditions. This allows for more inclusive and resilient financial products.
For algorithmic trading, synthetic market data can simulate unusual but plausible trading conditions, helping to stress test models and develop more robust strategies without needing rare or historic events.
In personal finance applications, a synthetic dataset makes it easier to develop and train chatbots, budgeting tools, or savings assistants without compromising real customer data. Developers can create realistic interaction scenarios to improve user experience and product performance.
In regtech and compliance testing, synthetic transaction logs can be used to validate rule-based systems and ensure audit trails work as intended, helping institutions stay ahead of evolving regulations.
What technical challenges and limitations affect synthetic dataset quality?
Synthetic data is powerful but not without issues. One key problem is making sure the synthetic data truly represents the real data it aims to copy. If the data is too different, models trained on it will not perform well in actual situations.
Distributional shift happens when the statistical patterns in synthetic data do not match those in real data. This can cause biased or inaccurate results. Techniques such as statistical testing and adversarial validation are used to detect and reduce these gaps.
There is also the risk of overfitting. This happens when models learn patterns unique to synthetic data but fail to generalise to real data. Combining synthetic and real data during training can help avoid this problem.
Another point to consider is how easy it is to tell synthetic data apart from real data. If synthetic data is too obviously artificial, it may reduce trust or cause issues in certain financial applications. The best generation methods work hard to make synthetic data realistic while maintaining enough variety.
Generating high-quality synthetic datasets can also be resource intensive. Training complex models like GANs requires computing power and expertise, which may not be accessible to all fintech companies.
Despite these challenges, improvements continue to be made. Hybrid strategies using both synthetic and real data are common in practice, providing a good balance for many financial systems.
How synthetic datasets are shaping the future of fintech
Synthetic datasets have evolved from a niche idea into a foundational tool for fintech innovation. They offer a way to create abundant, privacy-safe, and customisable data that opens new possibilities for product development, risk analysis, and customer experience.
As synthetic data becomes more realistic and accessible, fintech companies will be better equipped to test, train, and scale new solutions quickly and ethically. Whether it’s fighting fraud, serving underbanked communities, or exploring new investment strategies, a synthetic dataset provides a flexible, low-risk path forward.
Data is often called the new oil. In fintech, a synthetic dataset provides a cleaner, safer, and more sustainable way to fuel the next wave of financial innovation.
