Why Synthetic Data Works Better Than Real Data for Fraud Detection
Fraud is rare. Real transactions often have zero fraud, so models see mostly benign patterns. This imbalance makes fraud detection hard.
Real fraud data is too rare to train robust models
A typical credit card dataset might have 99.8% legitimate transactions and 0.2% fraud. Training on this forces the model to focus on the majority class. It learns the shape of normal activity and treats anything unusual as noise. Fraud episodes, phishing, account takeovers, synthetic identity schemes, appear only occasionally. A model trained on real data rarely sees enough examples to generalize.
Synthetic data changes the math
Generative models can produce thousands of fraudulent examples that match the statistical properties of your real data. Anomaly detection models, which flag anything that does not fit a learned pattern, benefit from seeing many anomalies during training. They learn which features are truly unusual instead of just rare. In experiments, synthetic fraud data can improve detection accuracy by 10-25 percent compared to training only on real events.
Tradeoffs between synthetic and real data
- ●Synthetic data is cheap and can be generated at scale.
- ●It removes privacy concerns because no real individual information appears in the dataset.
- ●Generative models may create unrealistic feature combinations if they mislearn the underlying distribution.
- ●Only certain data types, like tabular or sequential transaction logs, are suitable for current GANs and diffusion models.
Synthetic data is most effective when combined with real data. Start with a small real baseline, generate synthetic fraud patterns, and evaluate how the model reacts to the new examples.
How Coasty fits
Coasty builds computer-use agents that interact with real desktops and browsers. These agents capture realistic sequences of clicks, navigation paths, and form inputs. That interaction data can be transformed into synthetic datasets for fraud detection and anomaly models. Coasty does not sell off-the-shelf packages. Its offering is a custom synthetic data service you discuss directly with the team. If you need behaviorally realistic sequences for your model, book a data call to explore what is possible.
Fraud detection needs both rare events and realistic context. Synthetic data can fill the gap. To see how Coasty can generate synthetic behavior sequences for your use case, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.