Synthetic Data for Fraud Detection and Anomaly Models
Fraud and anomaly models live in a hard spot. Real transaction logs are noisy and biased. Fraud events are rare, often 0.1% or less. Models see mostly benign activity and learn to ignore the few bad cases. When fraud tactics shift, models become stale. Teams also hit walls with privacy laws and data sharing limits. They cannot simply take a large labeled fraud set and move it between banks, insurers, or payment processors. The result is under-trained models, high false positives, and missed attacks. Synthetic data can fix several of these issues at once.
Real data is rare and imbalanced
Fraud datasets are always imbalanced. A typical credit card transaction log might have 99.9% legitimate transactions and 0.1% fraud. Some industries are even more extreme. In insurance claims, fraud can be less than 0.05% of all events. This imbalance makes supervised models struggle. They tend to predict the majority class and miss rare signals. Synthetic oversampling can rebalance the training set, but it must be done carefully so the synthetic fraud is realistic.
Generating realistic fraud scenarios
The hardest part is making synthetic fraud look real. Simple duplication of existing fraud cases helps, but fraudsters adapt quickly. Synthetic data generators often use generative models to create new attack patterns. For example, they can model how fraudsters vary transaction amounts, timing, and device characteristics to avoid detection. A good synthetic dataset should include variations that are not obvious copies of any single real attack. It should also include subtle deviations that mimic human behavior, such as small timing jitter or unusual but plausible merchant categories. This helps models learn robust features rather than memorizing a few attack templates.
Privacy, compliance, and sharing data
Real financial data is sensitive. Banks and fintechs must comply with regulations like GDPR, CCPA, and sector-specific rules. Sharing labeled fraud datasets across organizations is often impossible or heavily restricted. Synthetic data removes PII and sensitive details while preserving statistical properties. Teams can train models on synthetic data and still get comparable performance. Some studies show that synthetic fraud datasets can achieve near-loss performance compared to real fraud data when the generation process is well-tuned. This enables collaboration on benchmark datasets and joint model development without breaking privacy rules.
Evaluating models on unseen attacks
Models often look great on validation sets because those sets contain data similar to what the model saw during training. Real attacks are different. Synthetic data lets teams create attack scenarios that are not present in their historical data. They can simulate new fraud strategies, such as synthetic identity fraud, account takeover, or complex money-laundering flows. By testing models on these synthetic attacks, teams can assess how well their systems generalize to novel threats. This is especially important for anomaly detection, where the definition of what is abnormal changes over time.
Synthetic data helps fraud and anomaly models by balancing rare events, providing privacy-safe training sets, and enabling evaluation on unseen attack patterns.
How Coasty fits
Coasty builds computer use agents that run on real desktops and browsers. These agents generate realistic interaction data, including clicks, form entries, navigation paths, and other human-like behaviors. Teams can use Coasty to capture realistic patterns of legitimate and suspicious activity, then synthesize custom datasets for fraud and anomaly models. The service is custom and contact-led, meaning you work with the Coasty team to design a dataset that matches your data needs and regulatory constraints. No self-serve product exists. If you want to explore how synthetic interaction data can improve your fraud detection pipeline, the next step is to talk to the Coasty data team.
Fraud and anomaly models need more than rare, noisy real data. Synthetic data gives you control over event rates, attack patterns, and privacy constraints. To see how Coasty can help you build custom synthetic datasets for your use case, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .