Why Synthetic Data Helps Build Better Fraud Detection Models
Financial fraud moves fast and hides in the noise. Banks and fintechs train models to spot credit card skimming, account takeovers, and synthetic identity fraud. The problem? Real fraud examples are rare. Banks also can't easily share labeled cases for training. That leaves models with mostly clean, benign transactions and a few scattered fraud labels. The data imbalance hurts performance.
The data imbalance problem in fraud
Fraud is a minority class. On a typical merchant transaction dataset, fraud might be 0.1 percent or less. When models see mostly non-fraud examples, they learn a simple rule: label everything as safe. Even a good model might only catch 30-50 percent of fraud events before it hits a performance ceiling. Synthetic data lets you generate thousands of realistic fraud cases to balance the training set and expose the model to rare behaviors.
Synthetic data adds realistic edge cases
Real fraud is messy. Attackers vary amounts, locations, device fingerprints, and behavioral patterns. Synthetic data can recreate complex attack scenarios that are hard to engineer manually. For example, you can generate sequences where a user logs in from a new device, changes shipping addresses multiple times, and ramps up transaction velocity over days. These patterns often precede account takeovers. Synthetic datasets let you label these sequences as fraud, giving models practice with multi-step attack signatures.
Avoid exposing sensitive data
Financial teams worry about PCI DSS and data privacy. They cannot ship raw transaction logs or user identifiers to third parties. Synthetic data removes those risks. You can generate labeled fraud cases that reuse statistical properties of your real data, amount distributions, merchant categories, time-of-day patterns, without reproducing exact records. This way, you train on realistic examples while keeping sensitive information in your own environment.
How to build effective synthetic fraud datasets
- ●Start with a baseline model and identify the false negatives.
- ●Use the model to flag borderline transactions and label them as potential fraud.
- ●Generate synthetic variations that preserve statistical properties but change details like amounts, merchant IDs, or device fingerprints.
- ●Human-in-the-loop review catches unrealistic or low-quality examples.
- ●Iteratively retrain and compare metrics like AUPRC and detection recall.
Synthetic data doesn’t replace real fraud cases. It complements them, giving you labeled edge cases and balanced training sets that improve detection rates and reduce false positives.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This means Coasty can generate synthetic datasets that reflect how users and attackers actually behave. The service is custom and contact-led. You talk to the Coasty team about your use case, data constraints, and model goals, and they design a synthetic data solution that fits your environment. No fixed packages or public pricing, just a tailored approach to high-quality labeled data.
If you want to train more robust fraud or anomaly detection models, synthetic data helps. To explore how Coasty can build custom synthetic datasets for your system, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.