Your machine learning models are only as good as the data they were trained on. Testing, training, and calibrating your machine learning models with realistic, balanced artificial data that is representative of the fraudulent activities your institution encounters improve fraud detection and the AML models’ accuracy. Better signals—calibrated to fit the bank’s unique data profile—result in fewer false positives and more true positives. Synthesized datasets are scalable, easy to augment, and safe to handle, so you can calibrate and recalibrate your ML models as often as you like without exposing sensitive data. What’s more, synthetic data empowers you to draw on alternative data sources locked by privacy regulations or policies to further improve the accuracy of your fraud detection analytics.