Power up machine learning for fraud detection and AML with synthetic data

Increase accuracy and operational efficiency using better-than-real synthetic datasets!


MOSTLY AI’s synthetic data have been shown to have a consistent improvement on the performance of fraud detecting machine learning models—between 2% and 15%—compared to using raw, imbalanced data. An improvement of only 2% could yield a 2% decrease in false positives, thus saving millions of dollars on investigation processes alone.


  • Traditional, rule-based fraud detection methods result in an unacceptably high rate of false positives.
  • Typically used data masking techniques endanger privacy and destroy fraudulent patterns.
  • Off-the-shelf ML models are trained on data that is not specific to the bank or institution and that do not reflect learnings from reality.
  • Risk nuances, such as high-risk cross-border transactions and unusual typologies of customer behavior are often missed.
  • Selecting, on-boarding, and customizing ML models is a long and costly process.
  • Datasets are inherently biased—the overrepresentation of specific patterns could lead to faulty model decisions.
  • Datasets are imbalanced—fraud events are rare and, as such, are underrepresented in raw datasets.
  • Fraud activities evolve quickly, and fraud detection algorithms need to be retrained periodically.
  • Non-compliant data management risks compliance fines and data breaches.


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.


  • MOSTLY AI’s unparalleled data accuracy allows machine learning algorithms to pick up on new fraudulent patterns hidden in the raw, imbalanced data.
  • Anonymization is automatic, and data privacy is a built-in feature; the synthetic copies bear no 1:1 relationship to the original data points. This is a mathematical guarantee that no individual of the original dataset can be reidentified in the synthetic version.
  • The resulting data no longer classifies as personal data, so you can share your auto-anonymized synthetic datasets with third-party AI vendors and internal fraud departments in a privacy-compliant and agile way.
  • MOSTLY AI’s synthetic data platform is easy to use and allows data scientists to quickly and efficiently augment
  • Each generated synthetic dataset comes with an automatically compiled privacy and accuracy report to make sure that your machine learning algorithm is fed with the best possible version of your training data.

Download the case study to find out how MOSTLY AI's synthetic data can improve the performance of your fraud detection models, reduce false positives and help models detect new types of fraud!

Want to learn more about how synthetic data can power up your fraud detection?