Consilient launched Federated Synthetic Data, a library of synthetic AML training data covering 24 validated typologies across five risk families and about 110 million labeled transactions. Institutions use it to train detection models for typologies they have few confirmed cases of, such as trade based laundering, corruption and sanctions evasion, then train locally and share only approved model learning through federation. In Consilient's test, models trained on synthetic data alone ranked a real bank's confirmed suspicious accounts 10 to 15 times better than random.
Our readBanks short on confirmed cases for a typology can run model based detection from day one instead of waiting years for labels, without moving customer data. The test bank is unnamed and the result comes from Consilient's own study, so validators should ask how the bank's customer mix compares with theirs and request the method behind the figure.