Our Federated solution
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Federated Synthetic Data
Build detection before enough cases exist: Federated Synthetic Data for stronger AML models
Consilient’s Federated Synthetic Data turns regulatory intelligence, known cases, and financial-crime expertise into realistic, model-ready behavior. It gives models credible examples of rare, emerging, and under-represented risks where an institution’s own labeled history is scarce or absent.
Those models can then learn from the institution’s own customer and transaction data, adapting the initial synthetic learning to local behavior. Through federation, permitted model learning can be combined across participating institutions while the underlying customer data stays local, broadening the range of patterns the model can learn from.
Key features
Start with 24 validated typologies across five risk families and roughly 110M labeled transactions. Each package contains realistic suspicious, legitimate, and ambiguous behavior designed for model development, augmentation, and evaluation.
Create model capability for new and emerging risks before enough confirmed internal cases exist. Consilient converts regulatory intelligence, cases, and expert knowledge into model-ready scenarios, giving teams a credible starting point where historical labels are limited or absent.
Synthetic-trained models can then learn locally from your own customer and transaction data. This adapts the starting model to the products, customers, behaviors, and risk profile actually present within your institution.
Federated learning combines permitted model updates across participating institutions while the underlying customer and transaction data stays local. This broadens the range of patterns models can learn from beyond the experience available inside any one institution.
Use synthetic data to build new models, strengthen sparse historical datasets, and run controlled tests against known suspicious, legitimate, and ambiguous scenarios. Existing rules, models, and technology vendors can be challenged against behaviors that may be too rare to test reliably with historical data alone.
Every package is tested for behavioral plausibility, distributional overlap, leakage controls, realistic class ratios, and model non-triviality. The objective is not synthetic data that looks easy to model, but data that creates a credible and demanding representation of real financial-crime behavior.
Key benefits
Build higher-performing models
Improve model performance by giving detection models more varied, realistic examples of suspicious, legitimate, and ambiguous behavior to learn from. Synthetic data can supplement sparse historical labels and introduce behavioral patterns that may not exist in an institution's own data, strengthening the model's ability to distinguish meaningful risk.
Detect priority regulatory risks
Build coverage for rare, emerging, and high-priority financial-crime risks before enough confirmed internal cases have accumulated. Consilient converts regulatory intelligence, known cases, and specialist expertise into model-ready behavior, helping institutions respond more quickly to risks such as sanctions evasion, corruption, trade-based laundering, and organized laundering networks.
Make machine learning possible
Enable institutions with insufficient historical data to develop and use machine-learning detection models. Synthetic data provides the labeled examples needed to establish an initial model, which can then learn from the institution's own data and, through federation, benefit from wider institutional learning without pooling customer data.
Build higher-performing models
Improve model performance by giving detection models more varied, realistic examples of suspicious, legitimate, and ambiguous behavior to learn from. Synthetic data can supplement sparse historical labels and introduce behavioral patterns that may not exist in an institution's own data, strengthening the model's ability to distinguish meaningful risk.
Detect priority regulatory risks
Build coverage for rare, emerging, and high-priority financial-crime risks before enough confirmed internal cases have accumulated. Consilient converts regulatory intelligence, known cases, and specialist expertise into model-ready behavior, helping institutions respond more quickly to risks such as sanctions evasion, corruption, trade-based laundering, and organized laundering networks.
Make machine learning possible
Enable institutions with insufficient historical data to develop and use machine-learning detection models. Synthetic data provides the labeled examples needed to establish an initial model, which can then learn from the institution's own data and, through federation, benefit from wider institutional learning without pooling customer data.
Synthetic data creates the examples. Federation broadens the learning.
No institution sees enough examples of every financial-crime risk it needs to detect. For rare and emerging behaviors, labeled outcomes can take years to accumulate, while the examples that do exist are fragmented across the market. Consilient turns regulatory intelligence, known cases, and expert knowledge into realistic, model-ready financial behavior, giving institutions a credible starting point before sufficient internal cases exist.
Once deployed, models can learn from the institution’s own customer and transaction data, adapting synthetic learning to local behavior. Federation then combines permitted model learning across participating institutions without moving the underlying customer data. The result is a system that starts with synthetic coverage, learns from reality, and broadens through the network, helping detection reflect a wider range of behaviors than any one institution can build from its own history alone.