Federated Learning for AML Models

Collaborate on Financial Crime Detection — Without Sharing Data

Traditional AML models are institution-bound and limited by siloed datasets. But criminals don’t stop at borders — or banks. Nevora enables secure, privacy-first collaboration through federated learning: banks improve detection together while keeping data on-premise. It’s AML intelligence at scale — with zero data leakage.

Built for Cross-Bank Patterns

Federated learning allows institutions to detect laundering tactics that span multiple banks — like smurfing or nested structures — by training models collectively across institutions without centralizing data.

Privacy-Preserving by Design

Data never leaves your infrastructure. Only model parameters are shared, keeping PII, transaction records, and customer files fully protected and regulator-approved.

Performance Without Compromise

Our optimized neural architectures are tailored for imbalanced AML datasets and non-IID (non-identically distributed) data. The result: stronger signal detection, fewer false positives, and full explainability across institutions.

Case Study: Privacy-Preserving Financial Crime Detection Using Federated Learning

Challenge

Cross-bank money laundering tactics go undetected due to data silos. Institutions needed a way to collaborate on AML model development without exposing sensitive information.

Solution

Nevora developed a federated learning demonstrator that trains models across simulated banking environments. Techniques like FedProx and regularization tuning enabled learning across non-uniform data, without centralizing it.

Impact

Achieved an AUC of ~0.86 in four synthetic environments. Proved that secure AML model collaboration is feasible — a foundation for cross-institutional, regulator-accepted intelligence.