Systems and methods for privacy-preserving multi-agent financial crime detection with cross-jurisdictional compliance management

A computer-implemented method for performing privacy-preserving financial crime detection across a plurality of financial institutions, the method executed by one or more processors operating within at least one trusted execution environment that cryptographically isolates code and data during execution, the method comprising:

• receiving financial transaction data from multiple institutional data sources, wherein the financial transaction data includes sensitive attributes comprising at least one of account identifiers, customer identifiers, and personally identifiable information;
• preprocessing the received data using a data ingestion agent executing within the trusted execution environment that applies deterministic, reference-preserving tokenization to the sensitive attributes by replacing each sensitive attribute with a corresponding token according to a token-to-original-value mapping stored in a hardware security module that is inaccessible to a network analysis engine and to a federated learning engine of the method and anonymization;
• analyzing the preprocessed data using the network analysis engine operating exclusively on the tokenized data without access to the token-to-original-value mapping, that maps inter-entity relationships and identifies patterns indicative of financial crimes by extracting graph topology features from a graph-based representation of the tokenized data in which nodes represent entities and edges represent financial interactions;
• executing privacy-preserving computations across institutional boundaries using a federated learning engine and secure multi-party computation to generate updated detection models, wherein each participating financial institution locally trains a model on its own tokenized data and transmits only encrypted model updates, comprising one or more of encrypted gradients and encrypted weight adjustments, to an aggregation service such that no raw training data is transmitted across institutional boundaries;
• presenting analysis results through a privacy-enhanced graphical user interface that applies data masking, k-anonymity filtering, and visual differential privacy based on user access levels, wherein the visual differential privacy comprises injecting calibrated noise into network metrics displayed in the graphical user interface in accordance with a privacy budget that is dynamically adjusted based on an authorization level of a requesting user;
• and
• logging all data access, model interactions, and system outputs to a cryptographically signed, tamper-evident audit trail repository for regulatory review and cross-jurisdictional compliance, wherein each log entry includes a timestamp, an agent identifier, a hashed record identifier, an action performed, and a digital signature.