US 2026/0253135 A1 · REAL-TIME FRAUD DETECTION SYSTEM USING DENDRITIC MEMORY MODELING AND QUANTUM INTEGRATION

Abstract

A real-time fraud detection system is disclosed that models financial transactions as evolving memory graphs using biologically inspired dendritic architectures. The system employs graph neural networks with edge decay and reinforcement mechanisms to retain behavioral context over time, enabling dynamic anomaly detection. An attention-based recall module reactivates latent fraud patterns based on contextual similarity. Optionally, quantum walk engines and entangled memory states are integrated to accelerate pattern discovery in high-dimensional data. The invention supports few-shot meta-learning, real-time inference, and deployment on neuromorphic or quantum-classical platforms, offering adaptive, scalable, and interpretable fraud detection across dynamic financial ecosystems.

Claims20

Dependent claims

A real-time fraud detection system comprising:

a transaction ingestion module configured to receive and preprocess streaming financial transaction data;
a memory model comprising a graph neural network configured to represent each transaction as a node within a dynamically evolving memory graph, wherein edges represent behavioral and temporal similarity;
an edge modulation mechanism that applies biologically inspired decay and reinforcement functions to edge weights, simulating dendritic plasticity;
an attention-based recall module configured to reactivate previously learned memory patterns based on contextual similarity to incoming transactions;
and
an anomaly scoring module configured to assign a fraud probability score to each transaction based on deviation from learned memory paths.

3 more elements

Dependencies are inferred from the claim text.

Real-time fraud detection system using dendritic memory modeling and quantum integration

Life of this patent

Real-time fraud detection system using dendritic memory modeling and quantum integration

Life of this patent

Abstract

Claims20