LEAP-HGN: A Brain-Inspired Amygdala-Parietal Detection Framework with LLM-Driven Evolutionary Rules for Anti-Money Laundering

Money laundering activities are increasingly evolving into a time-sensitive, dense micro-circulation pattern, posing severe challenges to existing graph neural network technologies in dealing with the disruption of continuity caused by discrete time slices, the signal drowning due to massive business noise, and the logical lag resulting from the dynamic evolution of strategies. To address these issues, we propose the LEAP-HGN (LLM-Evolved Amygdala-Parietal HyperGraph Network), a novel approach that simulates the human brain's threat processing mechanism within a unified brain-like cognitive framework. This model first constructs a spatio-temporal multi-view money laundering hypergraph, explicitly restoring the disrupted temporal causal chains of funds. On this basis, we redesign a biomimetic amygdala-parietal synergy mechanism: the amygdala low pathway uses neural gain modulation technology to transform expert rules into explicit warning factors, forcibly amplifying the response of sparse high-risk nodes before deep feature propagation, thereby preventing signal dilution from the source; the parietal high pathway performs deep spatial reasoning through Markov high-order diffusion and residual attention methods, penetrating the dense noise background from a global perspective and reconstructing hidden long-distance multi-hop links. Additionally, we design a Large Language Model (LLM) self-evolutionary closed loop based on hard sample mining, an LLM-driven loop that parses hard samples into symbolic rules for autonomous re-injection, achieving continuous self-evolution without human intervention. Extensive experiments on six money laundering datasets demonstrate that LEAP-HGN significantly outperforms 12 SOTA baselines. Code and datasets will be made publicly available at https://anonymous.4open.science/r/LEAP-9AEF upon acceptance.