Given　the　anonymity　and　complexity　of　illegal　transactions,　traditional　deep-learning　methods　struggle　to　establish　correlations　between　transaction　addresses,　cash　flows,　and　physical　users.　Additionally,　the　limited　number　of　labels　for　illegal　transactions　results　in　severe　class　imbalance　and　other　challenges.　To　overcome　these　limitations,　we　propose　a　reinforcement　learning-enhanced,　multi-relational,　attention　graph-aware　framework　to　detect　anti-money　laundering　and　illegal　trading　activities.　On　the　one　hand,　a　data-driven,　graph-aware　layer　establishes　long-term　dependencies　and　correlations　between　transaction　graph　nodes.　Similarity　among　graph　nodes　divides　the　topological　graph　into　three　subgraphs.　Learning　from　these　subgraphs　and　converging　nodes　enriches　local,　global,　and　contextual　details.　Simultaneously,　using　repeated　nodes　across　the　subgraphs　enhances　interactivity　between　them,　reduces　intra-class　ambiguity,　and　accentuates　inter-class　differences.　On　the　other　hand,　a　reinforcement　learning　module　embedded　in　the　graph-aware　layer　compensates　for　the　missing　details　in　node　features　caused　by　masking　operations.　Furthermore,　the　reconstructed　loss　function　addresses　significant　classification　inaccuracies　by　reducing　the　weight　assigned　to　easily　classified　samples.　Balancing　these　issues　and　individually　supervising　each　component　enables　the　detection　framework　to　achieve　optimal　performance.　The　evaluation　results　demonstrate　that　our　proposed　model　exhibits　optimal　detection　performance　and　robustness,　such　as　F1　of　93.85%　and　94.39%.　©　The　Author(s)　2024.