Knowledge-Integrated Representation Learning for Crypto Anomaly Detection under Extreme Label Scarcity; Relational Domain-Logic Integration with Retrieval-Grounded Context and Path-Level Explanations

TL;DR

This paper introduces RDLI, a novel framework that combines domain logic and contextual information to improve crypto anomaly detection under extreme label scarcity, enhancing accuracy and explainability.

Contribution

The paper presents RDLI, a new method integrating expert heuristics and retrieval-grounded context into GNNs for better anomaly detection and interpretability in crypto networks.

Findings

RDLI outperforms state-of-the-art GNNs by 28.9% in F1 score under 0.01% label scarcity.

Path level explanations from RDLI improve user trust and perceived usefulness.

Incorporating domain logic and context reduces false positives during market regime shifts.

Abstract

Detecting anomalous trajectories in decentralized crypto networks is fundamentally challenged by extreme label scarcity and the adaptive evasion strategies of illicit actors. While Graph Neural Networks (GNNs) effectively capture local structural patterns, they struggle to internalize multi hop, logic driven motifs such as fund dispersal and layering that characterize sophisticated money laundering, limiting their forensic accountability under regulations like the FATF Travel Rule. To address this limitation, we propose Relational Domain Logic Integration (RDLI), a framework that embeds expert derived heuristics as differentiable, logic aware latent signals within representation learning. Unlike static rule based approaches, RDLI enables the detection of complex transactional flows that evade standard message passing. To further account for market volatility, we incorporate a Retrieval Grounded Context (RGC) module that conditions anomaly scoring on regulatory and macroeconomic context, mitigating false positives caused by benign regime shifts. Under extreme label scarcity (0.01%), RDLI outperforms state of the art GNN baselines by 28.9% in F1 score. A micro expert user study further confirms that RDLI path level explanations significantly improve trustworthiness, perceived usefulness, and clarity compared to existing methods, highlighting the importance of integrating domain logic with contextual grounding for both accuracy and explainability.